Sparse Bayesian Learning Approach for Broken Rotor Bar Fault Diagnosis
Ming Ma, Zheng Cao, Haijun Fu, Weichao Xu, Jisheng Dai
Abstract
This paper addresses the issue of broken rotor bar (BRB) fault detection for induction motors (IMs). The widely used fast Fourier transform (FFT) based methods are sensitive to noise and have a low resolution for short-time data; while subspace-type methods suffer from substantial performance degradation due to inappropriate model order choice and spatial smoothing operation. To effectively identify the weak fault sidebands from the rotor current signal, we cast the fault sideband identification problem as a sparse representation (SR) problem, and present a novel sparse Bayesian learning (SBL) approach for the BRB fault diagnosis. The novelties of the proposed approach are three-fold: (i) a three-stage hierarchical sparse prior is combined into the SBL framework to greatly enforce the sparsity of the current spectrum, leading to heavily sharpened fault sidebands; (ii) a new computationally efficient SR model with a truncated spectral grid is introduced to significantly reduce the computational load for Bayesian inference; and (iii) the symmetric pairing structure of fault sidebands is additionally exploited as a priori knowledge to enhance the robustness of the BRB identification performance. Both simulation and experiment results indicate the superiority of the proposed method, especially for the short sampling time and/or high noise level current signal.