Research on Rotor Fault Identification Method Based on Multi-sensor AE Signal Fusion and SVD Orthogonal Mode Frequency Domain Fractal

Abstract

This study addresses the dynamic identification of typical faults, such as rotor cracks and reversed installation of blades, in automotive power steering pumps by proposing a method for fault feature extraction and classification using multi-sensor acoustic emission (AE) signals. Initially, two AE sensors are positioned near the pump's inlet port and return port to synchronously capture AE signal time-domain waveforms under various working conditions, using a sampling frequency of 1 MHz. AE sub-signals for a single period are then extracted. After whitening, signals from the four sensors were arranged as column vectors to form a matrix. The singular value decomposition (SVD) was applied to extract four orthogonal mode vectors, which were then decomposed via three-layer wavelet packet decomposition (WPD). The frequency-domain fractal dimension (FFD) of the first four nodes is calculated. The random forest was used to assess feature importance and select the top six parameters. These features were fed into a support vector machine (SVM), and the performance of different kernel functions was compared. Experiments were conducted under pump body pressures of 4 MPa, 7.5 MPa, and 13 MPa, with a rotational speed of 1000 r/min. Results showed minimal differences among kernel functions, though radial basis function (RBF) performed best for the normal state. By incorporating SVD for multi-sensor information fusion and feature extraction, the classification accuracy of rotor states including normal, cracked, and reversed blade installation is improved from 87.77% to 97.55%, demonstrating the method's effectiveness and practicality.