Bearing Compound Fault Diagnosis Based on Double-Domain Reweighted Adaptive Sparse Representation

Abstract

Compound faults easily happen in rolling bearing due to the complex working environment. Diagnosing compound faults accurately is a thorny problem, which can ensure the normal operation of mechanical structure. To tackle this problem, this paper proposes a novel method called double-domain reweighted adaptive sparse representation. The proposed method reweights the weight through the fault information from the wavelet domain and time domain. In the wavelet domain, periodic clustering similarity is proposed to measure fault features by similarity calculation. Then, wavelet domain information is transformed into time domain. In time domain, the Hadamard product of the sub-band signals is calculated to obtain fault features. The fault features in time domain are further transferred into wavelet domain, which is used for reweighting the weight. The proposed model’s optimal parameters are determined through the hippopotamus optimization algorithm, with the proposed frequency correlated kurtosis as the objective function. Frequency correlated kurtosis measures the signal’s frequency domain periodicity. The proposed method is verified by simulation, experimental signals, and comparison experiments, whose results show the effectiveness and superiority of the proposed method.

Keywords

• Compound fault diagnosis
• double-domain
• adaptive sparse representation