Fault Diagnosis Using Genetic Algorithm (GA) Optimized RBF Neural Network

Application of Genetic Algorithm Optimized RBF Neural Network in Diesel Engine Fault Diagnosis

RBF (Radial Basis Function) neural networks serve as powerful pattern recognition tools, particularly suitable for nonlinear system modeling and fault diagnosis applications. However, their performance highly depends on parameter configurations including the number of hidden layer nodes, center positions, and width parameters. Traditional approaches relying on empirical knowledge or trial-and-error adjustments often yield limited effectiveness.

Genetic Algorithm (GA) addresses this challenge as a global optimization technique that mimics biological evolution mechanisms through selection, crossover, and mutation operations to search for optimal solutions. Integrating GA with RBF neural networks enables automatic optimization of network architecture and parameters, thereby enhancing diagnostic accuracy.

The implementation approach consists of the following key steps: Identify RBF neural network's critical parameters (center points, width parameters, and output layer weights) as optimization variables for the genetic algorithm. Design a fitness function, typically using diagnostic accuracy rates or error metrics, to evaluate individual solutions. Through GA's iterative evolution process, progressively optimize parameter combinations to achieve the highest-performing RBF network configuration.

Key advantages of this methodology include: Elimination of subjective manual parameter tuning Enhanced generalization capability of diagnostic models Suitability for complex nonlinear systems

In diesel engine fault diagnosis applications, the optimized RBF network demonstrates improved accuracy in identifying abnormal vibrations, temperature variations, and other characteristic patterns, enabling early fault warnings. Future work could integrate deep learning techniques to further expand model capabilities.