A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for...

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Published inEnergies (Basel) Vol. 15; no. 23; p. 8938
Main Authors Kumar, Rahul R., Andriollo, Mauro, Cirrincione, Giansalvo, Cirrincione, Maurizio, Tortella, Andrea
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2022
MDPI
Subjects
artificial intelligence
classical techniques
data-driven
Electric fault location
Engineering Sciences
Failure
Fault diagnosis
Induction electric motors
Machine learning
Maintenance costs
Methods
model-based
motor
Preventive maintenance
Signal processing
Fiji
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Abstract This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
AbstractList This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
Audience Academic
Author Cirrincione, Giansalvo
Andriollo, Mauro
Cirrincione, Maurizio
Tortella, Andrea
Kumar, Rahul R.
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  givenname: Rahul R.
  orcidid: 0000-0002-9287-7197
  surname: Kumar
  fullname: Kumar, Rahul R.
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  givenname: Mauro
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  surname: Andriollo
  fullname: Andriollo, Mauro
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  givenname: Giansalvo
  surname: Cirrincione
  fullname: Cirrincione, Giansalvo
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  givenname: Maurizio
  surname: Cirrincione
  fullname: Cirrincione, Maurizio
– sequence: 5
  givenname: Andrea
  orcidid: 0000-0001-5974-5830
  surname: Tortella
  fullname: Tortella, Andrea
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Snippet This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of...
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SubjectTerms artificial intelligence
classical techniques
data-driven
Electric fault location
Engineering Sciences
Failure
Fault diagnosis
Induction electric motors
Machine learning
Maintenance costs
Methods
model-based
motor
Preventive maintenance
Signal processing
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Title A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors
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Volume 15
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