A robust index-guided GSR approach to improve the efficiency of bearing fault diagnosis

Stochastic resonance has been widely used in bearing fault diagnosis due to the enhancement principle of energy conversion from noise to weak signals. However, the diagnosis efficiency and robustness is still challenging. Under the mechanism of generalized stochastic resonance (GSR), in this paper we propose a scale-transformed linear oscillator (SLO) and obtain the analytical expression of fault feature amplification (FFA) to replace the numerical implementation of output signal-to-noise ratio in the multi-parameter optimization. This brings a substantial benefit to the reduction of time complexity in fast fault diagnosis, which is verified in both theory and simulations. In the experimental diagnosis for some typical cases, the results show that the proposed method is valid and exhibits superiority in diagnostic performance, efficiency and robustness, demonstrating that the FFA-guided GSR-SLO method has great potential in engineering applications, especially for real-time fault diagnosis in complex operating environments.