基于Wavelet leader和优化的等距映射算法的回转支承自适应特征提取

Multi-fractal adaptive feature extraction method based on Wavelet leader method and isometric mapping method optimized by hybrid grey wolf optimization algorithm (HGWO-ISOMAP) was proposed, in order to solve the problem that the vibration signal of slewing bearing is weak and the feature information is difficult to extract. Wavelet leader is utilized to calculate the multi-fractal features, mine the geometric structure information of vibration data, and construct a high-dimensional multi-fractal feature matrix. Adaptive feature selection of high-dimensional feature matrix is carried out through ISOMAP method optimized by HGWO. The selected feature matrix is input into the least squares support vector machine (LSSVM) optimized by genetic algorithm (GA) for fault state identification. A full life experiment of a certain type of slewing bearing was carried out by using self-developed comprehensive performance test platform of slewing bearing, in order to verify the superiority of the proposed method. Results show that compared with general time domain, time-frequency domain and frequency domain feature extraction methods, the proposed method can improve the recognition accuracy and reduce the calculation time, providing a new effective way for feature extraction of slewing bearing.