Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification

Abstract

Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM). The parallel-factor multi-scale analysis theory was studied to reconstruct tensor feature information based on a three-dimensional matrix for time, frequency, and spatial vectors. A multi-scale wavelet analysis was used to transform the original multi-channel experimental data acquired from a gearbox into a three-dimensional feature matrix of the multi-level structure. The optimal correspondence among the two-dimensional feature signals in the frequency and time domains for the different fault modes was established by the PARAFAC theory. An intelligent APSO algorithm was developed to obtain the optimal parameter structures of an SVM classifier. A comparison with the existing time–frequency analysis method showed that the proposed hybrid PARAFAC-PSO-SVM diagnosis model effectively eliminated the redundant information in the multi-dimensional tensor features but retained the important components. The PARAFAC-APSO-SVM hybrid diagnostic model achieved fast, accurate, and simple fault-classification and identification results, and could provide theoretical support for the application of the PARAFAC theory to complex mechanical fault diagnosis.