Most traditional stochastic resonance methods pose three fundamental limitations in extracting fault signature: (1) simulated impulse signals are insufficient to match the complicated mechanical vibration signals; (2) one-dimensional signals are vulnerable to interference from multiscale noise, which potentially yields false detection; and (3) optimization function in the signal-to-noise ratio largely depends on the advanced requirements of the characteristic frequency, thereby restricting the development for actual operation. Accordingly, this study investigated the dynamic model of rolling bearings and explored the benefits of the coupled hybrid stochastic resonance (CHSR) method for identifying weak signal signatures. In addition, we proposed multi-objective optimization and established a dynamic model of a rolling bearing with four degrees of freedom under a rotor system with external excitation. Overall, the proposed system offers three main advantages: (1) the dynamic model facilitates correspondence with real vibration signals; (2) the CHSR method can process two-dimensional signals and overcome the saturation phenomenon, which enhances the weak useful signal signature describing machinery health; and (3) the cross-correlation spectral kurtosis and the critical amplitude are defined within the multi-objective function to adaptively optimize the parameter adjustment of the CHSR. Thereafter, the proposed method exhibited adequate enhancement ability integrated the study on dynamic simulation and machinery bearings of wind power generator. Moreover, the applied machine learning model further clarified the superior performance of CHSR method in identifying the weak fault signature.

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 l 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 l 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.

As the core equipment of wind turbines, rolling bearings affect the normal operation of wind power generators, resulting in huge economic losses and significant social impacts in the case of faults. Most faults are not easily found because of the small vibration response of these rolling bearings that operate in harsh conditions. To address the problem that the fault identifications of rolling bearings are disturbed by the strong noise in wind power generators, an adaptive nonlinear method based on a piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis is proposed. Then, the vibration signals collected from the fault point of the outer and inner rings are used to clarify the outstanding capability of the proposed method when compared with the maximum cross-correlation-kurtosis-based unsaturated stochastic resonance method. Furthermore, the machine learning method based on the medium tree was adopted to further prove the excellent performance of the piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis for realizing the efficient detection of rolling bearing faults in wind power generators, which has important innovation significance and practical engineering value for ensuring the safe and stable operation of wind turbines.

Bearings are critical yet vulnerable components in mechanical equipment, with potential failures that can significantly impact system performance. As stochastic resonance methods effectively convert noise energy into fault characteristic energy within bearing vibration signals, they remain a research focus in bearing fault diagnosis. This study proposes a coupled neuron model based on biological stochastic resonance effects for processing bearing vibration signals. To enhance parameter optimization, we develop an improved deep reinforcement learning algorithm that incorporates a prioritized experience replay buffer into the network architecture. Using the SNR as the evaluation metric, the algorithm performs data screening on the replay buffer parameters before training the deep network for predicting coupled neuron model performance. In terms of experimental content, the study performed data processing on simulated signals and vibration signals of gearbox bearing faults collected in the laboratory environment. By comparing the coupled neuron model optimized with a reinforcement learning algorithm, particle swarm algorithm, and quantum particle swarm algorithm, the experimental results show that the coupled neuron model optimized with a deep reinforcement learning algorithm has the optimal signal-to-noise ratio of the output signal and recognition rate of the bearing faults, which are −13.0407 dB and 100%, respectively. The method shows significant performance advantages in realizing the energy enhancement of the bearing fault eigenfrequency and provides a more efficient and accurate solution for bearing fault diagnosis, which has important engineering application value.