Applications in bearing fault diagnosis of an improved Kurtogram algorithm based on flexible frequency slice wavelet transform filter bank

Abstract

This paper proposes a flexible frequency slice wavelet transform (flexible FSWT) for Kurtogram, a classic signal processing method, with an intend to expand its range of application. The Kurtogram, however, does not consider the details of the spectrum while dividing the frequency bands, which may lead to diagnosis errors. The variable window characteristics of the order statistic filter and the minimum value spectrum segmentation method have been utilized to obtain the multi-level spectrum segmentation filter bank, which can be employed to construct the basic framework of the novel Kurtogram. Relying on the unrestricted filtering property of FSWT, the reconstruction and extraction of the composite spectrum boundaries have been realized. The flexible FSWT adds self-adaptability to the application of FSWT in fault diagnosis, and solves the problem of frequency band discrimination failure of time–frequency diagram diagnosis. The validity of flexible FSWT has been verified by simulation and experiment signals.