关键词: 滚动轴承, 变转速工况, GoDec分解, 阶频谱相关, 低秩稀疏

Abstract: Under the condition of variable rotational speed, the weak fault features of rolling bearings are easy to be flooded and affected by the strong noise, which leads to the difficulty of identifying the fault features. Based on this, a combination of Order Frequency Spectral Coherence (OFSC) domain and low-rank sparse optimisation Go-Decomposition (GoDec) is proposed as a fault feature extraction method. (Go-Decomposition, GoDec) for fault feature extraction. Firstly, the OFSC calculation of the fault signal is performed according to the cyclic smoothness property of the angular-time domain of the variable speed fault signal. Secondly, according to the sparsity and low-rank nature of the fault features and background noise in the order-frequency spectral correlation domain, the fault features in the order-frequency domain are extracted by introducing the kernel paradigm and L1 paradigm to optimise the GoDec method. Finally, in order to highlight the fault features, the sparse components are feature-enhanced using Enhanced Envelope Order Spectrum (EEOS). The performance of the proposed method is verified by variable speed simulation signals and experimental data, and quantitative analysis is carried out. The results show that the accuracy of fault feature recognition of the proposed method is better than that of single sparse constraint, original GoDec and order ratio analysis methods, which has obvious advantages.

Key words: rolling bearings, variable speed conditions, GoDec, order spectral correlation, low order sparse