Fault feature extraction method of gear based on spectral amplitude modulation and autocorrelation analysis

Abstract

Due to the strong noise interference and amplitude modulation effect, it is difficult to extract the impact characteristics of the fault gear from the spectrum of the fault signal. To tackle these issues, a gear fault feature extraction method based on spectral amplitude modulation (SAM) and autocorrelation analysis is proposed. First, the SAM method is used to decompose the signal into different components according to energy, and the optimal component with the most fault information is determined by combining kurtosis index and magnitude order selection range. Then, the optimal component is denoised using the autocorrelation function. Finally, the fault feature frequency is extracted by calculating the squared envelope spectrum of the denoised signal. The superiority of the method is verified by simulating the signal. Furthermore, the effectiveness and superiority of the method in gear fault diagnosis are verified by comparison with the fast kurtogram, cepstrum pre-whitening, and SAM.