A novel coupled array of multi-stable stochastic resonance under asymmetric trichotomous noise and its application in rolling bearing compound fault diagnosis

Abstract

Rolling bearings are often subject to various types of damage in actual industrial operations. However, the early fault information is weak and completely overwhelmed by interference from noise, which is difficult to detect. In this paper, a novel coupled array of multi-stable stochastic resonance (NCAMSR) driven by asymmetric trichotomous noise is proposed for compound fault diagnosis. The Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) is used to separate the information of different fault features in the compound fault signal, and the outputs of the coupled array system are weighted to find the best effect. The spectral mean signal-to-noise ratio (SMSNR) is designed and the system parameters and weights are optimized using the particle swarm algorithm (PSO). To alleviate the problem of output saturation, the NCAMSR potential model based on the improved Gaussian potential is proposed, and the superior unsaturation of the NCAMSR potential function model is proven by theoretical analysis and experiments. Finally, the excellent performance of NCAMSR in compound fault detection is further verified by using engineering experiments with bearing data from Machinery Faults & Rotor Dynamics Simulator and real damage samples generated from accelerated life tests at Paderborn University, Germany.