Stator Resistance Imbalance Diagnosis Using Teager-Kaiser Energy Operator Based on Motor Current Signature Analysis

Haiyang Li, Huaqing Wang, Funso Otuyemi, Dong Zhen, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stator resistance imbalance fault is one of the most common faults happens in induction motors (IMs). Effective stator resistance imbalance detection plays significant roles in reducing maintenance costs and unscheduled downtimes. Stator resistance imbalance faults information can be detected through analysing the motor supply current signals. Various motor current signal processing methods have been investigated to extract the weak diagnostic feature from noisy current signals based on advanced signal processing analysis. In this paper, Teager-Kaiser energy operator (TKEO) is proposed to identify fault signatures based on motor current signal analysis (MCSA). TKEO is able to increase the signal-to-noise ratio and has the ability of demodulating FM as well AM signal for fault feature extraction. Experimental analysis results show that TKEO can successfully identify and extract the fault characteristic frequency for the condition monitoring and fault diagnosis of IMs operating under different operations based on MCSA.

Original languageEnglish
Title of host publicationAdvances in Asset Management and Condition Monitoring, COMADEM 2019
EditorsAndrew Ball, Len Gelman, B.K.N. Rao
PublisherSpringer, Cham
Pages953-962
Number of pages10
Volume166
ISBN (Electronic)9783030577452
ISBN (Print)9783030577445
DOIs
Publication statusPublished - 28 Aug 2020
Event32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference - University of Huddersfield, Huddersfield, United Kingdom
Duration: 3 Sep 20195 Sep 2019
Conference number: 32
http://www.comadem2019.com/ (Link to Conference Website)

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume166
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference32nd International Congress and Exhibition on Conditioning Monitoring and Diagnostic Engineering Management Conference
Abbreviated titleCOMADEM 2019
CountryUnited Kingdom
CityHuddersfield
Period3/09/195/09/19
Internet address

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