Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

Abstract. Failure Mode Effect and Critically Analysis(FMECA) is one of the key technologies in testability verification test that based on plenty of failure samples.Line replaceable module(LRM) is the core of the new generation avionics system and military equipment electronic system. Failure data of LRM is difficult to acquire under natural conditions,however,it could be obtained fleetly through accelerated degradation test(ADT).According to the degradation data obtained from the accelerated degradation test and the degradation model, the typical failure modes and the occurrence probability of the LRM could be obtained.