Blind source extraction of rolling bearings' multi-type faults based on self-learned sparse atomics

Abstract

The feature of rolling element bearings' multi-type faults is very hard to extract using common feature extraction method such as envelope demodulation, and the main reason is that there exists mutual coupling effect when multi-type faults arise in rolling element bearing synchronously. Blind source extraction originating from blind source separation is an effective method for feature extraction of rolling bearings' multi-type faults. However, the extraction result would not be ideal if blind source extraction is used directly due to the above stated mutual coupling effect. Sparse representation is a relative new signal processing method, which could capture the latent fault feature components buried in the vibration signal. So, blind source extraction of rolling element bearings' multi-type faults based on sparse representation is proposed in the paper. Firstly, the self-learned sparse atomics originating from sparse representation is applied to the multi-type faults vibration signals directly and several learned atomics are obtained. Then, the multi-type faults vibration signals are reconstructed based on the obtained learned atomics and sparse multi-type faults vibration signals are obtained. Thirdly, the blind source extraction method is applied to the reconstructed sparse vibration signals. Lastly, envelope demodulation is applied to the blind source extraction results respectively and satisfactory fault feature extraction results are obtained. The feasibility and effectiveness of the proposed method are verified through simulation and experiment.

Keywords

Blind source extraction rolling element bearing multi-type faults sparse representation self-learned sparse atomics

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