Rotating machinery fault diagnosis based on multivariate multiscale fuzzy distribution entropy and Fisher score

Abstract

With evaluating the randomness and the nonlinear dynamic change of time sequence effectively, multiscale fuzzy distribution entropy (MFDE) is proposed to extract fault features from vibration signal. However, it is unsuitable for multivariate signals, which contain more abundant fault information. Through changing the coarse-grained and calculation process of MFDE, a novel entropy named as multivariate multiscale fuzzy distribution entropy (MMFDE) is proposed, which not only has the characteristics of MFDE, but also considers the within- and cross-channel correlations. By applying MMFDE to two kinds of simulation signals, the results show the proposed entropy is stable and reliable. A novel fault diagnosis method for rotating machinery is proposed by extracting fault features with MMFDE, selecting sensitive ones with Fisher score (FS), and identifying working state with support vector machine (SVM). Two experiments show the better diagnosis performance of the proposed rotating machinery fault diagnosis method than the related methods with the accuracy of 98.95% and 96.80% respectively.