A Piecewise Hybrid Stochastic Resonance Method for Early Fault Detection of Roller Bearings

Abstract

An unsaturated stochastic resonance (USR) method to overcome the output saturation phenomenon observed in the classical bistable stochastic resonance (CBSR) method has been examined. However, the parameters of USR models can lead to inaccurate results while identifying the characteristic frequency amid high levels of background noise. To overcome this limitation, an adaptive piecewise hybrid stochastic resonance (APHSR) method that introduces a parameter μ to improve the performance of fault characteristic detection is proposed. The optimal parameters are determined automatically using both 3D reverse positioning and least-squares methods, combing with signal-to-noise ratio and spectral value as evaluation criteria. The significance of parameter μ is demonstrated by analyzing the critical amplitude and Kramers' escape rate. When the results were evaluated through comparison with the CBSR and USR methods via a simulation and two experiments on a motor and a parallel gearbox, it was demonstrated to be more capable of diagnosing the early faults of rotating machinery especially in high levels of background noise.

Keywords

• Early fault detection
• weak characteristic extraction
• piecewise hybrid stochastic resonance