Sparse representation learning for fault feature extraction and diagnosis of rotating machinery

Early fault feature extraction and fault diagnosis are of great importance for predictive maintenance of rotating machinery. To accurately extract early fault features from original noisy signals, a novel joint sparse representation learning method is developed in this paper, this method is based on the proposed nonlocal generalized minimax-concave (GMC) penalty and generalized fraction-order total variation (FTV) regularization. The motivation for this research is to leverage the benefits of joint regularizations. The proposed nonlocal GMC penalty regularization tends to preserve weak fault features, promote sparsity and avoid underestimating the amplitude of periodic fault impulses. Simultaneously, the proposed generalized FTV regularization tends to remove fault irrelevant noise and reduce staircase artifacts. Therefore, the proposed model can effectively extract early fault features from original noisy signals. The performance of the proposed model is verified by a series of experiments. In two fault diagnosis tasks, the peak signal-to-noise ratio (PSNR) of the proposed method reaches - 5 dB and - 8 dB, respectively. Compared with state-of-the-art methods, the PSNR has been improved by at least 2 dB, comparison results show that the proposed model has superior performance for early fault feature extraction.