Adaptive Sparse Representation-Based Minimum Entropy Deconvolution for Bearing Fault Detection

Abstract

Deconvolution methods have been extensively applied for enhancing the impulsive feature of those bearing signals, which promotes the accuracy of the bearing fault diagnosis. However, these methods are sensitive to the invers filters length, which leads to poor effect. To overcome the existing problems, a new adaptive sparse representation-based minimum entropy deconvolution (AdaSRMED) is proposed on basis of the impulsive feature extraction of sparse representation, which promotes the robustness for inverse filter length and its effectiveness of impulse enhancement. In AdaSRMED, a new adaptive sparse representation (SR) is proposed based on the features of fault impulsive signals for solving these existing problems of previous SR methods. Moreover, the new adaptive SR is integrated with the minimum entropy deconvolution (MED) for improving the performance of MED, which avoids the shortcomings of MED and MED-related methods. A series of simulation signal and real fault signal experiments are performed to illustrate the superiority of AdaSRMED in fault detection. The comparison analysis with the conventional MED and improved MEDs shows that AdaSRMED can effectively enhance the impulsive features and show good bearing fault detection performance.