Rolling bearing is the critical component widely used in mechanical equipment and one of the main causes of equipment failure. The accurate extraction of its fault feature is crucial to the stable operation of the equipment. The initial faults of the bearings are pretty weak, and transient signals are easily masked by background noise, which makes the extraction of fault features more difficult, the characteristics of bearing fault features and noise are required to be accurately depicted. For the above problems, to deeply explore the low-rank and sparse characteristics of bearing fault features and noise in the time-frequency domain and their intrinsic correlation, the two representative methods in the framework of low-rank and sparse decomposition for bearing fault feature extraction are compared and studied in this paper, to make full use of the properties of fault features and noise components, which provides a certain basis for selecting bearing fault extraction methods under noise disturbance. To address the above problems, the matrix decomposition model is further developed by utilizing the sparse and low-rank characteristics of periodic transient signals. Two fault feature extraction techniques, Go-Decomposition (Go-Dec) and Non-negative matrix factorization (NMF), are compared in this paper. They are applied to fault features extracted from rolling bearings in the time-frequency domain. First, the time-frequency matrix of vibration signal is generated based on Short Time Fourier Transform (STFT), and the sparsity and low rank of fault impulses are revealed in the time-frequency domain. Then, two matrix decomposition methods, Go-Dec and NMF, are used to decompose the matrix characterizing the fault features. Finally, the transient signal is recovered by inverse short-time Fourier transform of the decomposed fault matrix. The envelope spectrum of the reconstructed time-domain signal is taken to determine the fault type and frequency information of the rolling bearing. The simulated analysis and experimental validation compare the two fault feature decomposition methods in which Go-Dec can better reduce the noise interference and extract the sparse components characterizing the rolling bearing fault features.
Key words
rolling bearing /
fault feature extraction /
Short Time Fourier Transform /
Go-Dec /
NMF
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