Bearing Fault Detection based on Stochastic Resonance Optimized by Levenberg-Marquardt Algorithm

摘要

Bearings are one of the most important components in rotating machineries because their failure could cause catastrophic disasters of whole system. Currently, one of the main problems when implementing bearing prognostics and health management is how to detect the incipient fault as soon as possible. This capability can enable the operators having sufficient time to implement preventive maintenance activities. For incipient fault, its vibration signal is relatively weak and always submerged in the noise, which makes the fault hard to be detected. Stochastic resonance is a good way to detect the weak signal in strong noise. However, the effect of the stochastic resonance depends on the adjustment of two parameters. Current parameter optimization methods are mainly depend on some random searching algorithms like particle swarm optimization, genetic algorithm etc. However, these methods may converge to local optima and need more searching time. So, the Levenberg-Marquardt algorithm is utilized to optimize the two parameters in this paper. The resonance effect is evaluated by signal-to-noise ratio. In order to validate the effectiveness of the stochastic resonance optimized by Levenberg-Marquardt, two bearing fault data sets were used. The analysis results state the proposed method could detect the fault earlier.