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Weak Bearing Fault Diagnosis with Optimized Morlet Wavelet and Kurtosis



To achieve the early fault diagnosis for rolling bearings, this paper proposes a new transient fault detection approach by the use of optimized Morlet wavelet transform, kurtosis index and soft-thresholding. Firstly, a fast optimization algorithm based on the Shannon entropy is developed to obtain the optimized Morlet wavelet parameter. Compared to the existing Morlet wavelet parameter optimization algorithm, this algorithm has lower computation complexity. After performing the optimized Morlet wavelet transform on the analyzed signal, the kurtosis index is used to select the characteristic scales and obtain the corresponding wavelet coefficients. From the time-frequency distribution of the periodic impulsive signal, it can be found that the transient signal should be reconstructed by the wavelet coefficients at several characteristic scales rather than the wavelet coefficients at just one characteristic scale, so as to improve the accuracy of transient detection. Due to the noise influence on the characteristic wavelet coefficients, the adaptive soft-thresholding method is applied to denoise these coefficients. With the denoised wavelet coefficients, the transient signal can be reconstructed. The proposed method has been applied to analyze a simulated signal with a weak impulse, and diagnose a rolling bearing fault. The superiority of the method over the fast kurtogram method has been verified by the results of simulation analysis and real experiments. It can be concluded that the proposed method is extremely suitable for extracting the periodic impulsive feature from the strong background noise.

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