Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance

Adaptive stochastic resonance (ASR) has been proven effective in enhancing weak periodic signals that are submerged in heavy background noise. Given such benefit, ARS has also been applied in detecting bearing faults based on vibration signal analysis. However, when the vibration has an extremely low signal-to-noise ratio (SNR), the fault characteristic frequency may not be accurately enhanced via the traditional ASR. To address this problem, this paper designs the sound-aided vibration signal ASR (SAVASR) method, which procedures are summarized as follows. First, the bearing sound and vibration signals are demodulated. Second, the envelope vibration signal is adaptively enhanced by moving a sliding window along the time axis of the envelope sound signal. Third, the optimized fused signal is sent to the ASR system, in which the parameters are adaptively adjusted based on a synthetic evaluation index. Fourth, the bearing fault is detected from the spectrum of the optimal SAVASR output signal. Qualitative and quantitative analyses are performed to evaluate and compare the performance of SAVASR with that of ASR, where only the vibration signal is processed. Given its unique approach in detecting weak signals by fusing multiple sensor information, SAVASR shows high potential in automatically detecting bearing faults especially under low SNR conditions.