Blind Deconvolution Based on Modified Smoothness Index for Railway Axle Bearing Fault Diagnosis

Bingyan Chen, Fengshou Gu, Weihua Zhang, Mengying Tan, Yaping Luo, Zuolu Wang, Zewen Zhou

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

Abstract

Blind deconvolution is a widely used technique for fault diagnosis of rolling bearings. Traditional blind deconvolution methods, such as minimum entropy deconvolution, are susceptible to random transients, making it difficult to extract fault features of railway train axle bearings under strong external shock conditions. Deconvolution methods that take the fault characteristic frequency of interest as an input parameter, such as maximum second-order cyclostationarity blind deconvolution, can alleviate this deficiency, however, the bearing fault features are difficult to be extracted when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index of the squared envelope and the modified smoothness index of the squared envelope spectrum are proposed as objective functions of the deconvolution algorithms, allowing two new blind deconvolution methods to be developed for railway axle bearing faults diagnosis. The two proposed blind deconvolution methods are robust to random transients and do not require the characteristic frequency of interest as an input parameter. The fault diagnosis performance of the two proposed methods is verified using the experimental data of actual railway axle bearings and compared with the state-of-the-art deconvolution methods. The results show that the two proposed blind deconvolution methods can adaptively extract repetitive transient features from noisy vibration signals and effectively diagnose different faults of railway axle bearings.

Original languageEnglish
Title of host publicationProceedings of TEPEN 2022
Subtitle of host publicationEfficiency and Performance Engineering Network
EditorsHao Zhang, Yongjian Ji, Tongtong Liu, Xiuquan Sun, Andrew David Ball
PublisherSpringer, Cham
Pages447-457
Number of pages11
Volume129
ISBN (Electronic)9783031261930
ISBN (Print)9783031261923, 9783031261954
DOIs
Publication statusPublished - 4 Mar 2023
EventInternational Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China
Duration: 18 Aug 202221 Aug 2022
https://tepen.net/
https://tepen.net/conference/tepen2022/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume129 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

ConferenceInternational Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated titleTEPEN 2022
Country/TerritoryChina
CityBaotou
Period18/08/2221/08/22
Internet address

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