Multi-parameter-adjusting stochastic resonance in a standard tri-stable system and its application in incipient fault diagnosis

Abstract

The weak-signal detection approaches based on stochastic resonance (SR) are beneficial in detecting weak vibration signals from strong background noise. Therefore, many SR-based methods for mechanical incipient fault diagnosis appear. Among various nonlinear SR models, the underdamped tri-stable SR system, which has better output performance than other ones, has shown its potential superiority in weak-signal detection. The shortcomings for this model include its nonstandard forms of nonlinear potential functions and its inadequate research on parameter-adjusting mechanism for parameter-fixed noisy signals. In order to solve these issues, a standard tri-stable SR system is introduced in this paper and its SR performance is studied. Furthermore, a multi-parameter-adjusting SR (MPASR) model for the standard tri-stable system is proposed and its parameter adjustment rules for different input signals to produce SR are fully studied. At last, we propose a weak-signal detection method based on MPASR of the standard tri-stable system and employ two practical examples to demonstrate its feasibility in incipient fault diagnosis.