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

Rahul R. Kumar, Mauro Andriollo, Giansalvo Cirrincione, Maurizio Cirrincione () and Andrea Tortella
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Rahul R. Kumar: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag Laucala Campus, Suva, Fiji Islands
Mauro Andriollo: Electrical Machines Lab, University of Padova, 35121 Padova, Italy
Giansalvo Cirrincione: Laboratory of Novel Technologies, University of Picardie Jules Verne, 80000 Amiens, France
Maurizio Cirrincione: School of Information Technology, Engineering, Mathematics and Physics, University of the South Pacific, Private Mail Bag Laucala Campus, Suva, Fiji Islands
Andrea Tortella: Electrical Machines Lab, University of Padova, 35121 Padova, Italy

Energies, 2022, vol. 15, issue 23, 1-36

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.

Keywords: motor; classical techniques; artificial intelligence; signal processing; model-based; data-driven; electrical drives; fault statistics; stator fault; broken rotor bars; bearing; deep learning; fault diagnosis; condition monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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