The fast kurtogram (FK), as a fast and effective method for fault diagnosis, is well accepted by many experts and scholars. However, the FK can only estimate the bandwidth and central frequency which come from resonance modulation of the signal. Sometimes useful information (containing faults) may be lost due to the inaccuracy of the estimated center frequency or bandwidth. In this paper, a novel method named empirical scanning spectrum kurtosis (ESSK), based on empirical wavelet transform (EWT), is proposed. Constructed by the principle of EWT, a set of filters with varying bandwidth scan and filter the whole frequency domain from low to high and a series of empirical modal components are obtained. Then, the spectral kurtosis (SK) of these components is calculated. The center frequency and bandwidth corresponding to the component which has the maximum SK are selected as the optimal center frequency and bandwidth. This method can adaptively and accurately find the frequency band containing rich fault feature information, and extract the corresponding component. Multiple simulation signals and experimental signals are used to verify the effectiveness of the proposed method. The results show that the method can maximally extract the components which contain the periodic pulse information and accurately diagnose the faults of the rolling bearing. In addition, comparisons with three popular signal processing methods, including the sparsogram, firbased FK and shorttime Fourier transform (STFT) based FK are conducted to highlight the superiority of the proposed method.

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Singular spectrum decomposition (SSD) is a new adaptive signal processing method for nonlinear and non-stationary signals. By constructing a trajectory matrix and adaptively selecting the embedding dimensions, the method divides non-stationary signals into several single-component signals successively from high frequency to low frequency. However, in the process of component reconstruction, bandwidth estimation and determining sizable trends by building a Gaussian function superposition spectral model are extremely complicated. Moreover, the parameter setting requires too much manual intervention and lacks theoretical support. Hence, aimed at nonlinear and non-stationary vibration signals of rolling bearings, a novel method of fault feature extraction based on the order statistic filter (OSF) for fast SSD (FSSD) is proposed. The FSSD method adopts the envelope method of OSFs to divide the spectrum and determine the sizable trend to improve the process. The proposed method is applied to bearing fault diagnosis. The analysis results of simulation signals and bearing experimental signals show that the new method can decompose signals quickly, effectively and accurately, and the mode mixing and time-consuming problems are refined.

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