An Optimized Modulation Signal Bispectrum for Bearing Fault Diagnosis

Yuandong Xu, Yunpeng Cao, Fengshou Gu, Andrew Ball

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


Rolling element bearings are widely used in rotating machines and play an important role in supporting the rotor and minimizing friction during the operation. The health conditions of bearings have a significant influence on the reliability and safety of rotating machines and hence condition monitoring of bearings is of great significance. The modulation signal bispectrum (MSB) is an outstanding approach for early fault detection and diagnosis of bearings. To further improve the performance of the MSB method, an optimized method is proposed to enhance the noise reduction capability for a more accurate fault diagnosis. The MSB matrixes are automatically selected to remove the improper estimation for the following ensemble averaging according to the amplitude and phase of the coupled components. Both simulation and experimental studies demonstrate the effectiveness of the proposed optimized MSB method for bearing.

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
Number of pages10
ISBN (Electronic)9783031261930
ISBN (Print)9783031261923, 9783031261954
Publication statusPublished - 4 Mar 2023
EventInternational Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China
Duration: 18 Aug 202221 Aug 2022

Publication series

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


ConferenceInternational Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated titleTEPEN 2022
Internet address


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