Stochastic resonance (SR), with its unique noise utilization mechanism, has demonstrated remarkable advantages in the field of mechanical fault diagnosis. However, the performance of SR is highly dependent on the precise matching of noise intensity and system parameters. In practical engineering applications, noise is often uncontrollable and dynamically changing, which limits its applicability. By contrast, vibration resonance (VR) enhances weak signal detection by introducing a controllable external high-frequency excitation. The stiffness-softening effect induced by this excitation can modulate the potential-well characteristics, alter the system’s equilibrium states, and induce bifurcation, thereby effectively amplifying weak fault signals. Based on this, this paper proposes a potential-well parameter decoupled mechanical fault detection method based on time-delay feedback fractional VR (TDF-FVR). By independently adjusting the width and depth of the potential well, the method effectively optimizes the resonance effect to amplify weak fault signals. To achieve this, TDF-FVR system with tunable parameters is constructed, and a quantum genetic algorithm (QGA) is employed to adaptively optimize the system parameters, using the system’s response amplitude gain as the objective function to achieve optimal resonance and improve weak fault signal detection performance. Finally, bearing fault experiments verify the effectiveness of the proposed method, which shows excellent performance in weak fault signal detection. Furthermore, comparative results with the optimized wavelet packet transform, fast kurtogram, and feature modal decomposition methods further demonstrate the superiority of the proposed method.
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