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The most common Induction Motor (IM) faults discussed in literature are of three types, namely, bearing faults, stator faults, and rotor faults. These faults often result in unexpected failures or unplanned shutdowns of IMs. A reliable condition monitoring method, however, can ensure their safe and uninterrupted operation. The acoustic signal analysis is one of the effective condition monitoring techniques used to identify incipient faults in IMs while Artificial Intelligence (AI) technology has been widely integrated with Machine Learning (ML) algorithms to automate the machinery condition monitoring process. This paper reviews application of acoustic signal analysis to detect impending failures of IMs. Moreover, time domain and frequency domain analysis techniques and features that can be derived from raw acoustic data are also discussed in detail. The paper also presents intelligent condition monitoring systems that are developed to improve fault diagnostic accuracy and recent developments in acoustic signal analysis based condition monitoring of IMs.

Item Type:	Refereed Conference Paper
Keywords:	acoustic signal analysis, condition monitoring, induction motor, machine learning
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Marine engineering
Objective Division:	Transport
Objective Group:	Water transport
Objective Field:	International sea freight transport (excl. live animals, food products and liquefied gas)
UTAS Author:	Rajapaksha, N (Mr Nipuna Rajapaksha)
UTAS Author:	Jayasinghe, S (Dr Shantha Jayasinghe Arachchillage)
UTAS Author:	Enshaei, H (Dr Hossein Enshaei)
UTAS Author:	Jayarathne, N (Dr Nirman Sembukutti Vidanelage)
ID Code:	148912
Year Published:	2021
Deposited By:	Seafaring and Maritime Operations
Deposited On:	2022-02-18
Last Modified:	2022-02-18
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