Program Implementation of a Fault Diagnosis Method Based on Fuzzy Neural Network

Fuzzy Neural Network (FNN) is an intelligent algorithm that integrates fuzzy logic with neural networks, particularly suitable for fault diagnosis in complex systems. In industrial equipment or automation systems, faults often exhibit uncertainty and nonlinear characteristics, which traditional methods struggle to accurately capture.

The unique advantages of fuzzy neural networks include: Fuzzy logic handles uncertainty: Membership functions quantify "partial fault" states (such as "slight overheating"), overcoming the binary limitation of Boolean logic. Neural network's self-learning capability: During the training phase, the system automatically adjusts rule weights to adapt to different equipment fault patterns, reducing reliance on manual expertise.

In MATLAB implementation, the program typically contains the following core modules: Data preprocessing: Normalize sensor signals and design membership functions (e.g., Gaussian functions) to fuzzify input features through MATLAB's 'normalize' function and 'gaussmf' membership function. Network structure design: Implement multi-layer feedforward networks where fuzzy rule layers combine with neural nodes, and the output layer uses defuzzification methods like 'centroid' to convert to specific fault types. Hybrid training strategy: Combine backpropagation (for parameter optimization using 'trainlm' or 'trainbr' functions) with clustering algorithms (for rule generation using 'fcm' or 'subclust') to improve diagnostic accuracy.

This method demonstrates strong scalability and can be adapted to scenarios such as wind turbine gearboxes and chemical pipelines. Future integration with deep learning techniques could potentially enhance its capability to identify concurrent multiple faults.