Bearing fault detection based on improved NPE method

Bearings are key components in rotating machinery and are widely used in various industrial fields. Their operating conditions are related to the normal operation of the entire equipment or the entire production line. In order to quickly and accurately detect the occurrence of bearing faults, this paper studies the signal processing and fault feature extraction issues involved in rolling bearing fault detection based on the analysis of the bearing fault mechanism, and proposes a bearing fault detection method based on the improved neighborhood preserving embedding algorithm (NPE). Starting from solving the problems of redundancy of vibration signal data and strong correlation between data, this method optimizes the spatial structure of the high-dimensional vibration data manifold, realizes the simplification of high-dimensional vibration data, facilitates the fault detection process, and improves the accuracy of fault detection. Experimental results show that the fault detection accuracy of this method reaches 99.1%, which further proves the superiority of this method.