4.5 Review

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

Journal

ENERGIES
Volume 15, Issue 23, Pages -

Publisher

MDPI
DOI: 10.3390/en15238938

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

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This review paper provides a brief overview of conventional approaches and examines intelligent means for fault diagnosis and condition monitoring of electrical drives, with a focus on those common in Industry 4.0. The paper discusses various diagnostic techniques and their advancements, highlighting the effectiveness of model, signal, and data-based techniques for fault diagnosis and condition monitoring of induction motors. Additionally, neural and non-neural-based approaches for major faults in rotating machines are explored.
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.

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