Investigation into LSTM Deep Learning for Induction Motor Fault Diagnosis

Xiaoyu Zhao, Ibrahim Alqatawneh, Mark Lane, Haiyang Li, Yongrui Qin, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As motor faults could lead to unwanted loss in industry, it is important to find out the motor faults in time. Currently, with the popularity and mature application of deep learning, researchers in the field of electrical machine health assessment have begun to focus on deep learning methods. It is hoped that motor fault detection can be achieved with the help of deep learning methods. This paper presents to adopt deep learning methods represented by LSTM neural network for motor fault diagnosis and evaluates on our own experimental platform. Considering two typical motor faults with two different degrees of severity, the results show that the proposed LSTM approach has a high accuracy (98.81%) on motor fault classification. The results also confirm that: (1) adequate effort of preprocessing, including sample length selection in the time domain and frequency band selection in the frequency domain, can significantly improve accuracy and computational efficiency; (2) different faults can be separated through the information in frequency band of 100–1000 Hz, which has not been fully modelled analytically before.

Original languageEnglish
Title of host publicationProceedings of IncoME-VI and TEPEN 2021
Subtitle of host publicationPerformance Engineering and Maintenance Engineering
EditorsHao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
PublisherSpringer, Cham
Pages505-518
Number of pages14
Volume117
ISBN (Electronic)9783030990756
ISBN (Print)9783030990749
DOIs
Publication statusPublished - 18 Sep 2022
Event6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China
Duration: 20 Oct 202123 Oct 2021
Conference number: 6
https://tepen.net/conference/tepen2021/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume117
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

Conference6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated titleTEPEN-2021 and IncoME-VI
Country/TerritoryChina
CityTianjin
Period20/10/2123/10/21
Internet address

Fingerprint

Dive into the research topics of 'Investigation into LSTM Deep Learning for Induction Motor Fault Diagnosis'. Together they form a unique fingerprint.

Cite this