The Bearing Faults Detection Methods for Electrical Machines—The State of the Art

Muhammad Amir Khan, Bilal Asad (), Karolina Kudelina, Toomas Vaimann and Ants Kallaste
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Muhammad Amir Khan: Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Bilal Asad: Department of Electrical Power Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Karolina Kudelina: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia
Toomas Vaimann: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia
Ants Kallaste: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 12616 Tallinn, Estonia

Energies, 2022, vol. 16, issue 1, 1-54

Abstract: Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.

Keywords: bearing fault diagnosis; condition monitoring; feature extraction; fault detection and diagnoses; principal component analysis; neural networks; vibration signals; spectral analysis; genetic algorithm; support vector machines; power spectral density (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
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