Hits:

Date of Publication:2022-10-10

Journal:振动 测试与诊断

Issue:2

Page Number:162-166

ISSN No.:1004-6801

Abstract:Fault diagnosis of rolling element bearing is important to improve the performance and the reliability of mechanical systems. The extraction of feature parameters is essential to diagnose faults. The sample entropy was introduced into the field of fault diagnosis. Its performance and the choice of calculation parameters were discussed. Combined with wavelet packet decomposition and sample entropy, a feature extraction method for rolling element bearing faults was proposed. Firstly, the bearing vibration signal was processed with wavelet packet decomposition. Then, the sub-band with largest normalized energy was reconstructed. Finally, the sample entropy of the reconstructed signal was calculated and used to evaluate the fault condition. The practical application proves that the method is effective on fault diagnosis of rolling element bearing.

Note:新增回溯数据