An intelligent fault diagnosis method for bogie bearings of train based on wavelet packet decomposition and EEMD

作者：Liu Jian Qiang; Zhao Zhi Bo; Ren Gang; Wu Ning; Wang Guang Ming; Zhang Guo Ping

来源：Journal of the China Railway Society, 2015, 37(7): 40-45.

DOI：10.3969/j.issn.1001-8361.2015.07.007

• Bearing fault diagnosis
• Ensemble empirical mode decompositions (EEMD)
• Envelope analysis
• Fault characteristics
• Fault feature extractions
• Intelligent fault diagnosis
• Wavelet Packet Analysis
• Wavelet Packet Decomposition

Existing diagnosis methods for train bogie bearings have several shortcomings including unsatisfactory fault feature extraction and low diagnosis rate. To address these deficiencies, this paper presented an intelligent fault diagnosis method for train bogie bearings. Combining the wavelet packet analysis and EEMD, this method fully extracted the fault characteristics from the signal, and identified the fault modes with the search algorithm for fault identification and the energy criterion, thereby further improving the diagnosis efficiency. To verify the validity of the proposed strategy, a test platform for bearing fault diagnosis was built, where bogie bearings under three fault conditions, which were used in Guangzhou Metro, were tested and analyzed. The experimental results showed that the proposed fault diagnosis strategy for bogie bearings improved the rate and accuracy of train bogie bearing fault diagnosis by fully extracting fault features, quickly locating the search band, and accurately identifying bearing faults.

• 出版日期2015
• 单位北京交通大学