The fast bearing diagnosis based on adaptive GSR of fault feature amplification in scale-transformed fractional oscillator

From the noise-assisted perspective of stochastic resonance (SR), fractional system has been adopted to enhance the diagnostic performance of mechanical faults by utilizing the previous state information in mechanical degradation process, but the computation is extremely time-consuming. To address this challenge, we develop a fast diagnosis method leveraging the mechanism of generalized SR (GSR)-based active energy conversion in fluctuating-damping fractional oscillator (FDFO). Through the analysis of system stationary response, we propose a theoretical index known as fault feature amplification (FFA), which effectively replaces the time-consuming numerical solution in multi-parameter optimization, leading to a remarkable reduction in the time complexity of the adaptive diagnosis algorithm. This improvement brings about significant benefits, notably simplifying the diagnosis flow. Based on the results of performance evaluation in diagnosing simulated bearing signals, the proposed method exhibits a comprehensive superiority in identifying ability and diagnosis efficiency. Finally, this method has been further validated in experimental diagnosis, especially for some challenging cases, providing strong support for engineering applications, particularly in the fast diagnosis of complex operating environments.