Enhanced detection of rolling element bearing fault based on averaged stochastic resonance

Bearing localized faults generate a series of impact vibrations at bearing characteristic frequencies, which often contain very little energy, and are usually overwhelmed by noise and periodic components generated from other parts, such as gear and shaft. In the past decades, classical stochastic resonance (CSR) method is presented to enhance the fault detection of rolling element bearing. Aiming at identifying the bearing characteristic frequencies in the spectra, SR normalized scale transform has been proposed based on parameter-tuning bistable SR model by us. Based on our former work, this paper presents a new method via averaged stochastic resonance (ASR) to enhance the result of rolling element bearing fault detection furtherly. Simulations are made to validate the effect of the weak signal detection method of ASR. Moreover, two bearing fault enhanced detection strategies of CSR and ASR are investigated. Normal and seeded outer race fault bearings vibration signals from a test rig are processed and analyzed using the two methods, and the results are compared.