A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions

Abstract

Rotating machinery (RM) such as bearings and gears often operates under time-varying operating conditions (TVOC), which makes the vibration signals non-stationary. In this case, eliminating the effect of non-stationary noise and mining the weak fault information in the vibration signal is the key to implementing weak fault detection of RM. Therefore, a novel denoising strategy based on sparse modeling is proposed in this paper. Firstly, a time series model is utilized to model the non-stationary baseline vibration (BV) generated by healthy RM, and sparse representation theory is introduced to identify the model structure and parameters. Subsequently, a time–frequency response filter is constructed based on the baseline model parameters, which can be utilized to filter out the BV from the raw signal to enhance the fault information. Both simulation and experimental studies verify that the proposed method performs better than several comparison methods in weak fault detection of RM under TVOC.