Fault Diagnosis Method Using Fuzzy Neural Networks

Fuzzy neural network fault diagnosis methods integrate the advantages of fuzzy logic and neural networks, effectively handling complex uncertainty issues in industrial systems. When implemented on the MATLAB platform, this approach enables rapid deployment and direct application in practical scenarios. The MATLAB implementation typically involves creating hybrid systems using the Fuzzy Logic Toolbox and Neural Network Toolbox, with system integration achieved through Simulink blocks or custom MATLAB scripts.

The core innovation of fuzzy neural networks lies in combining the linguistic rule capabilities of fuzzy systems with the learning capacity of neural networks. In fault diagnosis applications, fuzzy logic first processes the fuzziness and uncertainty in sensor data—such as potential noise or measurement errors in temperature and vibration signals. The neural network then optimizes fuzzy rule parameters through training data, enabling adaptive system adjustment. Key MATLAB functions for this integration include anfis for adaptive neuro-fuzzy inference systems and nftool for neural network fitting tool implementation.

A typical MATLAB implementation of fuzzy neural network fault diagnosis involves several critical phases: During data preprocessing, raw signals undergo filtering and feature extraction using functions like filter and pca. The fuzzification stage converts precise inputs into fuzzy variables through membership function definitions using mfedit. Neural network training utilizes historical fault data to adjust membership functions and connection weights via algorithms like backpropagation implemented with trainlm. The final defuzzification output clearly displays fault types or system health status using centroid or mamdani methods accessible through defuzz functions.

This method proves particularly effective for early fault detection in complex equipment like rotating machinery and power systems. Compared to traditional threshold-based alarming, it detects abnormal trends earlier while reducing false alarm rates. MATLAB's Neural Network Toolbox and Fuzzy Logic Toolbox provide convenient interfaces for algorithm implementation, allowing users to focus on engineering definitions of fault characteristics rather than underlying mathematical computations. The implementation typically involves defining network architectures with feedforwardnet and fuzzy rule bases with fuzzy.

In practical applications, this approach supports rapid adaptation to new equipment through transfer learning techniques, and offers strong interpretability of diagnosis results—capable of outputting specific fault probabilities while tracing the fuzzy rule chains triggering diagnoses. For predictive maintenance systems in the Industry 4.0 context, this intelligent diagnosis module is becoming a critical component, with MATLAB deployment options including standalone executables via MATLAB Compiler or integration through MATLAB Production Server.