关键词: 风电机组滚动轴承, 故障诊断, 组稀疏, 图正则化

Abstract: Due to effects of the non-stationary operations, noisy working environment and strong electromagnetic interference for the wind turbines, the fault impulses of rolling bearings were submerged by strong noise, and the weak features were difficult to accurately identify. To solve the above problems, a frequency domain group sparse model with graph regularization constraints was proposed, which might effectively extract fault features of rolling bearings without periodic prior knowledge. Firstly, vibration signals were converted into graph signals to construct the graph regularization constraints, and the structured information was utilized to guide the penalty strength to improve the accuracy of sparse reconstruction. Secondly, the frequency domain group sparse model with graph regularization constraints was constructed, the method was given to determine the shrinkage threshold of the in-group components, and the objective function was simplified with the proximal mapping to optimize the solution. Finally, the parameters of the model were optimized by using the constructed comprehensive index and the moth flame optimization algorithm, and rolling bearing faults were identified by the envelope spectrum analysis of the reconstructed signals in the time domain. Numerical simulation and experimental results demonstrate that the proposed method has good anti-noise performance and may effectively extract weak fault features of rolling bearings under strong noise interference.

Key words:  , rolling bearing of wind turbine, fault diagnosis, group sparse, graph regularization