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Fault diagnosis of idler bearings based on MFCC-IMFCC hybrid cepstral coefficients

Published:2024-08-02 author:TAO Hanyu, CHEN Huanguo, PENG Chengcheng, et al. Browse: 709 Check PDF documents

Fault diagnosis of idler bearings based on MFCC-IMFCC hybrid cepstral coefficients

TAO Hanyu1, CHEN Huanguo1, PENG Chengcheng2, GAO Xiangchong1, YANG Lei1

(1.Zhejiang Provinces Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University,

Hangzhou 310018, China;

2.Technical Department, Hangzhou Lingwei Information Technology Co.,Ltd., Hangzhou 311215, China)

Abstract: Addressing the insufficient capability of Mel-frequency cepstral coefficient (MFCC) in extracting high-frequency features of idler bearing faults, a novel fault diagnosis method for idler bearings based on Mel-frequency cepstral coefficient and inverse-Mel-frequency cepstral coefficient (MFCC-IMFCC) hybrid cepstral coefficients and long short-term memory (LSTM) networks was proposed. Firstly, the acoustic signals of idler under three states were analyzed, revealing that the bearing fault information mainly resided in the mid-to-high-frequency range. Then, to effectively retain high-frequency information, MFCC-IMFCC were extracted and combined in a frame-level concatenation to form hybrid cepstral features. Finally, the hybrid cepstral features were input into a two-layer LSTM model for training, establishing a diagnostic model for idler bearing faults. The research results indicate that, for normal state, rolling element fault state, and eccentric rotation fault state, the average recognition accuracy of LSTM combined with hybrid cepstral features reaches 96.72%. Comparing to using individual MFCC and IMFCC features, the accuracy is improved by 3.94% and 7.41%, highlighting the significant advantage of hybrid cepstral features in representing information about idler bearing faults.

Key words: idler bearings; bearing fault acoustic signal; high frequency information; Mel-frequency cepstral coefficient (MFCC); inverse-Mel-frequency cepstral coefficient (IMFCC); hybrid cepstral coefficients; long short-term memory (LSTM) networks

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