Bearings fault diagnosis based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso

Abstract

Bearing fault diagnosis attracts great attention because the bearing condition has direct effects on productivity and safety in industry. To accurately identify the operating condition of bearings, a novel bearing fault diagnosis method based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso is first put forward in this article. In the proposed method, adaptive local iterative filtering was applied to decompose the nonlinear and non-stationary vibration signals into intrinsic mode functions. The multiscale permutation entropy values of the first several intrinsic mode functions were calculated to characterize the complexity of intrinsic mode functions in different scales, and they constructed feature vectors after normalization. Multinomial logistic model with group-lasso could perform multiple classifications with an embedded approach for feature selection, which is distinct from the traditional methods with two steps of dimensionality reduction and classification. Finally, the proposed method was verified with experiment data from Case Western Reserve University considering four conditions: different fault types, different damages, multiple types, and different loads. The results indicate that the proposed method is effective in identifying different categories of rolling bearings.