Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

Journal of Vibroengineering

Cong Wang¹  Hongli Zhang¹  Ping Ma¹  Wenhui Fan² 

[1] College of Electrical Engineering, Xinjiang University, Urumqi, China;Department of Automation, Tsinghua University, Beijing, China;

关键词: bearing fault diagnosis;    features extraction;    dual-tree complex wavelet transform;    permutation entropy;    optimized FCM;   

DOI  :  10.21595/jve.2017.18278

来源: DOAJ

【 摘 要 】

In order to enhance the capability of feature extraction and fault classification of bearings, this study proposes a feature extraction approach based on dual-tree complex wavelet transform (DTCWT) and permutation entropy (PE), using the fuzzy c means clustering (FCM) to identify fault types. The vibration signal of bearings can be decomposed into several wavelet components with DTCWT which can describe the local characteristics of vibration signals accurately. And the PE of each wavelet component, which can describe the complexity of a time series, is calculated to be regarded as the fault features. Then forming the standard clustering centers by the FCM, we defined a standard using the Hamming approach degree to evaluate the classification results in the FCM. In order to verify the effectiveness of the proposed approach, compared with two other typical signal analysis methods: ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), through extracting fault features, it required to identify the fault types and severities under variable operating conditions. The experimental results demonstrate that the proposed approach has a better accuracy and performance to diagnose a bearing fault under different fault severities and variable operating conditions. The proposed approach is suitable for a fault diagnosis due to its good ability to the feature extraction and fault classification.

【 授权许可】

Unknown