Novel data-driven short-frequency mutual information entropy threshold filtering and its application to bearing fault diagnosis

A rotating mechanical vibration signal is time-varying and non-stationary because of the effect of defects on the rotating components and working conditions. The commonly used short-time Fourier transform (STFT) divides the time-domain signal into several segments by a sliding window. However, the STFT cannot optimize the time and frequency resolution. Considerable prior knowledge and much expertise are needed to further analyze these complex vibration signals. Therefore, a novel processing method comprising a fault vibration signal and named short-frequency mutual information entropy threshold filtering is proposed to filter and reconstruct the non-stationary vibration signal for diagnosing the impulse fault features. In this method, the improved STFT with an adaptive optimal window length is used to transform the vibration signal into the time-frequency domain. Then, a Meyer wavelet filter bank is used to divide the frequency spectrum into several sub-bands in each moment. The minimum mutual information entropy between the sub-bands and the original frequency is used to remove the partial sub-bands and filter the useful features from each frequency spectrum. Then, the frequency spectra are rearranged and an inverse STET is used to recover the original signal. The envelope spectrum method is used to extract the fault features from the recovered signal. This method is completely determined by the characteristics of the vibration signal data. Finally, the fault vibration signal from our testing rig and wind turbine gearbox are used to verify the performance of the proposed method. The results show that this method can efficiently detect impact fault features in the vibration signal.