Bearings fault detection using wavelet transform and generalized Gaussian density modeling

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In the bearings fault detection applications, as the marginal distribution of wavelet transform output is a heavy-tailed bell-shaped function characterized by a larger portion of small wavelet coefficients or even zeros, the traditional feature extraction approaches such as wavelet-energy spectrum and energy spectrum entropy fail to accurately express the statistical feature of wavelet sub bands. In this paper, we propose a novel wavelet-based bearings fault detection approach using wavelet transform and Generalized Gaussian Density (GGD) modeling. A GGD-based feature descriptor is generated from concatenating the statistical parameters of each wavelet sub band estimated by the maximum likelihood method. According to the descriptor information, a class label for the bearing fault detection is assigned by the subsequent classifier. The extensive experimental results show that the proposed approach can more accurately and flexibly capture wavelet sub band information of bearings vibration signals than energy, energy entropy, Gaussian and Laplace based feature description methods. Moreover, the experimental results with different wavelet filters, decomposition levels, and classifiers demonstrate that the new method significantly improves the fault detection accuracy compared with traditional approaches with better robustness.