Periodic Pulse Feature Enhancement Method Based on Spectral Amplitude Modulation and Unbiased Autocorrelation Index

Rolling bearings are important wearing parts in modern industrial equipment, and their working status is related to production safety and efficiency. Detecting periodic pulse characteristics in bearing vibration signals is a key step in achieving fault diagnosis. Enhancing the intensity of this feature from the frequency domain is the core strategy of the Spectral Amplitude Modulation (SAM) algorithm. This paper uses SAM to enhance the periodic pulse characteristics in strong noise and strong interference signals, suppress noise and interference, and achieve more obvious fault diagnosis features. Aiming at the selection of modulation order in SAM, this paper uses unbiased autocorrelation index to quantify the periodic pulse characteristics in each modulation process to achieve adaptive feature enhancement and extraction. The simulated signal demonstrates the enhanced effect of this method from the frequency domain. Bearing experimental signals verify the effectiveness of this method.