31REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
MACHINE LEARNING ALGORITHMS FOR
PREDICTIVE MAINTENANCE: A SYSTEMATIC
LITERATURE MAPPING
1 Programa de Doctorado en Ingeniería Eléctrica, Escuela Politécnica Nacional (EPN), Quito, Ecuador.
2 Maestría en Electrónica y Automatización, Escuela Superior Politécnica de Chimborazo (ESPOCH), Riobamba, Ecuador.
RESUMENABSTRACT
Jorge Paredes Carrillo 1 jorge.paredes01@epn.edu.ec
Carlos Romero Barreno 2 carlos.romerob@espoch.edu.ec
Predictive maintenance is a practice that industrial
companies can apply to their processes thanks to
technologies such as artificial intelligence and the
Internet of Things. Machine Learning algorithms
are used in many fields to make predictions or
classifications. Predictive maintenance is an area
of research that provides new practices, strategies,
or methodologies. As a relatively new field, the
methodologies are still scattered and there is little
information on the maturity of the algorithms.
To provide a solid foundation, a systematic
literature review is presented to give engineers
and researchers an overview of machine learning
algorithms used in predictive maintenance. The
results obtained show some growth in recent years,
demonstrating the interest in this area of research.
However, most of the contributions in this field
can be summarized as concept proofs and it is still
difficult to obtain a prototype that can be validated
as a complete and certified system. This paper
describes the main machine learning algorithms
used in predictive maintenance according to their
type of use and supervision, analyses their input
and output parameters, and determines their
maturity.
Keywords: Machine Learning, Predictive
Maintenance, Systematic Literature Mapping,
PdM.
El mantenimiento predictivo es una práctica que
gracias a tecnologías como la inteligencia artificial
e internet de las cosas permiten que las empresas
industriales lo puedan aplicar en sus procesos. Los
algoritmos de Machine Learning son utilizados
en muchos campos y sirven para realizar tareas
de predicción o clasificación. El mantenimiento
predictivo es un campo de Investigación que aporta
con nuevas prácticas, estrategias o metodologías. Al
ser un campo relativamente nuevo las metodologías
aún se encuentran dispersas y existe poca información
sobre la madurez de los algoritmos. Para proporcionar
una base sólida, se presenta un mapeo sistemático
de literatura con el objetivo de ofrecer a ingenieros e
investigadores una visión general de los algoritmos
de Machine Learning usados en mantenimiento
predictivo. Los resultados obtenidos muestran un
crecimiento en los últimos años demostrando un
interés en este campo de investigación. Sin embargo,
la mayoría de las contribuciones en este campo
se pueden resumir como pruebas concepto y aún
resulta difícil obtener un prototipo para que sea
validado como un sistema completo y certificado. En
este artículo se describen los principales algoritmos
de Machine Learning usados en mantenimiento
predictivo de acuerdo al tipo de uso y su supervisión,
además, se analizan los parámetros de entrada y las
salidas de los mismos y por último se determina su
nivel de madurez.
Palabras Clave: Machine Learning,
Mantenimiento Predictivo, Mapeo Sistemático de
Literatura, PdM.
REVISTA PERSPECTIVAS
Algoritmos de Machine Learning usados en mantenimiento
predictivo: un mapeo sistemático de literatura
Fecha de Recepción: 06/03/2023. Fecha de Aceptación: 05/06/2024 Fecha de Publicación: 20/01/2025
VOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
DOI: https://doi.org/10.47187/perspectivas.7.1.227
32REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
The world is currently experiencing a new
industrial revolution called 'Industry 4.0', thanks
to advances in technologies such as artificial
intelligence, the Internet of Things (IoT) or big
data [1]. To ensure digital transformation, a new
approach is needed that combines physical and
digital systems. The integration of these two
systems will result in a large amount of data
from different parts of a factory, which must be
processed to extract information [2].
The use of IoT architectures generates a large
amount of data [3]. Much of this data includes
events and alarms that occur on the production
line of a factory. By processing and analyzing this
data, information about the production process can
be easily obtained. This is important for decision
making, maintenance tasks, fault detection, cost
reduction and improving operator safety [4].
The above benefits are closely related to internal
processes in the manufacturing industry. It is
necessary to implement strategies to identify
possible failures in critical machinery in order to
avoid unplanned shutdowns that affect production
[5]. For example, [6] proposes the monitoring of
an oil refinery's compressed gas system, using
Industrial Internet of Things architectures to
obtain data from specific machines and then using
machine learning algorithms to obtain predictions
of the machine's current state. A system to
perform predictive maintenance tasks is proposed
by [7], which is able to obtain a health index of
the machinery of an entire factory. A predictive
maintenance model uses neural network algorithms
to determine the remaining life of a machine and
supports maintenance scheduling in a factory [8].
A method can use quantitative and qualitative
analysis to apply machine learning techniques
to predict failures, thereby aiding maintenance
decision making and reducing the costs associated
with these tasks [9].
Several nomenclatures can be found in the literature
to describe maintenance strategies. However, in
this paper we consider the classification proposed
by [10]. They classify maintenance strategies as
shown below:
I. Introduction Corrective maintenance: this type of maintenance
is carried out to repair a machine after a fault has
occurred.
Preventive maintenance: this is carried out at
regular intervals according to a maintenance
schedule, even if the machine has not yet failed.
Predictive maintenance: this type of maintenance
attempts to predict a future failure before it occurs
in order to plan maintenance tasks and reduce
costs.
Fig. 1 gives an overview of the types of maintenance.
Although corrective maintenance is the simplest
strategy, it requires stopping production to correct
the fault, which increases maintenance costs.
Preventive maintenance is effective in preventing
breakdowns, but increases costs by performing
unnecessary maintenance when the machine is in
optimal condition. Predictive maintenance uses
data on specific machine quantities and a history
of failures. It can also use statistical approaches
and machine learning algorithms. Therefore,
predictive maintenance has several advantages,
such as maximizing machine uptime and reducing
maintenance tasks and associated costs [11].
Predictive maintenance uses machine learning, an
application of artificial intelligence, as its main
tool. This approach is the most optimal because
several machine learning algorithms have recently
emerged that are highly accurate and easy to
implement. In addition, machine learning is also
capable of handling large amounts of data and
extracting hidden relationships from dynamic
environments such as industrial environments
[12]. Therefore, machine learning can serve as
a powerful tool in predictive maintenance tasks,
although it is highly dependent on the algorithm
used. Therefore, the aim of this paper is to
present a systematic literature review that presents
the main machine learning algorithms used in
predictive maintenance. This paper provides a
useful background on the main machine learning
algorithms, as well as their main applications and
maturity levels, and will help future research work.
The paper is structured as follows: Section II
The world is currently experiencing a new
industrial revolution called 'Industry 4.0', thanks
to advances in technologies such as artificial
intelligence, the Internet of Things (IoT) or big
data [1]. To ensure digital transformation, a new
approach is needed that combines physical and
digital systems. The integration of these two
systems will result in a large amount of data
from different parts of a factory, which must be
processed to extract information [2].
The use of IoT architectures generates a large
amount of data [3]. Much of this data includes
events and alarms that occur on the production
line of a factory. By processing and analyzing this
data, information about the production process can
be easily obtained. This is important for decision
making, maintenance tasks, fault detection, cost
reduction and improving operator safety [4].
The above benefits are closely related to internal
processes in the manufacturing industry. It is
necessary to implement strategies to identify
possible failures in critical machinery in order to
avoid unplanned shutdowns that affect production
[5]. For example, [6] proposes the monitoring of
an oil refinery's compressed gas system, using
Industrial Internet of Things architectures to
obtain data from specific machines and then using
machine learning algorithms to obtain predictions
of the machine's current state. A system to
perform predictive maintenance tasks is proposed
by [7], which is able to obtain a health index of
the machinery of an entire factory. A predictive
maintenance model uses neural network algorithms
to determine the remaining life of a machine and
supports maintenance scheduling in a factory [8].
A method can use quantitative and qualitative
analysis to apply machine learning techniques
to predict failures, thereby aiding maintenance
decision making and reducing the costs associated
with these tasks [9].
Several nomenclatures can be found in the literature
to describe maintenance strategies. However, in
this paper we consider the classification proposed
by [10]. They classify maintenance strategies as
shown below:
I. Introduction Corrective maintenance: this type of maintenance
is carried out to repair a machine after a fault has
occurred.
Preventive maintenance: this is carried out at
regular intervals according to a maintenance
schedule, even if the machine has not yet failed.
Predictive maintenance: this type of maintenance
attempts to predict a future failure before it occurs
in order to plan maintenance tasks and reduce
costs.
Fig. 1 gives an overview of the types of maintenance.
Although corrective maintenance is the simplest
strategy, it requires stopping production to correct
the fault, which increases maintenance costs.
Preventive maintenance is effective in preventing
breakdowns, but increases costs by performing
unnecessary maintenance when the machine is in
optimal condition. Predictive maintenance uses
data on specific machine quantities and a history
of failures. It can also use statistical approaches
and machine learning algorithms. Therefore,
predictive maintenance has several advantages,
such as maximizing machine uptime and reducing
maintenance tasks and associated costs [11].
Predictive maintenance uses machine learning, an
application of artificial intelligence, as its main
tool. This approach is the most optimal because
several machine learning algorithms have recently
emerged that are highly accurate and easy to
implement. In addition, machine learning is also
capable of handling large amounts of data and
extracting hidden relationships from dynamic
environments such as industrial environments
[12]. Therefore, machine learning can serve as
a powerful tool in predictive maintenance tasks,
although it is highly dependent on the algorithm
used. Therefore, the aim of this paper is to
present a systematic literature review that presents
the main machine learning algorithms used in
predictive maintenance. This paper provides a
useful background on the main machine learning
algorithms, as well as their main applications and
maturity levels, and will help future research work.
The paper is structured as follows: Section II
33REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
II. Background
presents a background with several concepts of
the different machine leaning algorithms Section
III presents the planning and execution of the
systematic literature mapping. Section IV presents
a description of the main machine learning
algorithms used, while section V presents the
types of input data and outputs produced by the
algorithms, as well as their maturity. Section VI
presents a related work and finally, section VII
presents the contributions and conclusions of this
paper.
It is important to have some concepts, so here is
a brief description of some that were considered
important after the systematic mapping:
• Machine learning: is a branch of artificial
intelligence that allows machines to learn
without being programmed. This allows
machines to make predictions, classify or
identify patterns [13].
• Supervised algorithms: they base their learning
on a set of previously labelled data, so that the
value of their target attribute is known [14].
• Unsupervised algorithms: they base their
learning on an unlabeled data set, or no target
value or class is known. It is used for clustering
tasks [14].
Regression: aims to predict a numerical result
Fig. 1: Overview of types of maintenance
[15].
• Classification: aims to predict a categorical
outcome [15].
• Linear regression: is a supervised machine
learning algorithm. It is a data analysis
technique that predicts the value of unknown
data using another related and known data
value [16].
• Decision tree: is a supervised, non-parametric
machine learning algorithm. It has a
hierarchical tree structure consisting of a root
node, leaf nodes and internal nodes. It can be
used for regression or classification tasks [17].
• Random forest: is a popular machine learning
algorithm used for classification or regression.
It is a set of decision trees [18].
• Support Vector Machines: is a supervised
machine learning algorithm that allows
finding the optimal way to classify among
several classes. It can be used for both
regression and classification. It is based on the
principle of separating two classes by means
of a hyperplane called a support vector [19].
• Neural Networks: is a type of supervised
machine learning algorithm that aims to
simulate the behavior of the human brain. It
can be defined as a network of interconnected
nodes. They can be used to perform
classification or regression [20].
• K-means: is an unsupervised machine learning
algorithm that attempts to form clusters of
data with similar characteristics [21].
• K-nearest neighbor: is a supervised non-
parametric machine learning algorithm. It is
based on the distance from one data to another
and classifies objects based on the classes
of the nearest neighbors. This algorithm is
designed to perform classifications, although
it can also be applied to regressions [22].
• Long short-term memory: This is a type of
recurrent neural network. The output of the last
stage feeds the current stage. It is specifically
designed to handle sequential data. It is used
for classification or regression [23].
• Autoencoder: is a machine learning technique
that attempts to reconstruct the input data
from the output to eliminate errors or outliers
[24].
II. Background
presents a background with several concepts of
the different machine leaning algorithms Section
III presents the planning and execution of the
systematic literature mapping. Section IV presents
a description of the main machine learning
algorithms used, while section V presents the
types of input data and outputs produced by the
algorithms, as well as their maturity. Section VI
presents a related work and finally, section VII
presents the contributions and conclusions of this
paper.
It is important to have some concepts, so here is
a brief description of some that were considered
important after the systematic mapping:
• Machine learning: is a branch of artificial
intelligence that allows machines to learn
without being programmed. This allows
machines to make predictions, classify or
identify patterns [13].
• Supervised algorithms: they base their learning
on a set of previously labelled data, so that the
value of their target attribute is known [14].
• Unsupervised algorithms: they base their
learning on an unlabeled data set, or no target
value or class is known. It is used for clustering
tasks [14].
Regression: aims to predict a numerical result
Fig. 1: Overview of types of maintenance
[15].
• Classification: aims to predict a categorical
outcome [15].
• Linear regression: is a supervised machine
learning algorithm. It is a data analysis
technique that predicts the value of unknown
data using another related and known data
value [16].
• Decision tree: is a supervised, non-parametric
machine learning algorithm. It has a
hierarchical tree structure consisting of a root
node, leaf nodes and internal nodes. It can be
used for regression or classification tasks [17].
• Random forest: is a popular machine learning
algorithm used for classification or regression.
It is a set of decision trees [18].
• Support Vector Machines: is a supervised
machine learning algorithm that allows
finding the optimal way to classify among
several classes. It can be used for both
regression and classification. It is based on the
principle of separating two classes by means
of a hyperplane called a support vector [19].
• Neural Networks: is a type of supervised
machine learning algorithm that aims to
simulate the behavior of the human brain. It
can be defined as a network of interconnected
nodes. They can be used to perform
classification or regression [20].
• K-means: is an unsupervised machine learning
algorithm that attempts to form clusters of
data with similar characteristics [21].
• K-nearest neighbor: is a supervised non-
parametric machine learning algorithm. It is
based on the distance from one data to another
and classifies objects based on the classes
of the nearest neighbors. This algorithm is
designed to perform classifications, although
it can also be applied to regressions [22].
• Long short-term memory: This is a type of
recurrent neural network. The output of the last
stage feeds the current stage. It is specifically
designed to handle sequential data. It is used
for classification or regression [23].
• Autoencoder: is a machine learning technique
that attempts to reconstruct the input data
from the output to eliminate errors or outliers
[24].
34REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
III. Related works
IV. Systematic Literature Mapping
Machine learning algorithms used in predictive
maintenance is an area that is currently being
researched and exploited. Some literature
review works have already emerged from this
field. Carvalho et al, focuses on describing four
important algorithms such as random forest, neural
networks, support vector machine and k-means, in
addition, it also mentions the type of equipment
where these algorithms can be used, the year in
which this research is conducted is 2019 [25].
Machine learning algorithms can be used to
perform regression or classification. Classification
is an important task and supervised or
unsupervised algorithms can be used. Saranavan et
al, provide a literature review where they focus on
those supervised machine learning algorithms to
perform classifications, revealing methodologies,
advantages and disadvantages [26].
Another approach is to compare machine learning
algorithms used in predictive maintenance,
Silvestrin et al, provides a comparison of
convolutional neural networks with time series,
finding significant differences in the use of
convolutional neural networks [27]. Industry 4.0
is the new industrial revolution that the world is
currently experiencing, which is why Serradilla
et al, in their literature review, provide models
of machine learning architectures that can be
used in predictive maintenance tasks to ensure
reproducibility and replicability in different
environments. Following the Industry 4.0 line,
Drakaki et al. provide an insight into the main
machine learning algorithms used in predictive
maintenance of induction motors, focusing on
machine learning architectures and techniques
[28].
Focusing on more specific applications, there
are several works, the most notable of which is
that of Olesen et al. It identifies new trends and
challenges that can be solved by using predictive
maintenance and machine learning in pumping
systems and thermal power plants [29].
Of the works described above, none focuses on
classifying the algorithms or analyzing the types
of input data required by the machine learning
algorithm to be used and the output produced
by the algorithm. Similarly, no work focuses on
providing a maturity level for machine learning
algorithms used in predictive maintenance.
A systematic literature mapping SLM can
provide an overview of the area of interest.
This method identifies, appraises and interprets
information relevant to a particular area, problem
or phenomenon of interest [30]. A systematic
literature review is a secondary study that aims to
critically evaluate research with a similar scope.
The methodology proposed by [31] is used to
carry out SLR.
A. Scope of the Study
The main objective of this study is to provide an
overview of the state of the art of machine learning
based data analysis algorithms used in predictive
maintenance. To successfully achieve this goal, the
following research questions have been proposed:
• RQ 1. What types of machine learning
algorithms are used in predictive maintenance?
• RQ1.1 What are the algorithms?
A classification of all the algorithms
found will help the reader to have a
better understanding to find similarities
or differences that will help to improve
predictive maintenance.
• RQ 2. What input data does the machine
learning algorithm use?
Identifying the input and output parameters is
important as it will help to better understand
how the algorithms used in machine learning
work.
• RQ 2.1 What types of data does the machine
learning algorithm use?
Knowing whether the data used in machine
learning is synthetic or real data is important
for predictive maintenance applications.
• RQ 2.2 What input parameters are required
for this type of data?
III. Related works
IV. Systematic Literature Mapping
Machine learning algorithms used in predictive
maintenance is an area that is currently being
researched and exploited. Some literature
review works have already emerged from this
field. Carvalho et al, focuses on describing four
important algorithms such as random forest, neural
networks, support vector machine and k-means, in
addition, it also mentions the type of equipment
where these algorithms can be used, the year in
which this research is conducted is 2019 [25].
Machine learning algorithms can be used to
perform regression or classification. Classification
is an important task and supervised or
unsupervised algorithms can be used. Saranavan et
al, provide a literature review where they focus on
those supervised machine learning algorithms to
perform classifications, revealing methodologies,
advantages and disadvantages [26].
Another approach is to compare machine learning
algorithms used in predictive maintenance,
Silvestrin et al, provides a comparison of
convolutional neural networks with time series,
finding significant differences in the use of
convolutional neural networks [27]. Industry 4.0
is the new industrial revolution that the world is
currently experiencing, which is why Serradilla
et al, in their literature review, provide models
of machine learning architectures that can be
used in predictive maintenance tasks to ensure
reproducibility and replicability in different
environments. Following the Industry 4.0 line,
Drakaki et al. provide an insight into the main
machine learning algorithms used in predictive
maintenance of induction motors, focusing on
machine learning architectures and techniques
[28].
Focusing on more specific applications, there
are several works, the most notable of which is
that of Olesen et al. It identifies new trends and
challenges that can be solved by using predictive
maintenance and machine learning in pumping
systems and thermal power plants [29].
Of the works described above, none focuses on
classifying the algorithms or analyzing the types
of input data required by the machine learning
algorithm to be used and the output produced
by the algorithm. Similarly, no work focuses on
providing a maturity level for machine learning
algorithms used in predictive maintenance.
A systematic literature mapping SLM can
provide an overview of the area of interest.
This method identifies, appraises and interprets
information relevant to a particular area, problem
or phenomenon of interest [30]. A systematic
literature review is a secondary study that aims to
critically evaluate research with a similar scope.
The methodology proposed by [31] is used to
carry out SLR.
A. Scope of the Study
The main objective of this study is to provide an
overview of the state of the art of machine learning
based data analysis algorithms used in predictive
maintenance. To successfully achieve this goal, the
following research questions have been proposed:
• RQ 1. What types of machine learning
algorithms are used in predictive maintenance?
• RQ1.1 What are the algorithms?
A classification of all the algorithms
found will help the reader to have a
better understanding to find similarities
or differences that will help to improve
predictive maintenance.
• RQ 2. What input data does the machine
learning algorithm use?
Identifying the input and output parameters is
important as it will help to better understand
how the algorithms used in machine learning
work.
• RQ 2.1 What types of data does the machine
learning algorithm use?
Knowing whether the data used in machine
learning is synthetic or real data is important
for predictive maintenance applications.
• RQ 2.2 What input parameters are required
for this type of data?
35REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
• RQ 3. What is the output of the algorithm?
It is important to know the type of output the
algorithm produces to use it for predictive
maintenance tasks.
• RQ 4. What is the maturity of the algorithms
used?
It is important to know the maturity level of the
algorithms in order to know which ones are most
commonly used in predictive maintenance tasks.
B. Study Identification
A database-driven search approach was used
in this study, with Scopus as the main search
database:
1) Search String
The choice of keywords to construct the search
string was based on common terms used in
the literature and terms related to this work
(for example, PdM or ML). Some of the terms
suggested by [32] were used to find synonyms.
Table 1 shows the search string used.
The search string was validated by an expert
in the field. The expert provided 10 relevant
items, and the search string found 9 of them,
that is the string contains 90% of the items
provided by the expert.
A search was performed on 5 October 2023
and 6019 items were found.
2) Inclusion and Exclusion Procedure
The inclusion and exclusion process consists of
two phases: an automatic phase and a manual
phase. The automatic phase uses the Scopus
functions, which values are listed in Table II,
while the manual phase uses the CADIMA
software [33]. A flowchart of the inclusion and
exclusion procedure is shown in Fig. 2.
TABLE I
Search String USed
((((predictive AND (maintenance or monitoring)) or PdM))
AND (“machine learning” or ML) AND (algorithm OR model
OR strategy OR technique))
The manual phase was carried out on 2349
articles after removing duplicate articles
with a CADIMA proprietary function using
the inclusion and exclusion criteria in Table
III. Before starting the manual phase, a pilot
phase was carried out between the principal
investigator and the expert with a set of 10
randomly selected articles to standardize the
inclusion and exclusion criteria. The title and
abstract of each article were read and marked
as included or excluded. To ensure inter-rater
reliability, the Krippendorff alpha coefficient
was set at 0.8, which is an accepted value in
most studies [34]. At the end of the pilot study,
a Krippendorff alpha coefficient of 1 was
obtained.
The manual inclusion and exclusion process
consisted of 3 iterations carried out by the principal
investigator. In the first iteration, the title was
read and 502 articles were included for the next
iteration. In the second iteration, the abstract was
read, including 116 articles. In the third iteration,
TABLE II
Inclusion Criteria Used in Scopus
Filter Values
Research Field Engineering
Computer Science
Type of document Conference article
Journal article
Language English
Fig. 2: Inclusion and exclusion procedure
• RQ 3. What is the output of the algorithm?
It is important to know the type of output the
algorithm produces to use it for predictive
maintenance tasks.
• RQ 4. What is the maturity of the algorithms
used?
It is important to know the maturity level of the
algorithms in order to know which ones are most
commonly used in predictive maintenance tasks.
B. Study Identification
A database-driven search approach was used
in this study, with Scopus as the main search
database:
1) Search String
The choice of keywords to construct the search
string was based on common terms used in
the literature and terms related to this work
(for example, PdM or ML). Some of the terms
suggested by [32] were used to find synonyms.
Table 1 shows the search string used.
The search string was validated by an expert
in the field. The expert provided 10 relevant
items, and the search string found 9 of them,
that is the string contains 90% of the items
provided by the expert.
A search was performed on 5 October 2023
and 6019 items were found.
2) Inclusion and Exclusion Procedure
The inclusion and exclusion process consists of
two phases: an automatic phase and a manual
phase. The automatic phase uses the Scopus
functions, which values are listed in Table II,
while the manual phase uses the CADIMA
software [33]. A flowchart of the inclusion and
exclusion procedure is shown in Fig. 2.
TABLE I
Search String USed
((((predictive AND (maintenance or monitoring)) or PdM))
AND (“machine learning” or ML) AND (algorithm OR model
OR strategy OR technique))
The manual phase was carried out on 2349
articles after removing duplicate articles
with a CADIMA proprietary function using
the inclusion and exclusion criteria in Table
III. Before starting the manual phase, a pilot
phase was carried out between the principal
investigator and the expert with a set of 10
randomly selected articles to standardize the
inclusion and exclusion criteria. The title and
abstract of each article were read and marked
as included or excluded. To ensure inter-rater
reliability, the Krippendorff alpha coefficient
was set at 0.8, which is an accepted value in
most studies [34]. At the end of the pilot study,
a Krippendorff alpha coefficient of 1 was
obtained.
The manual inclusion and exclusion process
consisted of 3 iterations carried out by the principal
investigator. In the first iteration, the title was
read and 502 articles were included for the next
iteration. In the second iteration, the abstract was
read, including 116 articles. In the third iteration,
TABLE II
Inclusion Criteria Used in Scopus
Filter Values
Research Field Engineering
Computer Science
Type of document Conference article
Journal article
Language English
Fig. 2: Inclusion and exclusion procedure
36REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
the conclusions were read, including 77 articles.
For the coding and information extraction phase
we have a set of 77 articles. Fig. 3 shows the
percentage of articles included in each iteration.
The articles included are presented in Appendix
A.
Four labels were used to classify articles in the
manual inclusion and exclusion phase. These
labels are:
Included: the article meets all the inclusion criteria
and none of the exclusion criteria.
Excluded: the article meets the exclusion criteria
or none of the inclusion criteria.
Unclear: the investigator is in doubt as to whether
the article should be included or excluded.
Secondary: the article is a secondary or tertiary
contribution.
Fig. 5 shows the number of articles published
between 2014 and 2023 using the exclusion
and inclusion criteria presented in this paper.
This confirms that predictive maintenance is
a technique that has been used in papers since
2014. On the other hand, it can be observed that
the interest in this field of research has increased
in recent years, reaching a peak between 2021
and 2023. This effect is related to the amount of
data generated by industrial equipment and the
latest advances in machine learning algorithms.
The small number of works in the field
of predictive maintenance is due to the
complexity of implementing efficient strategies
in production environments [39]. On the
other hand, the number of machine learning
algorithms is limited because data science is
still a relatively new field of study and there
are still no defined methodologies for obtaining
historical maintenance and failure data in
industrial environments.
A. RQ. 1. What types of machine learning
algorithms are used in predictive maintenance?
The articles reviewed fall into two main
categories: use and type of supervision. Most
articles report the use of supervised machine
learning algorithms and for use in regression
(data prediction). This is because the datasets
used are labelled and categorized, which makes
it easier to make a prediction or classification.
On the other hand, there is little work using
unsupervised machine learning algorithms,
as they only aim to find patterns of possible
failures for future use in maintenance task
planning. Unsupervised algorithms are more
prone to failure when making predictions or
classifications. Of the selected papers, those
using unsupervised algorithms are only used in
regression tasks.
Fig. 6 shows the proportion of papers using
supervised and unsupervised algorithms and
whether they are also used for regression or
classification.
TABLE III
Inclusion and Exclusion Criteria
Criteria Type Values
Inclusion (all must
be met)
The article must be related to predictive
maintenance.
The article must be related to data analy-
sis based on machine learning.
The article should contain information
about the machine learning algorithm
used.
The article must contain information
about the input and output parameters
as well as the data used in the machine
learning model used.
The article must be a primary contri-
bution.
Exclusion (none can
be fulfilled)
The article is a secondary or tertiary
contribution.
Fig. 3: Articles included in each iteration
V. Results
the conclusions were read, including 77 articles.
For the coding and information extraction phase
we have a set of 77 articles. Fig. 3 shows the
percentage of articles included in each iteration.
The articles included are presented in Appendix
A.
Four labels were used to classify articles in the
manual inclusion and exclusion phase. These
labels are:
Included: the article meets all the inclusion criteria
and none of the exclusion criteria.
Excluded: the article meets the exclusion criteria
or none of the inclusion criteria.
Unclear: the investigator is in doubt as to whether
the article should be included or excluded.
Secondary: the article is a secondary or tertiary
contribution.
Fig. 5 shows the number of articles published
between 2014 and 2023 using the exclusion
and inclusion criteria presented in this paper.
This confirms that predictive maintenance is
a technique that has been used in papers since
2014. On the other hand, it can be observed that
the interest in this field of research has increased
in recent years, reaching a peak between 2021
and 2023. This effect is related to the amount of
data generated by industrial equipment and the
latest advances in machine learning algorithms.
The small number of works in the field
of predictive maintenance is due to the
complexity of implementing efficient strategies
in production environments [39]. On the
other hand, the number of machine learning
algorithms is limited because data science is
still a relatively new field of study and there
are still no defined methodologies for obtaining
historical maintenance and failure data in
industrial environments.
A. RQ. 1. What types of machine learning
algorithms are used in predictive maintenance?
The articles reviewed fall into two main
categories: use and type of supervision. Most
articles report the use of supervised machine
learning algorithms and for use in regression
(data prediction). This is because the datasets
used are labelled and categorized, which makes
it easier to make a prediction or classification.
On the other hand, there is little work using
unsupervised machine learning algorithms,
as they only aim to find patterns of possible
failures for future use in maintenance task
planning. Unsupervised algorithms are more
prone to failure when making predictions or
classifications. Of the selected papers, those
using unsupervised algorithms are only used in
regression tasks.
Fig. 6 shows the proportion of papers using
supervised and unsupervised algorithms and
whether they are also used for regression or
classification.
TABLE III
Inclusion and Exclusion Criteria
Criteria Type Values
Inclusion (all must
be met)
The article must be related to predictive
maintenance.
The article must be related to data analy-
sis based on machine learning.
The article should contain information
about the machine learning algorithm
used.
The article must contain information
about the input and output parameters
as well as the data used in the machine
learning model used.
The article must be a primary contri-
bution.
Exclusion (none can
be fulfilled)
The article is a secondary or tertiary
contribution.
Fig. 3: Articles included in each iteration
V. Results
37REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
In more than 50% of the selected papers, machine
learning algorithms are used for regression,
since one of the main objectives of predictive
maintenance is to estimate the remaining useful
life (RUL). On the other hand, the algorithms
using classification try to provide a state of health
of the machine or equipment by classifying it in
categories proposed by each author.
Table IV summarizes the types of algorithms
according to their use and type of supervision in
the articles studied.
RQ. 1.1. What are the algorithms?
The papers consulted use a range of machine
learning algorithms that can be applied to
predictive maintenance tasks. These algorithms
range from those with a relatively simple
mathematical basis, such as linear regression,
to the more mathematically complex variants
of artificial neural networks.
The authors do not use a single algorithm in
their work but use several to test the accuracy
and error results of their main contribution.
The most used algorithms for supervised
models are:
• Linear regression
• Decision tree
• Random forests
• Support vector machines
• Neural Networks
• K-Nearest Neighbour
• Gradient Boost
• XGboost
• Adaboost
• Long term memory
• Autoencoder
The choice of these algorithms depends very much
on the practical application and the data obtained.
For example, if you have a dataset with a lot of
outliers, a robust algorithm to use is Decision
Trees, while a vulnerable one is Adaboost.
On the other hand, the most used algorithms in
unsupervised models are:
• K-means
• Neural Networks
Fig. 4: Classification Scheme used
Fig. 5: Classification Scheme used
Fig. 6: Papers per type and use and supervision
In more than 50% of the selected papers, machine
learning algorithms are used for regression,
since one of the main objectives of predictive
maintenance is to estimate the remaining useful
life (RUL). On the other hand, the algorithms
using classification try to provide a state of health
of the machine or equipment by classifying it in
categories proposed by each author.
Table IV summarizes the types of algorithms
according to their use and type of supervision in
the articles studied.
RQ. 1.1. What are the algorithms?
The papers consulted use a range of machine
learning algorithms that can be applied to
predictive maintenance tasks. These algorithms
range from those with a relatively simple
mathematical basis, such as linear regression,
to the more mathematically complex variants
of artificial neural networks.
The authors do not use a single algorithm in
their work but use several to test the accuracy
and error results of their main contribution.
The most used algorithms for supervised
models are:
• Linear regression
• Decision tree
• Random forests
• Support vector machines
• Neural Networks
• K-Nearest Neighbour
• Gradient Boost
• XGboost
• Adaboost
• Long term memory
• Autoencoder
The choice of these algorithms depends very much
on the practical application and the data obtained.
For example, if you have a dataset with a lot of
outliers, a robust algorithm to use is Decision
Trees, while a vulnerable one is Adaboost.
On the other hand, the most used algorithms in
unsupervised models are:
• K-means
• Neural Networks
Fig. 4: Classification Scheme used
Fig. 5: Classification Scheme used
Fig. 6: Papers per type and use and supervision
38REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
• Principal Component Analysis
In general, the k-means algorithm is the most used
in unsupervised models because it can group the
TABLE IV
Types of algorithms proposed on the studied papers
TABLE V
Algorithms proposed on the studied papers
Algorithm Type Papers studied
According to type of
supervision
Supervised
ID1, ID5, ID12, ID30, ID32, ID34, ID41, ID42, ID61, ID70, ID83, ID87, ID93,
ID100, ID103, ID109, ID127, ID137, ID140, ID144, ID148, ID153, ID156, ID158,
ID165, ID169, ID172, ID173, ID188, ID191, ID197, ID206, ID208, ID219, ID232,
ID240, ID245, ID249, ID250, ID254, ID272, ID283, ID291, ID294, ID310, ID325,
ID334, ID343, ID356, ID357, ID375, ID378, ID396, ID406, ID410, ID417, ID425,
ID429, ID431, ID435, ID444, ID448, ID454, ID460, ID463, ID471, ID477, ID483,
ID489, ID494, ID499, ID501, ID502
Unsupervised ID16, ID20, ID157, ID215,
According to use
Regression
ID1, ID16, ID20, ID30, ID32, ID34, ID42, ID83, ID87, ID100, ID109, ID127,
ID137, ID140, ID153, ID156, ID157, ID158, ID165, ID169, ID173, ID215, ID219,
ID245, ID250, ID254, ID272, ID294, ID310, ID334, ID356, ID406, ID410, ID417,
ID425, ID429, ID431, ID435, ID444, ID448, ID502
Clasification
ID5, ID12, ID41, ID61, ID70, ID93, ID103, ID144, ID148, ID172, ID188, ID191,
ID197, ID206, ID208, ID232, ID240, ID249, ID283, ID291, ID325, ID343, ID357,
ID375, ID378, ID396, ID454, ID460, ID463, ID471, ID477, ID483, ID489, ID494,
ID499, ID501
data into clusters to find possible relationships.
Fig. 7 shows the proportion of machine learning
algorithms used in predictive maintenance.
Table V summarizes the algorithms used on the
studied papers.
B. RQ 2. What input data does the machine
learning algorithm use?
To better understand the input data that a machine
learning algorithm uses, the research question
has been divided into two sub-questions. The
Fig. 7: Machine Learning algorithms
Algorithms Papers Studied
Linear regression ID12, ID100, ID431
Decision tree ID103, ID29, ID429
Random forests ID32, ID34, ID41, ID156, ID245
Support vector machines
ID70, ID83, ID127, ID144, ID165,
ID191, ID272, ID356, ID375,
ID396, ID460
Neural Networks
ID1, ID5, ID30, ID42, ID61, ID87,
ID93, ID109, ID140, ID148, ID158,
ID169, ID172, ID173, ID188,
ID206, ID209, ID232, ID249,
ID250, ID254, ID283, ID294,
ID325, ID334, ID343, ID357,
ID378, ID406, ID410, ID417,
ID435, ID444, ID448, ID454,
ID463, ID471, ID477, ID483,
ID489, ID494, ID499, ID501,
ID502
K-Nearest Neighbour ID240
Gradient Boost ID191
XGboost ID137
Adaboost ID156
Long term memory ID310
Autoencoder ID16, ID157, ID208, ID425
K-means ID16, ID20
Principal Component
Analysis ID215
• Principal Component Analysis
In general, the k-means algorithm is the most used
in unsupervised models because it can group the
TABLE IV
Types of algorithms proposed on the studied papers
TABLE V
Algorithms proposed on the studied papers
Algorithm Type Papers studied
According to type of
supervision
Supervised
ID1, ID5, ID12, ID30, ID32, ID34, ID41, ID42, ID61, ID70, ID83, ID87, ID93,
ID100, ID103, ID109, ID127, ID137, ID140, ID144, ID148, ID153, ID156, ID158,
ID165, ID169, ID172, ID173, ID188, ID191, ID197, ID206, ID208, ID219, ID232,
ID240, ID245, ID249, ID250, ID254, ID272, ID283, ID291, ID294, ID310, ID325,
ID334, ID343, ID356, ID357, ID375, ID378, ID396, ID406, ID410, ID417, ID425,
ID429, ID431, ID435, ID444, ID448, ID454, ID460, ID463, ID471, ID477, ID483,
ID489, ID494, ID499, ID501, ID502
Unsupervised ID16, ID20, ID157, ID215,
According to use
Regression
ID1, ID16, ID20, ID30, ID32, ID34, ID42, ID83, ID87, ID100, ID109, ID127,
ID137, ID140, ID153, ID156, ID157, ID158, ID165, ID169, ID173, ID215, ID219,
ID245, ID250, ID254, ID272, ID294, ID310, ID334, ID356, ID406, ID410, ID417,
ID425, ID429, ID431, ID435, ID444, ID448, ID502
Clasification
ID5, ID12, ID41, ID61, ID70, ID93, ID103, ID144, ID148, ID172, ID188, ID191,
ID197, ID206, ID208, ID232, ID240, ID249, ID283, ID291, ID325, ID343, ID357,
ID375, ID378, ID396, ID454, ID460, ID463, ID471, ID477, ID483, ID489, ID494,
ID499, ID501
data into clusters to find possible relationships.
Fig. 7 shows the proportion of machine learning
algorithms used in predictive maintenance.
Table V summarizes the algorithms used on the
studied papers.
B. RQ 2. What input data does the machine
learning algorithm use?
To better understand the input data that a machine
learning algorithm uses, the research question
has been divided into two sub-questions. The
Fig. 7: Machine Learning algorithms
Algorithms Papers Studied
Linear regression ID12, ID100, ID431
Decision tree ID103, ID29, ID429
Random forests ID32, ID34, ID41, ID156, ID245
Support vector machines
ID70, ID83, ID127, ID144, ID165,
ID191, ID272, ID356, ID375,
ID396, ID460
Neural Networks
ID1, ID5, ID30, ID42, ID61, ID87,
ID93, ID109, ID140, ID148, ID158,
ID169, ID172, ID173, ID188,
ID206, ID209, ID232, ID249,
ID250, ID254, ID283, ID294,
ID325, ID334, ID343, ID357,
ID378, ID406, ID410, ID417,
ID435, ID444, ID448, ID454,
ID463, ID471, ID477, ID483,
ID489, ID494, ID499, ID501,
ID502
K-Nearest Neighbour ID240
Gradient Boost ID191
XGboost ID137
Adaboost ID156
Long term memory ID310
Autoencoder ID16, ID157, ID208, ID425
K-means ID16, ID20
Principal Component
Analysis ID215
39REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
1) RQ 2.2. What input parameters are
required for this type of data?
The input parameters used in machine learning
algorithms are highly dependent on the
application and the machine being monitored.
For example, when monitoring an induction
motor, data such as current, voltage or power,
as well as vibration or temperature, are used to
estimate remaining life or classify a possible
failure. Another component susceptible to
failure and found in many machines is the
bearing, and data such as vibration, temperature
and radial load are usually obtained.
Some works also use historical failure data
as input parameters to avoid certain failures,
component degradation levels and failure
patterns. The choice of input parameters
depends very much on the components and
variables that can be measured and controlled
in the machinery to be monitored.
C. RQ 3. What is the output of the algorithm?
The main objective of predictive maintenance
is to anticipate an eventual failure, so the main
outputs of the machine learning algorithms are the
remaining useful life and a failure classification.
The prediction of the remaining useful life is
performed using regression algorithms. Depending
on the measured variables or input parameters
Fig. 8: Types of Data Used in Papers
first sub-question focuses on the types of data,
while the second sub-question focuses on the
input parameters required by machine learning
algorithms.
1) RQ 2.1. What types of data does the
machine learning algorithm use?
According to the classification scheme in
Figure 4, data can be divided into real and
synthetic data. Real data are those obtained
from the monitored machines that have not
been subjected to any outlier elimination or
filtering process. Synthetic data, on the other
hand, are those that have undergone various
processes to protect their information, as they
may belong to a machine of a critical process
in an industry.
Moreover 72% of the articles consulted use
real data obtained from different machines in
real or laboratory scenarios, although they are
also obtained from public repositories such
as the Bearing Data Center of Case Western
University Bearing, while the remaining
28% of the articles use synthetic data mainly
obtained from public repositories such as
NASA's CMAPSS and also from machines
present in industries that do not reveal their
name for reasons of confidentiality.
Table VI summarizes the types of data used in
studied papers.
Fig. 8 shows the proportion of data types used
in the selected articles.
TABLE VI
Types of data on the studied papers
Type of Data Papers studied
Real
ID1, ID5, ID16, ID20, ID30, ID34, ID42,
ID61, ID70, ID83, ID103, ID109, ID127,
ID140, ID144, ID148, ID153, ID156,
ID157, ID165, ID172, ID188, ID191,
ID197, ID206, ID208, ID215, ID232,
ID240, ID249, ID254, ID272, ID291,
ID294, ID310, ID325, ID334, ID343,
ID357, ID375, ID378, ID396, ID406,
ID410, ID417, ID425, ID429, ID431,
ID435, ID444, ID448, ID454, ID460
Syntethic
ID12, ID32, ID41, ID87, ID93, ID100,
ID137, ID158, ID169, ID173, ID219,
ID245, ID250, ID283, ID356, ID463,
ID471, ID477, ID483, ID489, ID494,
ID499, ID501, ID502
1) RQ 2.2. What input parameters are
required for this type of data?
The input parameters used in machine learning
algorithms are highly dependent on the
application and the machine being monitored.
For example, when monitoring an induction
motor, data such as current, voltage or power,
as well as vibration or temperature, are used to
estimate remaining life or classify a possible
failure. Another component susceptible to
failure and found in many machines is the
bearing, and data such as vibration, temperature
and radial load are usually obtained.
Some works also use historical failure data
as input parameters to avoid certain failures,
component degradation levels and failure
patterns. The choice of input parameters
depends very much on the components and
variables that can be measured and controlled
in the machinery to be monitored.
C. RQ 3. What is the output of the algorithm?
The main objective of predictive maintenance
is to anticipate an eventual failure, so the main
outputs of the machine learning algorithms are the
remaining useful life and a failure classification.
The prediction of the remaining useful life is
performed using regression algorithms. Depending
on the measured variables or input parameters
Fig. 8: Types of Data Used in Papers
first sub-question focuses on the types of data,
while the second sub-question focuses on the
input parameters required by machine learning
algorithms.
1) RQ 2.1. What types of data does the
machine learning algorithm use?
According to the classification scheme in
Figure 4, data can be divided into real and
synthetic data. Real data are those obtained
from the monitored machines that have not
been subjected to any outlier elimination or
filtering process. Synthetic data, on the other
hand, are those that have undergone various
processes to protect their information, as they
may belong to a machine of a critical process
in an industry.
Moreover 72% of the articles consulted use
real data obtained from different machines in
real or laboratory scenarios, although they are
also obtained from public repositories such
as the Bearing Data Center of Case Western
University Bearing, while the remaining
28% of the articles use synthetic data mainly
obtained from public repositories such as
NASA's CMAPSS and also from machines
present in industries that do not reveal their
name for reasons of confidentiality.
Table VI summarizes the types of data used in
studied papers.
Fig. 8 shows the proportion of data types used
in the selected articles.
TABLE VI
Types of data on the studied papers
Type of Data Papers studied
Real
ID1, ID5, ID16, ID20, ID30, ID34, ID42,
ID61, ID70, ID83, ID103, ID109, ID127,
ID140, ID144, ID148, ID153, ID156,
ID157, ID165, ID172, ID188, ID191,
ID197, ID206, ID208, ID215, ID232,
ID240, ID249, ID254, ID272, ID291,
ID294, ID310, ID325, ID334, ID343,
ID357, ID375, ID378, ID396, ID406,
ID410, ID417, ID425, ID429, ID431,
ID435, ID444, ID448, ID454, ID460
Syntethic
ID12, ID32, ID41, ID87, ID93, ID100,
ID137, ID158, ID169, ID173, ID219,
ID245, ID250, ID283, ID356, ID463,
ID471, ID477, ID483, ID489, ID494,
ID499, ID501, ID502
40REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
of the algorithm, the accuracy can increase or
decrease.
The classification of failures according to pre-
defined categories is useful for scheduling possible
maintenance tasks. It is also possible to determine
the state of health of the machine according to the
measured variables at a given time.
Fig. 9 shows the ratio of the outputs of the
algorithms obtained in the systematic mapping,
while Table VII summarizes the Outputs of the
algorithms.
D. RQ 4. What is the maturity of the algorithms
used?
Although machine learning algorithms already
have very concrete applications that are accessible
to the public, the level of maturity in predictive
maintenance is still very low. Most papers focus
on interpreting the data obtained to test different
performance metrics in controlled environments
or laboratories. More than 90% of the papers
selected in this study are only proof of concepts.
The papers that have been carried out in a relevant
environment represent 10% of the total number
of papers selected. Although the breakthrough
to relevant environments has been made, no
prototypes have been developed for testing in a real
environment, nor have the necessary qualifications
for a complete system been achieved.
Fig. 10 shows the proportion of maturity of
machine learning algorithms used in predictive
maintenance, while Table VIII summarizes the
algorithm maturity ratio
Fig. 9: Outputs of The Algorithm
Fig. 10: Algorithm Maturity Ratio
TABLE VII
Outputs of the algorithms on the studied papers
Output Papers Studied
Remaining useful
life RUL
ID1, ID16, ID30, ID32, ID42, ID61,
ID83, ID87, ID100, ID109, ID137, ID140,
ID153, ID156, ID157, ID158, ID165,
ID169, ID173, ID215, ID219, ID245,
ID250, ID406, ID410, ID417, ID425,
ID429, ID431, ID435, ID444, ID448,
ID454,
Faulire classifi-
cation
ID5, ID12, ID20, ID70, ID103, ID144,
ID148, ID188, ID191, ID197, ID206,
ID232, ID240, ID249, ID283, ID291,
ID325, ID396, ID460, ID463, ID471,
ID477, ID483
Health index
ID34, ID41, ID93, ID127, ID172, ID254,
ID272, ID294, ID310, ID334, ID356,
ID343, ID357, ID375, ID378, ID489,
ID494, ID499, ID501, ID502
There is no doubt that predictive strategies are
increasingly being used in industrial maintenance.
By using predictive strategies, they reduce the
costs associated with unplanned downtime and can
maximize their production. Although there is still
no defined methodology for data collection and
processing, progress has been made in this area,
especially since 2014, and the last three years have
seen exponential growth in these topics.
Machine learning emerged in the 90s with the aim
of giving computers intelligence. In this field, there
are already defined methodologies, strategies and
algorithms capable of solving specific problems,
which have been tested in relevant environments
and some form complete certified systems. Today,
with technological advances, it is relatively easy
to implement a machine learning algorithm for
either prediction or classification, and there are
VI. DISCUSSION
of the algorithm, the accuracy can increase or
decrease.
The classification of failures according to pre-
defined categories is useful for scheduling possible
maintenance tasks. It is also possible to determine
the state of health of the machine according to the
measured variables at a given time.
Fig. 9 shows the ratio of the outputs of the
algorithms obtained in the systematic mapping,
while Table VII summarizes the Outputs of the
algorithms.
D. RQ 4. What is the maturity of the algorithms
used?
Although machine learning algorithms already
have very concrete applications that are accessible
to the public, the level of maturity in predictive
maintenance is still very low. Most papers focus
on interpreting the data obtained to test different
performance metrics in controlled environments
or laboratories. More than 90% of the papers
selected in this study are only proof of concepts.
The papers that have been carried out in a relevant
environment represent 10% of the total number
of papers selected. Although the breakthrough
to relevant environments has been made, no
prototypes have been developed for testing in a real
environment, nor have the necessary qualifications
for a complete system been achieved.
Fig. 10 shows the proportion of maturity of
machine learning algorithms used in predictive
maintenance, while Table VIII summarizes the
algorithm maturity ratio
Fig. 9: Outputs of The Algorithm
Fig. 10: Algorithm Maturity Ratio
TABLE VII
Outputs of the algorithms on the studied papers
Output Papers Studied
Remaining useful
life RUL
ID1, ID16, ID30, ID32, ID42, ID61,
ID83, ID87, ID100, ID109, ID137, ID140,
ID153, ID156, ID157, ID158, ID165,
ID169, ID173, ID215, ID219, ID245,
ID250, ID406, ID410, ID417, ID425,
ID429, ID431, ID435, ID444, ID448,
ID454,
Faulire classifi-
cation
ID5, ID12, ID20, ID70, ID103, ID144,
ID148, ID188, ID191, ID197, ID206,
ID232, ID240, ID249, ID283, ID291,
ID325, ID396, ID460, ID463, ID471,
ID477, ID483
Health index
ID34, ID41, ID93, ID127, ID172, ID254,
ID272, ID294, ID310, ID334, ID356,
ID343, ID357, ID375, ID378, ID489,
ID494, ID499, ID501, ID502
There is no doubt that predictive strategies are
increasingly being used in industrial maintenance.
By using predictive strategies, they reduce the
costs associated with unplanned downtime and can
maximize their production. Although there is still
no defined methodology for data collection and
processing, progress has been made in this area,
especially since 2014, and the last three years have
seen exponential growth in these topics.
Machine learning emerged in the 90s with the aim
of giving computers intelligence. In this field, there
are already defined methodologies, strategies and
algorithms capable of solving specific problems,
which have been tested in relevant environments
and some form complete certified systems. Today,
with technological advances, it is relatively easy
to implement a machine learning algorithm for
either prediction or classification, and there are
VI. DISCUSSION
41REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
specialized frameworks and software for this type
of task.
Today, the transition from Industry 3.0 to Industry
4.0 has led to the emergence of new paradigms.
Artificial intelligence is one of the main enablers
of Industry 4.0, which, together with other
technologies such as the Internet of Things, can
create a new dimension of work that combines the
operational and administrative parts of companies.
In this way, both vertical and horizontal integration
will be achieved, providing companies with
competitive tools.
The relationship between predictive maintenance
and machine learning is extremely important, as
PdM is a product of ML. With the large amount of
data generated in today's factories, it is possible to
generate predictive or classification models with
acceptable, albeit improvable, accuracies.
Machine learning algorithms can be classified
according to the type of monitoring and the use
of monitoring, a classification widely accepted by
different authors. It is difficult to add new fields,
as the current ones are sufficiently broad and can
easily include other subcategories that we would
like to propose.
The algorithms proposed in the works related
to predictive maintenance have very solid
mathematical and statistical bases. Some algorithms
have advantages and disadvantages, such as noise
immunity or susceptibility to outliers. From the
review of the papers, there is a tendency to use
hybrid approaches that combine several algorithms
to provide robustness. Such hybrid algorithms still
need to improve their methodologies in order to be
implemented ubiquitously and with relative ease.
On the other hand, these approaches represent
new lines of research for future work that have a
solid foundation.
Although several literature reviews have been
carried out, most seek to establish a taxonomy or
classification of algorithms so that the reader can
have a broad vision and do not focus on a particular
application. However, this work provides the
author with a specific vision of machine learning
algorithms in the field of predictive maintenance.
In addition, the level of maturity of the algorithm
analyzed in the field can be measured, which is
important for implementation in the field. real life
of this type of applications.
Talking about input data would be very broad, as
predictive maintenance can be applied to many
machines, but the most common application is
for induction motors, although there are other
machines such as turbojet engines, gearboxes
or wind generators. However, there are other
approaches, such as monitoring specific machine
parts such as gears or bearings, which are
components with high mechanical wear due to
the working conditions they are exposed to. It
is important to use real data wherever possible
to create models that have real accuracy and can
be extrapolated to other applications. It is also
necessary to consider sharing knowledge with the
scientific community, so it is suggested to use real
rather than synthetic data sets.
Regarding the outputs of the algorithms, the most
common in predictive maintenance is to estimate
the remaining useful life, to have a classification or
to obtain the health index of the machine. However,
other outputs can be obtained, for example by
using supervised algorithms to find relationships
between the data obtained from the machine.
The prediction of remaining useful life can be
improved by using more data and more variables
to obtain estimates with an acceptable margin of
time before damage to the machine occurs.
Finally, the level of maturity of all the works
consulted is still insufficient to obtain a complete
and certified system. However, some works have
been tested in relevant environments such as
factories. This type of contribution can be used as
a basis for designing a prototype and validating it in
real conditions before any necessary certifications
are obtained.
VII. CONCLUSIONS
The main results of a systematic literature review
were presented to review the state of the art of
machine learning algorithms used in predictive
specialized frameworks and software for this type
of task.
Today, the transition from Industry 3.0 to Industry
4.0 has led to the emergence of new paradigms.
Artificial intelligence is one of the main enablers
of Industry 4.0, which, together with other
technologies such as the Internet of Things, can
create a new dimension of work that combines the
operational and administrative parts of companies.
In this way, both vertical and horizontal integration
will be achieved, providing companies with
competitive tools.
The relationship between predictive maintenance
and machine learning is extremely important, as
PdM is a product of ML. With the large amount of
data generated in today's factories, it is possible to
generate predictive or classification models with
acceptable, albeit improvable, accuracies.
Machine learning algorithms can be classified
according to the type of monitoring and the use
of monitoring, a classification widely accepted by
different authors. It is difficult to add new fields,
as the current ones are sufficiently broad and can
easily include other subcategories that we would
like to propose.
The algorithms proposed in the works related
to predictive maintenance have very solid
mathematical and statistical bases. Some algorithms
have advantages and disadvantages, such as noise
immunity or susceptibility to outliers. From the
review of the papers, there is a tendency to use
hybrid approaches that combine several algorithms
to provide robustness. Such hybrid algorithms still
need to improve their methodologies in order to be
implemented ubiquitously and with relative ease.
On the other hand, these approaches represent
new lines of research for future work that have a
solid foundation.
Although several literature reviews have been
carried out, most seek to establish a taxonomy or
classification of algorithms so that the reader can
have a broad vision and do not focus on a particular
application. However, this work provides the
author with a specific vision of machine learning
algorithms in the field of predictive maintenance.
In addition, the level of maturity of the algorithm
analyzed in the field can be measured, which is
important for implementation in the field. real life
of this type of applications.
Talking about input data would be very broad, as
predictive maintenance can be applied to many
machines, but the most common application is
for induction motors, although there are other
machines such as turbojet engines, gearboxes
or wind generators. However, there are other
approaches, such as monitoring specific machine
parts such as gears or bearings, which are
components with high mechanical wear due to
the working conditions they are exposed to. It
is important to use real data wherever possible
to create models that have real accuracy and can
be extrapolated to other applications. It is also
necessary to consider sharing knowledge with the
scientific community, so it is suggested to use real
rather than synthetic data sets.
Regarding the outputs of the algorithms, the most
common in predictive maintenance is to estimate
the remaining useful life, to have a classification or
to obtain the health index of the machine. However,
other outputs can be obtained, for example by
using supervised algorithms to find relationships
between the data obtained from the machine.
The prediction of remaining useful life can be
improved by using more data and more variables
to obtain estimates with an acceptable margin of
time before damage to the machine occurs.
Finally, the level of maturity of all the works
consulted is still insufficient to obtain a complete
and certified system. However, some works have
been tested in relevant environments such as
factories. This type of contribution can be used as
a basis for designing a prototype and validating it in
real conditions before any necessary certifications
are obtained.
VII. CONCLUSIONS
The main results of a systematic literature review
were presented to review the state of the art of
machine learning algorithms used in predictive
42REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
[1] P. Das, S. Perera, S. Senaratne, and R.
Osei-Kyei, “Paving the way for industry 4.0
maturity of construction enterprises: a state
of the art review,” Engineering, Construction
and Architectural Management, vol. ahead-
of-print, no. ahead-of-print, 2022, doi:
10.1108/ECAM-11-2021-1001/FULL/
XML.
[2] T. Borgi, A. Hidri, B. Neef, and M. S. Naceur,
“Data analytics for predictive maintenance
of industrial robots,” Proceedings of
International Conference on Advanced
Systems and Electric Technologies, IC_
ASET 2017, pp. 412–417, Jul. 2017, doi:
10.1109/ASET.2017.7983729.
[3] R. Alkhazaleh, K. Mykoniatis, and A.
Alahmer, “The Success of Technology
Transfer in the Industry 4.0 Era: A
Systematic Literature Review,” Journal of
VIII. Referencias
Open Innovation: Technology, Market, and
Complexity 2022, Vol. 8, Page 202, vol.
8, no. 4, p. 202, Nov. 2022, doi: 10.3390/
JOITMC8040202.
[4] R. S. Peres, A. Dionisio Rocha, P. Leitao,
and J. Barata, “IDARTS – Towards
intelligent data analysis and real-time
supervision for industry 4.0,” Comput
Ind, vol. 101, pp. 138–146, Oct. 2018, doi:
10.1016/J.COMPIND.2018.07.004.
[5] D. Martin, S. Heinzel, J. K. von
Bischhoffshausen, and N. Kühl, “Deep
Learning Strategies for Industrial Surface
Defect Detection Systems,” Proceedings of
the 55th Hawaii International Conference
on System Sciences, Sep. 2021, doi:
10.48550/arxiv.2109.11304.
[6] P. G. Ramesh, S. J. Dutta, S. S. Neog, P.
Baishya, and I. Bezbaruah, “Implementation
of Predictive Maintenance Systems in
Remotely Located Process Plants under
Industry 4.0 Scenario,” Springer Series
in Reliability Engineering, pp. 293–326,
2020, doi: 10.1007/978-3-030-36518-
9_12/COVER.
[7] Y. C. Chiu, F. T. Cheng, and H. C. Huang,
“Developing a factory-wide intelligent
predictive maintenance system based
on Industry 4.0,” https://doi.org/10.10
80/02533839.2017.1362357, vol. 40,
no. 7, pp. 562–571, Oct. 2017, doi:
10.1080/02533839.2017.1362357.
[8] G. M. Sang, L. Xu, and P. de Vrieze, “A
Predictive Maintenance Model for Flexible
Manufacturing in the Context of Industry
4.0,” Front Big Data, vol. 4, p. 61, Aug.
2021, doi: 10.3389/FDATA.2021.663466/
BIBTEX.
[9] A. Mubarak, M. Asmelash, A. Azhari,
T. Alemu, F. Mulubrhan, and K. Saptaji,
“Digital Twin Enabled Industry 4.0
Predictive Maintenance Under Reliability-
Centred Strategy,” 2022 1st International
Conference on Electrical, Electronics,
Information and Communication
Technologies, ICEEICT 2022, 2022, doi:
10.1109/ICEEICT53079.2022.9768590.
[10] K. Sarita, S. Kumar, and R. K. Saket, “Fault
Detection of Smart Grid Equipment Using
maintenance. An overview of algorithms and
predictive maintenance has been provided so
that researchers can further develop this area
of research with new contributions. While for
engineers and technicians, the maturity of the
algorithms is provided for future developments
leading to certification of a complete system.
This field of research is in constant development,
as can be seen in the growing number of articles
published in recent years. Most contributions
attempt to use an algorithm that is tested against
other algorithms to measure its metrics such
as accuracy or error. Machine learning has now
become a highly researched and developed topic
in various fields, although it is still at a low level
of maturity in predictive maintenance.
This work can serve as a theoretical basis for the
generation of hybrid algorithms that are much
more robust, with higher accuracy and lower
error. Consideration should also be given to the
generation of new methods to assist in the collection
and processing of data from industrial machines.
Finally, the topic should be further developed to
reach a level of maturity that allows industrial
companies to adopt this type of technology.
[1] P. Das, S. Perera, S. Senaratne, and R.
Osei-Kyei, “Paving the way for industry 4.0
maturity of construction enterprises: a state
of the art review,” Engineering, Construction
and Architectural Management, vol. ahead-
of-print, no. ahead-of-print, 2022, doi:
10.1108/ECAM-11-2021-1001/FULL/
XML.
[2] T. Borgi, A. Hidri, B. Neef, and M. S. Naceur,
“Data analytics for predictive maintenance
of industrial robots,” Proceedings of
International Conference on Advanced
Systems and Electric Technologies, IC_
ASET 2017, pp. 412–417, Jul. 2017, doi:
10.1109/ASET.2017.7983729.
[3] R. Alkhazaleh, K. Mykoniatis, and A.
Alahmer, “The Success of Technology
Transfer in the Industry 4.0 Era: A
Systematic Literature Review,” Journal of
VIII. Referencias
Open Innovation: Technology, Market, and
Complexity 2022, Vol. 8, Page 202, vol.
8, no. 4, p. 202, Nov. 2022, doi: 10.3390/
JOITMC8040202.
[4] R. S. Peres, A. Dionisio Rocha, P. Leitao,
and J. Barata, “IDARTS – Towards
intelligent data analysis and real-time
supervision for industry 4.0,” Comput
Ind, vol. 101, pp. 138–146, Oct. 2018, doi:
10.1016/J.COMPIND.2018.07.004.
[5] D. Martin, S. Heinzel, J. K. von
Bischhoffshausen, and N. Kühl, “Deep
Learning Strategies for Industrial Surface
Defect Detection Systems,” Proceedings of
the 55th Hawaii International Conference
on System Sciences, Sep. 2021, doi:
10.48550/arxiv.2109.11304.
[6] P. G. Ramesh, S. J. Dutta, S. S. Neog, P.
Baishya, and I. Bezbaruah, “Implementation
of Predictive Maintenance Systems in
Remotely Located Process Plants under
Industry 4.0 Scenario,” Springer Series
in Reliability Engineering, pp. 293–326,
2020, doi: 10.1007/978-3-030-36518-
9_12/COVER.
[7] Y. C. Chiu, F. T. Cheng, and H. C. Huang,
“Developing a factory-wide intelligent
predictive maintenance system based
on Industry 4.0,” https://doi.org/10.10
80/02533839.2017.1362357, vol. 40,
no. 7, pp. 562–571, Oct. 2017, doi:
10.1080/02533839.2017.1362357.
[8] G. M. Sang, L. Xu, and P. de Vrieze, “A
Predictive Maintenance Model for Flexible
Manufacturing in the Context of Industry
4.0,” Front Big Data, vol. 4, p. 61, Aug.
2021, doi: 10.3389/FDATA.2021.663466/
BIBTEX.
[9] A. Mubarak, M. Asmelash, A. Azhari,
T. Alemu, F. Mulubrhan, and K. Saptaji,
“Digital Twin Enabled Industry 4.0
Predictive Maintenance Under Reliability-
Centred Strategy,” 2022 1st International
Conference on Electrical, Electronics,
Information and Communication
Technologies, ICEEICT 2022, 2022, doi:
10.1109/ICEEICT53079.2022.9768590.
[10] K. Sarita, S. Kumar, and R. K. Saket, “Fault
Detection of Smart Grid Equipment Using
maintenance. An overview of algorithms and
predictive maintenance has been provided so
that researchers can further develop this area
of research with new contributions. While for
engineers and technicians, the maturity of the
algorithms is provided for future developments
leading to certification of a complete system.
This field of research is in constant development,
as can be seen in the growing number of articles
published in recent years. Most contributions
attempt to use an algorithm that is tested against
other algorithms to measure its metrics such
as accuracy or error. Machine learning has now
become a highly researched and developed topic
in various fields, although it is still at a low level
of maturity in predictive maintenance.
This work can serve as a theoretical basis for the
generation of hybrid algorithms that are much
more robust, with higher accuracy and lower
error. Consideration should also be given to the
generation of new methods to assist in the collection
and processing of data from industrial machines.
Finally, the topic should be further developed to
reach a level of maturity that allows industrial
companies to adopt this type of technology.
43REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
Machine Learning and Data Analytics,”
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Bakry, “Predictive maintenance analytics
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and opportunities,” Systems Engineering,
2022, doi: 10.1002/SYS.21651.
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Composite Structures, An International
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Bakry, “Predictive maintenance analytics
and implementation for aircraft: Challenges
and opportunities,” Systems Engineering,
2022, doi: 10.1002/SYS.21651.
[12] T. Wuest, D. Weimer, C. Irgens, and
K. D. Thoben, “Machine learning in
manufacturing: advantages, challenges, and
applications,” http://mc.manuscriptcentral.
com/tpmr, vol. 4, no. 1, pp. 23–45, Jun. 2016,
doi: 10.1080/21693277.2016.1192517.
[13] J. Bell, “What Is Machine Learning?,”
Machine Learning and the City, pp. 207–216,
May 2022, doi: 10.1002/9781119815075.
CH18.
[14] V. Garg and A. T. Kalai, “Supervising
Unsupervised Learning,” Adv Neural Inf
Process Syst, vol. 31, 2018.
[15] S.-E. Kim, Q.-V. Vu, G. Papazafeiropoulos,
Z. Kong, and V.-H. Truong, “Comparison of
machine learning algorithms for regression
and classification of ultimate load-carrying
capacity of steel frames,” Steel and
Composite Structures, An International
Journal, vol. 37, no. 2, pp. 193–209,
2020, Accessed: Feb. 23, 2023. [Online].
Available: https://www.dbpia.co.kr/journal/
articleDetail?nodeId=NODE10697357
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learning techniques using decision tree and
support vector machine,” Proceedings - 2nd
International Conference on Computing,
Communication, Control and Automation,
ICCUBEA 2016, Feb. 2017, doi: 10.1109/
ICCUBEA.2016.7860040.
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Bioinformatics), vol. 7473 LNCS, pp. 246–
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Cramer, M. F. Chiang, and J. Peter Campbell,
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Networks, and Deep Learning,” Transl Vis
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2020, doi: 10.1167/TVST.9.2.14.
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“The k-means Algorithm: A Comprehensive
Survey and Performance Evaluation,”
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IX. Appendix
LIST OF STUDIED PAPERS
ID Reference Authors Year
1 Evaluating time series encoding techniques for Predictive Maintenan-
ce
De Santo, A.; Ferraro, A.; Galli, A.; Moscato, V.;
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IX. Appendix
LIST OF STUDIED PAPERS
ID Reference Authors Year
1 Evaluating time series encoding techniques for Predictive Maintenan-
ce
De Santo, A.; Ferraro, A.; Galli, A.; Moscato, V.;
Sperlì, G. 2022
5 Logistic-ELM: a novel fault diagnosis method for rolling bearings Tan, Z.; Ning, J.; Peng, K.; Xia, Z.; Wu, D. 2022
45REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
12 Data Science Application for Failure Data Management and Failure
Prediction in the Oil and Gas Industry: A Case Study
Arena, S.; Manca, G.; Murru, S.; Orrù, P.F.;
Perna, R.; Reforgiato Recupero, D. 2022
16 Fault detection and diagnosis for industrial processes based on cluste-
ring and autoencoders: a case of gas turbines Barrera, J.M.; Reina, A.; Mate, A.; Trujillo, J.C. 2022
20 Predicting Bearings Degradation Stages for Predictive Maintenance in
the Pharmaceutical Industry
Juodelyte, D.; Cheplygina, V.; Graversen, T.;
Bonnet, P. 2022
30 Remaining Useful Life Estimation of Cooling Units via Time-Fre-
quency Health Indicators with Machine Learning Rosero, R.L.; Silva, C.; Ribeiro, B. 2022
32 Simulation for predictive maintenance using weighted training algori-
thms in machine learning Jittawiriyanukoon, C.; Srisarkun, V. 2022
34 Real-Time Induction Motor Health Index Prediction in A Petrochemi-
cal Plant using Machine Learning Khrakhuean, W.; Chutima, P. 2022
41 Selecting an appropriate supervised machine learning algorithm for
predictive maintenance Ouadah, A.; Zemmouchi-Ghomari, L.; Salhi, N. 2022
42 On the Importance of Temporal Information for Remaining Useful
Life Prediction of Rolling Bearings Using a Random Forest Regressor
Bienefeld, C.; Kirchner, E.; Vogt, A.; Kacmar,
M. 2022
61 A recurrent neural network method for condition monitoring and
predictive maintenance of pressure vessel components Halliday, C.; Palmer, I.; Joyce, M.; Pready, N. 2022
70 Remaining Useful Life Estimation Using Fault to Failure Transforma-
tion in Process Systems
Arunthavanathan, R.; Khan, F.; Ahmed, S.;
Imtiaz, S. 2022
83 Estimation of Remaining Useful Life (RUL) of BLDC Motor using
Machine Learning Approaches Sree, R.D.; Jayanthy, S.; Vigneshwaran, E.E. 2022
87 Predictive Maintenance Algorithm Based on Machine Learning for
Industrial Asset
Alfaro-Nango, A.J.; Escobar-Gomez, E.N.;
Chandomi-Castellanos, E.; Velazquez-Trujillo,
S.; Hernandez-De-Leon, H.R.; Blanco-Gonzalez,
L.M.
2022
93 RNN-Autoencoder Approach for Anomaly Detection in Power Plant
Predictive Maintenance Systems
Santoso, B.; Anggraeni, W.; Pariaman, H.; Pur-
nomo, M.H. 2022
100 Predicting Remaining Useful Life of Wind Turbine Bearing using
Linear Regression Jellali, A.; Maatallah, H.; Ouni, K. 2022
103 Predictive Maintenance of Hydraulic System using Machine Learning
Algorithms Yugapriya, M.; Judeson, A.K.J.; Jayanthy, S. 2022
109
An Artificial Intelligence Neural Network Predictive Model for Ano-
maly Detection and Monitoring of Wind Turbines Using SCADA
Data
Amini, A.; Kanfoud, J.; Gan, T.-H. 2022
127 Predictive maintenance of abnormal wind turbine events by using ma-
chine learning based on condition monitoring for anomaly detection
Chen, H.; Hsu, J.-Y.; Hsieh, J.-Y.; Hsu, H.-Y.;
Chang, C.-H.; Lin, Y.-J. 2021
137 Remaining Useful Life Prediction of Equipment Based on XGBoost Jia, Z.; Xiao, Z.; Shi, Y. 2021
140
Integrating physics and data driven cyber-physical system for condi-
tion monitoring of critical transmission components in smart produc-
tion line
Song, L.; Wang, L.; Wu, J.; Liang, J.; Liu, Z. 2021
144 Application of machine learning to a medium gaussian support vector
machine in the diagnosis of motor bearing faults Lin, S.-L. 2021
148 IIoT Solution for predictive monitoring based on vibration data from
motors using Microsoft Azure machine learning studio and Power BI
Ferreira, R.H.M.S.; de Figueiredo, L.O.; Lima,
R.B.C.; Silva, L.A.P.; Barros, P.R. 2021
153 Predictive Analytics of Machine Failure using Linear Regression on
KNIME Platform Pakhir, E.A.; Ayuni, N. 2021
156 Predictive maintenance system for production lines in manufacturing:
A machine learning approach using IoT data in real-time Ayvaz, S.; Alpay, K. 2021
157 An applicable predictive maintenance framework for the absence of
run-to-failure data Kim, D.; Lee, S.; Kim, D. 2021
158 Two stage deep learning for prognostics using multi-loss encoder and
convolutional composite features Pillai, S.; Vadakkepat, P. 2021
165 Intelligent Predictive Maintenance Model for Rolling Components of
a Machine based on Speed and Vibration
Ahmad, B.; Mishra, B.K.; Ghufran, M.; Pervez,
Z.; Ramzan, N. 2021
169 Remaining Useful Life Prediction of Bearings Using Ensemble Lear-
ning: The Impact of Diversity in Base Learners and Features Shi, J.; Yu, T.; Goebel, K.; Wu, D. 2021
172 A deep learning model for predictive maintenance in cyber-physical
production systems using LSTM autoencoders
Bampoula, X.; Siaterlis, G.; Nikolakis, N.; Ale-
xopoulos, K. 2021
173 Remaining useful life (Rul) prediction of equipment in production
lines using artificial neural networks Kang, Z.; Catal, C.; Tekinerdogan, B. 2021
188 A Novel Approach for Incipient Fault Diagnosis in Power Transfor-
mers by Artificial Neural Networks De Andrade Lopes, S.M.; Flauzino, R.A. 2021
12 Data Science Application for Failure Data Management and Failure
Prediction in the Oil and Gas Industry: A Case Study
Arena, S.; Manca, G.; Murru, S.; Orrù, P.F.;
Perna, R.; Reforgiato Recupero, D. 2022
16 Fault detection and diagnosis for industrial processes based on cluste-
ring and autoencoders: a case of gas turbines Barrera, J.M.; Reina, A.; Mate, A.; Trujillo, J.C. 2022
20 Predicting Bearings Degradation Stages for Predictive Maintenance in
the Pharmaceutical Industry
Juodelyte, D.; Cheplygina, V.; Graversen, T.;
Bonnet, P. 2022
30 Remaining Useful Life Estimation of Cooling Units via Time-Fre-
quency Health Indicators with Machine Learning Rosero, R.L.; Silva, C.; Ribeiro, B. 2022
32 Simulation for predictive maintenance using weighted training algori-
thms in machine learning Jittawiriyanukoon, C.; Srisarkun, V. 2022
34 Real-Time Induction Motor Health Index Prediction in A Petrochemi-
cal Plant using Machine Learning Khrakhuean, W.; Chutima, P. 2022
41 Selecting an appropriate supervised machine learning algorithm for
predictive maintenance Ouadah, A.; Zemmouchi-Ghomari, L.; Salhi, N. 2022
42 On the Importance of Temporal Information for Remaining Useful
Life Prediction of Rolling Bearings Using a Random Forest Regressor
Bienefeld, C.; Kirchner, E.; Vogt, A.; Kacmar,
M. 2022
61 A recurrent neural network method for condition monitoring and
predictive maintenance of pressure vessel components Halliday, C.; Palmer, I.; Joyce, M.; Pready, N. 2022
70 Remaining Useful Life Estimation Using Fault to Failure Transforma-
tion in Process Systems
Arunthavanathan, R.; Khan, F.; Ahmed, S.;
Imtiaz, S. 2022
83 Estimation of Remaining Useful Life (RUL) of BLDC Motor using
Machine Learning Approaches Sree, R.D.; Jayanthy, S.; Vigneshwaran, E.E. 2022
87 Predictive Maintenance Algorithm Based on Machine Learning for
Industrial Asset
Alfaro-Nango, A.J.; Escobar-Gomez, E.N.;
Chandomi-Castellanos, E.; Velazquez-Trujillo,
S.; Hernandez-De-Leon, H.R.; Blanco-Gonzalez,
L.M.
2022
93 RNN-Autoencoder Approach for Anomaly Detection in Power Plant
Predictive Maintenance Systems
Santoso, B.; Anggraeni, W.; Pariaman, H.; Pur-
nomo, M.H. 2022
100 Predicting Remaining Useful Life of Wind Turbine Bearing using
Linear Regression Jellali, A.; Maatallah, H.; Ouni, K. 2022
103 Predictive Maintenance of Hydraulic System using Machine Learning
Algorithms Yugapriya, M.; Judeson, A.K.J.; Jayanthy, S. 2022
109
An Artificial Intelligence Neural Network Predictive Model for Ano-
maly Detection and Monitoring of Wind Turbines Using SCADA
Data
Amini, A.; Kanfoud, J.; Gan, T.-H. 2022
127 Predictive maintenance of abnormal wind turbine events by using ma-
chine learning based on condition monitoring for anomaly detection
Chen, H.; Hsu, J.-Y.; Hsieh, J.-Y.; Hsu, H.-Y.;
Chang, C.-H.; Lin, Y.-J. 2021
137 Remaining Useful Life Prediction of Equipment Based on XGBoost Jia, Z.; Xiao, Z.; Shi, Y. 2021
140
Integrating physics and data driven cyber-physical system for condi-
tion monitoring of critical transmission components in smart produc-
tion line
Song, L.; Wang, L.; Wu, J.; Liang, J.; Liu, Z. 2021
144 Application of machine learning to a medium gaussian support vector
machine in the diagnosis of motor bearing faults Lin, S.-L. 2021
148 IIoT Solution for predictive monitoring based on vibration data from
motors using Microsoft Azure machine learning studio and Power BI
Ferreira, R.H.M.S.; de Figueiredo, L.O.; Lima,
R.B.C.; Silva, L.A.P.; Barros, P.R. 2021
153 Predictive Analytics of Machine Failure using Linear Regression on
KNIME Platform Pakhir, E.A.; Ayuni, N. 2021
156 Predictive maintenance system for production lines in manufacturing:
A machine learning approach using IoT data in real-time Ayvaz, S.; Alpay, K. 2021
157 An applicable predictive maintenance framework for the absence of
run-to-failure data Kim, D.; Lee, S.; Kim, D. 2021
158 Two stage deep learning for prognostics using multi-loss encoder and
convolutional composite features Pillai, S.; Vadakkepat, P. 2021
165 Intelligent Predictive Maintenance Model for Rolling Components of
a Machine based on Speed and Vibration
Ahmad, B.; Mishra, B.K.; Ghufran, M.; Pervez,
Z.; Ramzan, N. 2021
169 Remaining Useful Life Prediction of Bearings Using Ensemble Lear-
ning: The Impact of Diversity in Base Learners and Features Shi, J.; Yu, T.; Goebel, K.; Wu, D. 2021
172 A deep learning model for predictive maintenance in cyber-physical
production systems using LSTM autoencoders
Bampoula, X.; Siaterlis, G.; Nikolakis, N.; Ale-
xopoulos, K. 2021
173 Remaining useful life (Rul) prediction of equipment in production
lines using artificial neural networks Kang, Z.; Catal, C.; Tekinerdogan, B. 2021
188 A Novel Approach for Incipient Fault Diagnosis in Power Transfor-
mers by Artificial Neural Networks De Andrade Lopes, S.M.; Flauzino, R.A. 2021
46REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
191 A Combination of Fourier Transform and Machine Learning for Fault
Detection and Diagnosis of Induction Motors
Duc Nguyen, V.; Zwanenburg, E.; Limmer, S.;
Luijben, W.; Back, T.; Olhofer, M. Limmer, S.;
Luijben, W.; Back, T.; Olhofer, M.
2021
197 Motor classification with machine learning methods for predictive
maintenance
Kammerer, C.; Gaust, M.; Küstner, M.; Starke, P.;
Radtke, R.; Jesser, A. 2021
206 Machine Learning Based Predictive Maintenance System for Indus-
trial Chain Conveyor System
Goh, K.W.; Chaw, K.H.; Yong, J.C.E.; Koh, Y.S.;
Dares, M.; Su, E.L.M.; Yeong, C.F. 2021
208 Bearing Fault Detection Using Comparative Analysis of Random
Forest, ANN, and Autoencoder Methods
Kamat, P.; Marni, P.; Cardoz, L.; Irani, A.; Gaju-
la, A.; Saha, A.; Kumar, S.; Sugandhi, R. 2021
215 Fault Detection of Smart Grid Equipment Using Machine Learning
and Data Analytics Sarita, K.; Kumar, S.; Saket, R.K. 2021
219 Automatic Remaining Useful Life Estimation Framework with Embe-
dded Convolutional LSTM as the Backbone
Zhou, Y.; Hefenbrock, M.; Huang, Y.; Riedel, T.;
Beigl, M. 2021
232 On-line fault diagnosis of rolling bearing based on machine learning
algorithm Sun, J.; Yu, Z.; Wang, H. 2020
240 EEMD assisted supervised learning for the fault diagnosis of BLDC
motor using vibration signal Shifat, T.A.; Hur, J.-W. 2020
245 Experimental analysis of machine learning algorithms used in predic-
tive maintenance
Sahasrabudhe, N.; Asegaonkar, R.; Deo, S.;
Umredkar, S.; Mundada, K. 2020
249 Wind power forecasting of an offshore wind turbine based on hi-
gh-frequency SCADA data and deep learning neural network Lin, Z.; Liu, X. 2020
250 A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault
Prediction
Tzanis, N.; Andriopoulos, N.; Magklaras, A.;
Mylonas, E.; Birbas, M.; Birbas, A. 2020
254 Advanced Predictive Maintenance with Machine Learning Failure
Estimation in Industrial Packaging Robots Koca, O.; Kaymakci, O.T.; Mercimek, M. 2020
272 Failure Prediction of Aircraft Equipment Using Machine Learning
with a Hybrid Data Preparation Method Celikmih, K.; Inan, O.; Uguz, H. 2020
283 Predictive model for the degradation state of a hydraulic system with
dimensionality reduction Quatrini, E.; Costantino, F.; Pocci, C.; Tronci, M. 2020
291 Predictive Maintenance of Air Conditioning Systems Using Supervi-
sed Machine Learning
Trivedi, S.; Bhola, S.; Talegaonkar, A.; Gaur, P.;
Sharma, S. 2019
294 The Prediction of Remaining Useful Life (RUL) in Oil and Gas In-
dustry using Artificial Neural Network (ANN) Algorithm Fauzi, M.F.A.M.; Aziz, I.A.; Amiruddin, A. 2019
310 A deep learning approach for failure prognostics of rolling element
bearings Sadoughi, M.; Lu, H.; Hu, C. 2019
325 Intelligent fault diagnosis for rotating machines using deep learning
Sumba, J.C.; Quinde, I.R.; Ochoa, L.E.; Martí-
nez, J.C.T.; Vallejo Guevara, A.J.; Morales-Me-
nendez, R.
2019
334 Robot fault detection and remaining life estimation for predictive
maintenance Pinto, R.; Cerquitelli, T. 2019
343 Fault Analysis and Predictive Maintenance of Induction Motor Using
Machine Learning Kavana, V.; Neethi, M. 2018
356 Prediction of Remaining Useful Lifetime (RUL) of turbofan engine
using machine learning
Mathew, V.; Toby, T.; Singh, V.; Rao, B.M.;
Kumar, M.G. 2018
357 Transformer Fault Condition Prognosis Using Vibration Signals over
Cloud Environment Bagheri, M.; Zollanvari, A.; Nezhivenko, S. 2018
375 Increasing production efficiency via compressor failure predictive
analytics using machine learning
Pandya, D.; Srivastava, A.; Doherty, A.; Sunda-
reshwar, S.; Needham, C.; Chaudry, A.; Krish-
nalyer, S.
2018
378 Predictive maintenance strategy of running fault based on ELM algo-
rithm for power transformer Wu, Q.; Yang, X.; Deng, R. 2018
396 Fault diagnosis of automobile gearbox based on machine learning
techniques
Praveenkumar, T.; Saimurugan, M.; Krishnaku-
mar, P.; Ramachandran, K.I. 2014
406 Machine Learning for the Detection and Diagnosis of Anomalies in
Applications Driven by Electric Motors Ferraz, F.; Romero, R.; Hsieh, S. 2023
410 Neural Networks Detect Inter-Turn Short Circuit Faults Using Inverter
Switching Statistics for a Closed-Loop Controlled Motor Drive Oner, M.; Sahin, I.; Keysan, O. 2023
417 An Approach to Predicting the Residual Life of an Electric Locomo-
tive Traction Motor Sidorenko. V.; Kulaginm M. 2023
425 Optoelectronic sensor fault detection based predictive maintenance
smart industry 4.0 using machine learning techniques Zhu, C.; Shao, S. 2023
429 A Robot-Operation-System-Based Smart Machine Box and Its Appli-
cation on Predictive Maintenance Chang, Y.; Chai, Y.; Li, B.; Lin, H. 2023
191 A Combination of Fourier Transform and Machine Learning for Fault
Detection and Diagnosis of Induction Motors
Duc Nguyen, V.; Zwanenburg, E.; Limmer, S.;
Luijben, W.; Back, T.; Olhofer, M. Limmer, S.;
Luijben, W.; Back, T.; Olhofer, M.
2021
197 Motor classification with machine learning methods for predictive
maintenance
Kammerer, C.; Gaust, M.; Küstner, M.; Starke, P.;
Radtke, R.; Jesser, A. 2021
206 Machine Learning Based Predictive Maintenance System for Indus-
trial Chain Conveyor System
Goh, K.W.; Chaw, K.H.; Yong, J.C.E.; Koh, Y.S.;
Dares, M.; Su, E.L.M.; Yeong, C.F. 2021
208 Bearing Fault Detection Using Comparative Analysis of Random
Forest, ANN, and Autoencoder Methods
Kamat, P.; Marni, P.; Cardoz, L.; Irani, A.; Gaju-
la, A.; Saha, A.; Kumar, S.; Sugandhi, R. 2021
215 Fault Detection of Smart Grid Equipment Using Machine Learning
and Data Analytics Sarita, K.; Kumar, S.; Saket, R.K. 2021
219 Automatic Remaining Useful Life Estimation Framework with Embe-
dded Convolutional LSTM as the Backbone
Zhou, Y.; Hefenbrock, M.; Huang, Y.; Riedel, T.;
Beigl, M. 2021
232 On-line fault diagnosis of rolling bearing based on machine learning
algorithm Sun, J.; Yu, Z.; Wang, H. 2020
240 EEMD assisted supervised learning for the fault diagnosis of BLDC
motor using vibration signal Shifat, T.A.; Hur, J.-W. 2020
245 Experimental analysis of machine learning algorithms used in predic-
tive maintenance
Sahasrabudhe, N.; Asegaonkar, R.; Deo, S.;
Umredkar, S.; Mundada, K. 2020
249 Wind power forecasting of an offshore wind turbine based on hi-
gh-frequency SCADA data and deep learning neural network Lin, Z.; Liu, X. 2020
250 A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault
Prediction
Tzanis, N.; Andriopoulos, N.; Magklaras, A.;
Mylonas, E.; Birbas, M.; Birbas, A. 2020
254 Advanced Predictive Maintenance with Machine Learning Failure
Estimation in Industrial Packaging Robots Koca, O.; Kaymakci, O.T.; Mercimek, M. 2020
272 Failure Prediction of Aircraft Equipment Using Machine Learning
with a Hybrid Data Preparation Method Celikmih, K.; Inan, O.; Uguz, H. 2020
283 Predictive model for the degradation state of a hydraulic system with
dimensionality reduction Quatrini, E.; Costantino, F.; Pocci, C.; Tronci, M. 2020
291 Predictive Maintenance of Air Conditioning Systems Using Supervi-
sed Machine Learning
Trivedi, S.; Bhola, S.; Talegaonkar, A.; Gaur, P.;
Sharma, S. 2019
294 The Prediction of Remaining Useful Life (RUL) in Oil and Gas In-
dustry using Artificial Neural Network (ANN) Algorithm Fauzi, M.F.A.M.; Aziz, I.A.; Amiruddin, A. 2019
310 A deep learning approach for failure prognostics of rolling element
bearings Sadoughi, M.; Lu, H.; Hu, C. 2019
325 Intelligent fault diagnosis for rotating machines using deep learning
Sumba, J.C.; Quinde, I.R.; Ochoa, L.E.; Martí-
nez, J.C.T.; Vallejo Guevara, A.J.; Morales-Me-
nendez, R.
2019
334 Robot fault detection and remaining life estimation for predictive
maintenance Pinto, R.; Cerquitelli, T. 2019
343 Fault Analysis and Predictive Maintenance of Induction Motor Using
Machine Learning Kavana, V.; Neethi, M. 2018
356 Prediction of Remaining Useful Lifetime (RUL) of turbofan engine
using machine learning
Mathew, V.; Toby, T.; Singh, V.; Rao, B.M.;
Kumar, M.G. 2018
357 Transformer Fault Condition Prognosis Using Vibration Signals over
Cloud Environment Bagheri, M.; Zollanvari, A.; Nezhivenko, S. 2018
375 Increasing production efficiency via compressor failure predictive
analytics using machine learning
Pandya, D.; Srivastava, A.; Doherty, A.; Sunda-
reshwar, S.; Needham, C.; Chaudry, A.; Krish-
nalyer, S.
2018
378 Predictive maintenance strategy of running fault based on ELM algo-
rithm for power transformer Wu, Q.; Yang, X.; Deng, R. 2018
396 Fault diagnosis of automobile gearbox based on machine learning
techniques
Praveenkumar, T.; Saimurugan, M.; Krishnaku-
mar, P.; Ramachandran, K.I. 2014
406 Machine Learning for the Detection and Diagnosis of Anomalies in
Applications Driven by Electric Motors Ferraz, F.; Romero, R.; Hsieh, S. 2023
410 Neural Networks Detect Inter-Turn Short Circuit Faults Using Inverter
Switching Statistics for a Closed-Loop Controlled Motor Drive Oner, M.; Sahin, I.; Keysan, O. 2023
417 An Approach to Predicting the Residual Life of an Electric Locomo-
tive Traction Motor Sidorenko. V.; Kulaginm M. 2023
425 Optoelectronic sensor fault detection based predictive maintenance
smart industry 4.0 using machine learning techniques Zhu, C.; Shao, S. 2023
429 A Robot-Operation-System-Based Smart Machine Box and Its Appli-
cation on Predictive Maintenance Chang, Y.; Chai, Y.; Li, B.; Lin, H. 2023
47REVISTA PERSPECTIVASVOLUMEN 7, N˚1 / ENERO - JULIO 2025 / e - ISSN: 2661
431 Improving Reliability of 2 Wheelers Using Predictive Diagnostics Vijaykumar, S.; Sabu, A.; Pradhan, D.; Shrivard-
hankar, Y. 2023
435 Remaining Useful Life Estimation Using Fault to Failure Transforma-
tion in Process Systems
Arunthavanathan, R.; Khan, F.; Ahmed, S.;
Imtiaz, S. 2023
444 Tool remaining useful life prediction using bidirectional recurrent
neural networks (BRNN)
De Barrera, T.; Ferrando, J.; García, A.; Badiola,
X.; de Buruaga, M.; Vicente, J. 2023
448 Remaining cycle time prediction with Graph Neural Networks for
Predictive Process Monitoring
Duong, L.; Travé-Massuyès, L.; Subias, A.;
Merle, C. 2023
454 Remaining Useful Life Estimation for Railway Gearbox Bearings
Using Machine Learning
Beqiri, L.; Bakhshi, Z.; Punnekkat, S.; Cicchetti,
A. 2023
460 An IoT and Machine Learning-Based Predictive Maintenance System
for Electrical Motors Mohammed, N.; Abdulateef, O.; Hamad, A. 2023
463 A machine learning based predictive maintenance algorithm for ship
generator engines using engine simulations and collected ship data
Park J.;
Oh, J. 2023
471 Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions
for Improving Energy Extraction Using Machine Learning Approach Sethi, M.; Sahoo, S.; Dhanraj, J.; Sugumaran, V. 2023
477 LSTM-Based Condition Monitoring and Fault Prognostics of Rolling
Element Bearings Using Raw Vibrational Data Afridi, Y.; Hasan, L.; Ullah, R.; Ahmad, Z. 2023
483 Multivariate Time-Series Classification of Critical Events from Indus-
trial Drying Hopper Operations: A Deep Learning Approach Rahman, M.; Farahani, M.; Wuest, T. 2023
489 Predicting Forced Blower Failures Using Machine Learning Algori-
thms and Vibration Data for Effective Maintenance Strategies Salem, K.; AbdelGwad, E.; Kouta, H. 2023
494
Using machine learning and deep learning algorithms for downtime
minimization in manufacturing systems: an early failure detection
diagnostic service
Shahin, M.; Chen, F.; Hosseinzadeh, A.; Zand,
N. 2023
499 Anomaly detection in the temperature of an ac motor using embedded
machine learning Ismail, E.; Ahmad, M. 2023
501 Early detection of tool wear in electromechanical broaching machines
by monitoring main stroke servomotors
Aldekoa, I.; del Olmo, A.; Sastoque-Pinilla, L.;
Sendino-Mouliet, S.; Lopez-Nova, U.; del La-
calle, L.
2023
502 Remaining useful lifetime prediction for predictive maintenance in
manufacturing Taşcı, B.; Omar, A.; Ayvaz, S. 2023
431 Improving Reliability of 2 Wheelers Using Predictive Diagnostics Vijaykumar, S.; Sabu, A.; Pradhan, D.; Shrivard-
hankar, Y. 2023
435 Remaining Useful Life Estimation Using Fault to Failure Transforma-
tion in Process Systems
Arunthavanathan, R.; Khan, F.; Ahmed, S.;
Imtiaz, S. 2023
444 Tool remaining useful life prediction using bidirectional recurrent
neural networks (BRNN)
De Barrera, T.; Ferrando, J.; García, A.; Badiola,
X.; de Buruaga, M.; Vicente, J. 2023
448 Remaining cycle time prediction with Graph Neural Networks for
Predictive Process Monitoring
Duong, L.; Travé-Massuyès, L.; Subias, A.;
Merle, C. 2023
454 Remaining Useful Life Estimation for Railway Gearbox Bearings
Using Machine Learning
Beqiri, L.; Bakhshi, Z.; Punnekkat, S.; Cicchetti,
A. 2023
460 An IoT and Machine Learning-Based Predictive Maintenance System
for Electrical Motors Mohammed, N.; Abdulateef, O.; Hamad, A. 2023
463 A machine learning based predictive maintenance algorithm for ship
generator engines using engine simulations and collected ship data
Park J.;
Oh, J. 2023
471 Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions
for Improving Energy Extraction Using Machine Learning Approach Sethi, M.; Sahoo, S.; Dhanraj, J.; Sugumaran, V. 2023
477 LSTM-Based Condition Monitoring and Fault Prognostics of Rolling
Element Bearings Using Raw Vibrational Data Afridi, Y.; Hasan, L.; Ullah, R.; Ahmad, Z. 2023
483 Multivariate Time-Series Classification of Critical Events from Indus-
trial Drying Hopper Operations: A Deep Learning Approach Rahman, M.; Farahani, M.; Wuest, T. 2023
489 Predicting Forced Blower Failures Using Machine Learning Algori-
thms and Vibration Data for Effective Maintenance Strategies Salem, K.; AbdelGwad, E.; Kouta, H. 2023
494
Using machine learning and deep learning algorithms for downtime
minimization in manufacturing systems: an early failure detection
diagnostic service
Shahin, M.; Chen, F.; Hosseinzadeh, A.; Zand,
N. 2023
499 Anomaly detection in the temperature of an ac motor using embedded
machine learning Ismail, E.; Ahmad, M. 2023
501 Early detection of tool wear in electromechanical broaching machines
by monitoring main stroke servomotors
Aldekoa, I.; del Olmo, A.; Sastoque-Pinilla, L.;
Sendino-Mouliet, S.; Lopez-Nova, U.; del La-
calle, L.
2023
502 Remaining useful lifetime prediction for predictive maintenance in
manufacturing Taşcı, B.; Omar, A.; Ayvaz, S. 2023