Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection

• A gearbox fault feature extraction method is proposed.
• Wavelet correlation filtering is applied for signal preprocessing.
• The spectral kurtosis is presented for defining the optimal filter adaptively.
• The proposed method shows satisfied adaptability in extracting the signal transient.

Spectral kurtosis (SK) provides a valuable tool for detecting the signal transients buried in noise, which makes it very powerful for designing a filter to extract the signal transients. However, SK requires the selection of a time–frequency frame for decomposition based on Short Time Fourier Transform (STFT). This paper presents an adaptive spectral kurtosis filtering technique to extract the signal transients based on Morlet wavelet. The Morlet wavelet is used as a filter bank whose center frequency is defined by the wavelet correlation filtering. Different bandwidth filter in the filter bank is used to select the optimal filter for extracting the signal transients as the one that maximizes the SK. Effectiveness of the proposed technique is verified through the transient extraction of a simulate signal. For the gear fault feature detection of vehicle transmission gearbox, the proposed technique is applied in the extraction of the signal transients that shows the gear fault, which proves the effectiveness of the proposed technique in extracting the signal transients in the practical application.