A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD

Abstract Targeting that the measured vibration signal of roller bearing contains the characteristics of non-stationary and nonlinear, and the extraction features may contain smaller correlation and redundancy characteristics in the roller bearing fault diagnosis, the vibration signal processing method based upon improved ITD (intrinsic time-scale decomposition) and feature selection method based on Wrapper mode are put forward. In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. However, the parameters are fitted by using least squares in VPMCD method, while least squares regression is sensitive to “abnormal value”. Therefore, a robust regression-variable predictive mode-based class discriminate (RRVPMCD) method is proposed in this paper, robust regression is adopted to estimate parameters and the effect of “abnormal value” in the estimation of parameters would be reduced by giving each feature a weight. Firstly, improved ITD method and feature selection method based on Wrapper mode are combined to extract the fault features of roller bearing vibration signals, and feature vector matrixes are established, then a predictive model is built through the method of RRVPMCD, finally, the established predictive model is used for pattern recognition. Experimental results show that the model based on the improved ITD, the Wrapper feature selection and RRVPMCD method can effectively identify work status and fault type of roller bearing.

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