AdaClass filter and its application in bearing fault diagnosis

Abstract

The vibration signals recorded by the sensor reflect the operating state of bearings, and extracting recognizable features effectively from them has become a hot issue in fault diagnosis. Currently, signal processing based filtering methods have emerged as a popular approach for extracting fault-related features. However, conventional filters based on specified assumptions and theoretical models have limited adaptability to multiple types of bearings under different operating conditions, which can significantly impact the diagnostic results. Given this, a data-driven Adaptive Class (AdaClass) filter is proposed to extract the response characteristics of different categories within the latent space. The filter details are obtained by statistically analyzing the mean vectors of samples for each class in the reconstructed feature subspaces. Notably, the latent feature space is mapped by linear operators linear discriminant analysis and class-wise principal component analysis, where the data has a more concise feature representation and a more distinct feature structure. The low-dimensional projection operations enhance the differential information among different categories, and reorganize the internal structure within the same category. Furthermore, a bearing fault diagnosis model is developed based on the AdaClass filter banks, utilizing one-step convolution to improve the efficiency of feature extraction. Experimental results show that the proposed method outperforms the competitors in terms of accuracy, time consumption, and noise resistance, especially for small sample scenarios.