Method of fault feature selection and fusion based on poll mode and optimized weighted KPCA for bearings

Abstract

The health monitoring system for equipment is essential in the smooth proceeding of industrial production. However, the fault features to be detected in monitoring systems are generally selected through projects and expertise, which are not capable for complex and ever-changing fault information and may result in incomplete correspondence to the fault types emerged. To dig deeper for the effective features hidden in the data instead of selecting by experience, a feature selection and fusion method based on poll mode and optimized Weighted Kernel Principal Component Analysis (WKPCA) method is then proposed. Specifically, inspired by poll-mode and multi-criteria strategy, a multi-measure hierarchical model is designed to sort the fault features with high sensitivity, acquiring the feature subset with corresponding weight coefficient. Considering the variation in fault information collected by different sensors, the diagnosis rate in Extreme Learning Machine (ELM) is taken as the index for evaluation of each single sensor, then the sensitivity weight matrix of features extracted by multiple sensors is constructed after linear normalization. To integrate the feature information, WKPCA is applied for the weighted fusion of features, and Quantum Genetic Algorithm (QGA) is used to search for the kernel width parameter when the best separability in the samples under the fusion is reached. Finally, such samples are introduced to drive the diagnostic model of the monitoring system in rolling bearing. The experimental results show that, compared with the traditional feature selection and fusion methods, this method is capable for sorting out highly sensitive features with more fault information self-adaptively, and can improve the separability in the subset of fault samples effectively.