The adaptive bearing fault diagnosis based on optimal regulation of generalized SR behaviors in fluctuating-damping induced harmonic oscillator

Abstract

Stochastic resonance (SR) has been widely used in bearing fault diagnosis due to the noise-assisted enhancement principle of weak fault signal. The purpose of this paper is to cast off the basic constraint of nonlinearity and extend the classical SR-based nonlinear methods to a new linear Langevin system of fluctuating-damping linear oscillator (FDLO), and to introduce the generalized scale transformation (GST) to make it more suitable for processing fault features. By analyzing the system stationary response, it is found that the proposed GST-FDLO system displays a rich variety of generalized SR (GSR) behaviors generated by the active synergy between internal regulation and external input signal. Furthermore, the GSR-based energy conversion mechanism is investigated to reveal the effects of different parameters on the enhancement of fault feature. Finally, the experimental results demonstrate that the proposed method is always valid and exhibits the superiority in diagnosis performance and operating efficiency, especially in several typical difficult cases, such as corrupted record, low signal-to-noise ratio (SNR), and non-periodic impulses.