Time-delayed feedback stochastic resonance enhanced minimum entropy deconvolution for weak fault detection of rolling element bearings

Abstract

Early fault characteristics of roller element bearings are overwhelmed by strong background noise and vibrational responses excited by other parts in machinery. Stochastic resonance (SR) can use noise to enhance weak useful signal detection, but when noise imbedded in signals is too strong to trigger SR. In this paper, therefore, we would propose a time-delayed feedback SR enhanced minimum entropy deconvolution method to enhance weak fault characteristic of roller element bearings, where minimum entropy deconvolution is used to preprocess vibration signals to partly eliminate strong noise imbedded in signals and then the preprocessed signals are fed into time-delayed feedback SR to further enhance weak fault characteristics. In time-delayed feedback SR, the signal-to-noise ratio (SNR) is seen as an indicator to optimize the adjusting parameters of the time-delayed feedback SR for triggering the optimal resonance. Finally, a simulation and a bearing fault experiment are performed to demonstrate the feasibility of the proposed method, respectively. The detected results indicate that the proposed method can detect weak fault characteristics precisely and is superior to minimum entropy deconvolution and fast kurtogram methods.