Fault diagnosis of tooth surface spalling based on variational mode decomposition and maximum correlation kurtosis method

Abstract

As the early fault features of tooth surface spalling are very weak and difficult to extract because of random noise and other types of signal interference, a method that combines maximum correlation kurtosis uncoiling and variational mode decomposition is proposed herein. First, a series of modes are obtained by variational mode decomposition, and the kurtosis criterion is applied to select the modes containing rich fault information for reconstruction and noise reduction. Second, the maximum correlation kurtosis deconvolution method is used to enhance the selected signals. Finally, the fault features are extracted by envelope demodulation of the reconstructed signal. The effectiveness of the proposed method is verified by analysis, and the different frequency components of the vibration signals of tooth surface spalling faults are shown to be separated accurately.