Bearing fault feature extraction based on MOMEDA and CS-Wood-Saxon stochastic resonance

Abstract

Abstract Nonlinear system and noise intensity are the key factors for fault signal feature extraction by using stochastic resonance, which directly affects the output effect of stochastic resonance.1. Since the bistable stochastic resonance system is limited by frequency interval and system parameters when transmitting the original signal, the Wood-Saxon stochastic resonance nonlinear system is adopted in this paper.2. Since the collected bearing fault original signal contains a large amount of background noise, in order to make better use of noise intensity, the output of stochastic resonance model is more conducive to fault feature extraction. Before the signal is processed, the signal is pre-processed and filtered.To solve the above problems, a Cuckoo Search algorithm(CS) based on Multipoint Optimal Minimum Entroy Deconvolution Adjusted (MOMEDA) is proposed. CS) Fault feature extraction method of adaptive Wood-Saxon stochastic resonance bearing.By means of MOMEDA parameter selection calculation analysis, simulation and examples, it is proved that the proposed method can effectively reduce the noise in the signal and enhance the weak feature, so as to realize the accurate early bearing fault diagnosis.