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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rahul R. Kumar & Mauro Andriollo & Giansalvo Cirrincione & Maurizio Cirrincione & Andrea Tortella, 2022. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors," Energies, MDPI, vol. 15(23), pages 1-36, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8938-:d:984644
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    References listed on IDEAS

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    1. Ganesh Kumar Balakrishnan & Chong Tak Yaw & Siaw Paw Koh & Tarek Abedin & Avinash Ashwin Raj & Sieh Kiong Tiong & Chai Phing Chen, 2022. "A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations," Energies, MDPI, vol. 15(16), pages 1-37, August.
    2. Levent Eren, 2017. "Bearing Fault Detection by One-Dimensional Convolutional Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, July.
    3. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
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    Cited by:

    1. Reza Bazghandi & Mohammad Hoseintabar Marzebali & Vahid Abolghasemi & Shahin Hedayati Kia, 2023. "A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines," Energies, MDPI, vol. 16(14), pages 1-17, July.
    2. Muhammad Usman Sardar & Toomas Vaimann & Lauri Kütt & Ants Kallaste & Bilal Asad & Siddique Akbar & Karolina Kudelina, 2023. "Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques," Energies, MDPI, vol. 16(15), pages 1-41, July.
    3. Sarahi Aguayo-Tapia & Gerardo Avalos-Almazan & Jose de Jesus Rangel-Magdaleno & Juan Manuel Ramirez-Cortes, 2023. "Physical Variable Measurement Techniques for Fault Detection in Electric Motors," Energies, MDPI, vol. 16(12), pages 1-21, June.

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