Group sparse low-rank matrix estimation for variable speed rolling bearing fault feature extraction
WANG Ran1,ZHANG Junwu1,YU Liang2
1.School of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China;
2.State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
Abstract:The effective extraction of early bearing failure features is of great importance to avoid serious mechanical accidents. The impulse signals characterizing bearing faults are often submerged in strong background noise interference, and the bearings often operate under variable speed conditions, which makes the task of fault feature extraction more difficult. To address this issue, one kind of group sparse low-rank matrix estimation algorithm for rolling bearing fault feature extraction under variable speed conditions is proposed in this paper. Firstly, the measured signal is transformed into the order-frequency domain by using order-frequency spectral correlation (OFSC) according to the angle\time cyclostationarity of the bearing fault pulse signal under variable speed conditions. Secondly, the group sparsity and low-rank property of the bearing fault pulse in the order-frequency domain are revealed, and a convex optimization problem is constructed to enhance these two properties accordingly, and a nonconvex penalty function is introduced to improve the sparsity of the fault characteristics. Again, the convex optimization problem is solved in the framework of the alternating direction method of multipliers (ADMM) and optimization-minimization (MM),and the group sparse low-rank (GSLR) matrix estimation algorithm is derived. Finally, the target components obtained from the solution are detected by constructing the enhanced envelope order spectrum (EEOS) for the fault features. The analysis of simulation and experimental signals verify the effectiveness of the method in fault feature extraction.
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