基于低秩和稀疏分解的滚动轴承故障特征提取方法对比研究

Rolling bearings are widely used key components in mechanical equipment, and accurate extraction of their fault features is crucial for stable operation of equipment. Initial fault of bearing is very weak and easily covered by background noise to make it be difficult to extract its fault features. It is necessary to accurately characterize characteristics of bearing fault features and noise. Here, aiming at the above problems mentioned, low rank and sparse characteristics of bearing fault features and noise in time-frequency domain as well as their inherent correlations were studied, contrastive study on 2 representative methods for bearing fault feature extraction was conducted under the framework of low rank and sparse decomposition to fully utilize properties of fault features and noise components, and provide a certain basis for selecting bearing fault extraction methods under noise interference. Matrix factorization model was established using sparse and low rank characteristics of periodic transient impact signals in time-frequency domain. 2 representative decomposition methods, go-decomposition (Go-Dec) and non-negative matrix factorization (NMF) were compared and applied in fault feature extraction of rolling bearing in time-frequency domain. Firstly, time-frequency matrix of vibration signals was generated based on short time Fourier transform (STFT), and sparsity and low rank of bearing fault pulses in time-frequency domain were revealed. Then, Go-Dec and NMF were used to decompose the matrix characterizing fault features. Finally, inverse STFT was performed for the decomposed matrices to reconstruct transient pulse signal, and the envelope spectrum of transient pulse signal was used to determine fault type and frequency information of rolling bearing. The two fault feature decomposition methods were compared using simulation analysis and experiments. The results showed that Go-Dec can better remove noise interference, and effectively extract sparse components characterizing fault features of rolling bearing.