Fault diagnosis of position control systems based on multimodal fuzzy neural networks

The reliability of position control systems is critical for the operation of complex equipment, but their intricate operating environments and diverse fault modes pose significant challenges for fault diagnosis. This paper proposes a fault diagnosis method based on a Multimodal Wavelet Fuzzy Neural Network (MWFNN). First, by analyzing the fault mechanisms of position control systems, time-domain features are extracted from key system signals, and the 10 most relevant features are selected through Pearson correlation analysis as inputs to the model. Subsequently, an MWFNN model is constructed by integrating the characteristics of fuzzy neural networks and wavelet neural networks. This model utilizes fuzzy logic to handle uncertainties in input data while introducing a hybrid wavelet neural network to extract multi-scale features. Experiments are conducted using simulated data for validation, and the results demonstrate that the proposed MWFNN model achieves an average diagnostic accuracy of 96.72% in 10-fold cross-validation, significantly outperforming traditional machine learning and neural network models. This method effectively enhances the fault diagnosis accuracy of position control systems, exhibits high robustness and interpretability, and provides a novel approach for intelligent fault diagnosis in complex position control systems.