Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings

• Published in: Shock and vibration 2018-01, Vol.2018 (2018), p.1-12
• Main Authors: Peng, Yiqiang, Xu, Yanhai, Zhang, Weihua, Xiong, Qing, Deng, Pengyi
• Format: Article
• Language: English
• Subjects: Accelerometers; Bandpass filters; Bearings; Civil engineering; Crack propagation; Efficiency; Failure analysis; Fatigue failure; Fatigue life; Fault detection; Fault diagnosis; Fault location; Inspection; Maintenance; Parameter sensitivity; Particle swarm optimization; Shafts (machine elements); Signal processing; Spectrum analysis; Treads; Variation; Vehicles; Vibration; Vibration monitoring; Wheelsets; Workflow
• ISSN: 1070-9622; 1875-9203
• DOI: 10.1155/2018/1737219

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off-line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band-pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.