A multi-parameter constrained potential underdamped stochastic resonance method and its application for weak fault diagnosis

Vibration information is widely used in the fault diagnosis of rotating machinery, but the weak components reflecting early faults are often difficult to extract because of strong background noise. Among the variety of weak information extraction methods, the stochastic resonance (SR) method has the unique advantage of transferring noise energy to weak signals and has great application prospect in weak signal extraction. The current underdamped SR method with two adjustment parameters cannot achieve an optimal potential model structure, so it is easy to fall prey to the local optimization problem without having a better feature extraction method. Therefore, in this paper, we present a multi-parameter constrained potential underdamped stochastic resonance method by investigating the potential model and signal-to-noise ratio of the system. Moreover, the weighted ant colony algorithm is introduced to optimize potential parameters and a damping factor is used to improve the ability of weak fault information extraction. Furthermore, several types of bearing failure experiments were implemented to verify the effectiveness of the proposed method. The results show that the proposed method has excellent weak signal extraction ability and is suitable for early fault diagnosis.