- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | WANG Shengbo, JIANG Xiaomo, CHEN Bingyan, CHENG Yao, MEI Guiming. Axle-Box Bearing Fault Diagnosis of Railway Vehicle Based on Enhanced Time-Varying Morphological Filtering[J].Journal of Southwest Jiaotong University.doi:10.3969/j.issn.0258-2724.20240297
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Morphological filtering (MF) is an effective method for bearing fault diagnosis with the capacity of recovering transient impulse features from noisy vibration signals, in which the choice of shape and length of structural element has an important impact on MF performance. To solve this problem, an enhanced time-varying structural element (ETVSE) based on median filtering was proposed to more accurately match and extract periodic transient features hidden in noisy signals. Moreover, the power spectrum (i.e., the frequency spectrum of autocorrelation signal) was applied to the filtered signal to further enhance fault-related components and eliminate broadband noise pollution. Finally, a bearing fault diagnosis method called enhanced time-varying morphological filtering (ETVMF) was developed, which combined the advantages of ETVSE and power spectrum. The analysis results of simulated data and measured data of two railway axle-box bearing test rigs show that, compared with the compared method, ETVMF demonstrates superior fault feature extraction performance and can accurately identify bearing inner race, outer race, and rolling element faults under complex noise interference, while obtaining higher performance quantification index and lower calculation cost.
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