Open Access Repository

Acoustic analysis based condition monitoring of induction motors: A review

Rajapaksha, N, Jayasinghe, S ORCID: 0000-0002-3304-9455, Enshaei, H ORCID: 0000-0002-5649-7015 and Jayarathne, N 2021 , 'Acoustic analysis based condition monitoring of induction motors: A review', in Proceedings of the 2021 IEEE Annual Southern Power Electronics Conference (SPEC) , Institute of Electrical and Electronics Engineers, United States, pp. 1-10 , doi: https://doi.org/10.1109/SPEC52827.2021.9709467.

Full text not available from this repository.

Abstract

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: Conference Publication
Authors/Creators:Rajapaksha, N and Jayasinghe, S and Enshaei, H and Jayarathne, N
Keywords: acoustic signal analysis, condition monitoring, induction motor, machine learning
Journal or Publication Title: Proceedings of the 2021 IEEE Annual Southern Power Electronics Conference (SPEC)
Publisher: Institute of Electrical and Electronics Engineers
DOI / ID Number: https://doi.org/10.1109/SPEC52827.2021.9709467
Copyright Information:

Copyright 2021 IEEE

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP