Research on feature extraction algorithm of rolling bearing fatigue evolution stage based on acoustic emission

This paper focuses on extracting effective evolution stage features of rolling bearing from the monitoring signal. Each feature has a different damage sensibility to different fatigue evolution stages. Fatigue evolution information is dispersed in different features, which increases the difficulty to recognize the fatigue stages. This paper presents a new feature extraction method for acoustic emission (AE) signal of rolling bearing to solve the problem, and a specially designed test rig is used for the experimental verification. The new method combines wavelet packet de-noising (WPD) with an improved kernel entropy component analysis (KECA). First, de-noising original signal by WPD method. Second, applying KECA method with Gaussian kernel function on the feature matrix extracted from the de-noised signal. A new particle swarm optimization method based on the best kernel entropy component number theory with inertia weight and dynamic accelerating constant (BWCPSO) is proposed to optimize the kernel parameter. BWCPSO method puts the minimized kernel entropy components number with the maximum stage information of rolling bearing as its objective. The optimal kernel parameter can make KECA method extract and converge the original signal information greatly. Finally, each fatigue evolution stage can be identified adaptively by the main kernel entropy score (KES) graphs. The experiment results show that the proposed method extracts the fatigue evolution stages information of rolling bearing effectively and much easier and more accuracy than the traditional feature trend analysis and other two traditional feature extraction methods.