Intelligent Rub-Impact Fault Diagnosis Based on Genetic Algorithm-Based IMF Selection in Ensemble Empirical Mode Decomposition and Diverse Features Models

Abstract

Rub-impact faults condition monitoring is a challenging problem due to the complexity in vibration signal of rub-impact faults. These complexities make it hard to use traditional time- and frequency-domain analysis. Recently, various time-frequency analysis approaches namely empirical mode decomposition (EMD) and ensemble EMD (EEMD) have been used for rubbing fault diagnosis. However, traditional EMD suffers from “mode-mixing” problems that cause difficulty to find physically meaningful intrinsic mode functions (IMF) for feature extraction. We propose an intelligent rub-impact fault diagnosis scheme using a genetic algorithm (GA)-based meaningful IMF selection technique for EEMD and diverse features extraction models. First, the acquired signal is adaptively decomposed into a series of IMFs by EEMD that correspond to different frequency bands of the original signal. Then, a GA search using a new fitness function, which combines the mean-peak ratio (MPR) of rub impact and mutual information (MI)-based similarity measure, is applied to select the meaningful IMF components. The designed fitness function ensures the selection of discriminative IMFs which carry the explicit information about rubbing faults. Those selected IMFs are utilized for extracting fault features, which are further employed with k-nearest neighbor (k-NN) classifier for fault diagnosis. The obtained results show that the proposed methodology efficiently selects discriminant signal-dominant IMFs, and the presented diverse feature models achieve high classification accuracy for rub-impact faults diagnosis.