Sparse representation based on spectral kurtosis for incipient bearing fault diagnosis

The bearing fault, generating harmful vibrations, is one of the main causes of machine breakdowns. Therefore, performing bearing fault diagnosis is a key point to improve the reliability of the mechanical systems and reduce the corresponding operating expense. Recently, more and more studies focus on addressing this problem through detection transient signal by means of sparse representation (SR) theory. Although tremendous progress has been made, one important drawback remains to be solved: in the early stage of bearing failure, the incipient impact signal is relatively weak, which is hard to detect due to other mechanical components harmonic signals and interference of noise. In order to solve this problem, spectral kurtosis (SK), a popular tool to detect non-stationary signal, is introduced as a pre-procedure of the transient signal sparse representation. A novel sparse representation based on spectral kurtosis method is proposed in this work, namely the SKSR. SKSR utilizes the advantages of both of the methods: it is possible to choose the best matching of the atomic signal with the failure signal structure feature to gain sparse representation and efficiently extract transient signal from strong noise. The effectiveness of the proposed method is verified by the numerically simulations and lab experiments. The results show that the presented method is efficient for the title problem.