Cluster discharge resonance neuron model and its application in machinery multi-dimensional fault vibration signals.

Abstract

Through the analysis of multidimensional vibration signals of machinery, existing faults in mechanical equipment can be timely identified to ensure normal equipment operation. In biological nervous systems, noise influences the generation, transmission, and response of neural signals, causing these signals to exhibit diverse dynamic behaviors. Inspired by this mechanism, to improve fault diagnosis accuracy, this study investigates the advantages of stochastic resonance effects in discharge neural networks and proposes an adaptive cluster discharge resonance neuron (CDRN) model. This model enhances characteristic frequency extraction capability for bearing fault vibration signals. In addition, by embedding a multi-dimensional neuron model into the CDRN framework, the limitation of one-dimensional signals being vulnerable to complex noise interference and false detection is overcome. Using the output signal-to-noise ratio and neural network classifier recognition rate as evaluation metrics, the detection performance of the CDRN method is compared with the single one-dimensional stochastic resonance (SOSR) method and the single hyperbolic tangent neuron (SHTN) method for early-stage mechanical fault detection in wind turbine bearing inner and outer rings. In the two experiments, the fault detection rate of the CDRN method reaches 100%. Experimental results demonstrate that the CDRN method outperforms both SOSR and SHTN in mechanical fault feature recognition and significantly improves early fault detection accuracy.