Prognostics and health management for induction machines: a comprehensive review

Chao Huang (), Siqi Bu (), Hiu Hung Lee (), Kwong Wah Chan () and Winco K. C. Yung ()
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Chao Huang: The Hong Kong Polytechnic University
Siqi Bu: The Hong Kong Polytechnic University
Hiu Hung Lee: Centre for Advances in Reliability and Safety
Kwong Wah Chan: Centre for Advances in Reliability and Safety
Winco K. C. Yung: Centre for Advances in Reliability and Safety

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 3, No 1, 937-962

Abstract: Abstract Induction machines (IMs) are utilized in different industrial sectors such as manufacturing, transportation, transmission, and energy due to their ruggedness, low cost, and high efficiency. If IMs fail without advanced warning, unscheduled maintenance needs to be performed, leading to downtime and maintenance costs for asset owners. To avoid these, conducting prognostics and health management (PHM) for IMs is indispensable. There are different PHM methods (expert knowledge, physics-based, and machine learning) to analyze the health and estimate the remaining useful life (RUL) of IMs. It is essential to select appropriate methods and algorithms to solve practical engineering problems by comparing their pros and cons. This paper will systematically summarize the application of the PHM framework to IMs and comprehensively present how to select appropriate general methods as well as specific algorithms applied in the PHM for IMs to solve practical engineering problems, aiming to provide some guidance for future researchers and practitioners.

Keywords: Prognostics and health management; Induction machines; Computational intelligence; Deep learning; Transfer learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02103-6

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