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The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults. [ABSTRACT FROM AUTHOR]

Feature extraction plays an important role in fault diagnosis. It is critical to extract the representative features for improving the classification performance. An intelligent fault diagnosis method based on Marginal Fisher analysis (MFA) is put forward and applied to rolling bearings. The high-dimensional features in time-domain, frequency-domain and wavelet-domain are extracted from the raw vibration signals to obtain rich faulty information. Subsequently, MFA excavates the underlying low-dimensional fault characteristics embedded in the high-dimensional feature space by preserving local manifold structure. Thus, the optimal low-dimensional features are obtained to characterize the various fault conditions of rolling bearings and finally fed into the simplest k-nearest neighbor classifier to recognize different fault categories. The diagnosis results validate the feasibility and effectiveness of the proposed fault diagnosis method, compared with the other three similar approaches. [ABSTRACT FROM AUTHOR]

In vibration analysis, fault feature extraction from strong background noises is of great importance. Frequency band entropy based on short-time Fourier transform illustrates the complexity of every frequency component in the frequency domain, and it can be used to detect the periodical components hidden in the signal. This article shows how the frequency band entropy offers a robust way in detecting faults even when the signal is under strong masking noises. Furthermore, frequency band entropy provides a way of blindly designing optimal band-pass filters. The filtering signal combined with envelope analysis is helpful in fault diagnosis. The effectiveness of the proposed method is demonstrated on both simulated and actual data from rolling bearings. [ABSTRACT FROM AUTHOR]