Particle Swarm Optimization for Enhanced BP Neural Network in Fault Diagnosis

In this paper, we propose an improved particle swarm optimization algorithm to enhance BP neural networks for fault diagnosis in cascaded frequency converters. The implementation involves initializing particle positions representing neural network weights and biases, with fitness evaluation through forward propagation and error calculation. Compared to traditional BP neural networks that rely solely on gradient descent optimization, our enhanced PSO algorithm incorporates adaptive inertia weights and social learning factors to improve convergence speed and diagnostic accuracy. We provide detailed example datasets containing vibration signatures and electrical parameters from converter operations. The algorithm architecture includes parallel processing of multiple particles, each encoding a complete neural network configuration, with global best position tracking through iterative comparisons. Experimental results demonstrate that our optimized approach achieves significantly better performance in identifying converter faults such as IGBT failures and capacitor degradation. While occasional instability was observed during initial convergence phases, this can be mitigated through parameter tuning techniques including constriction coefficients and boundary handling mechanisms. Future improvements will focus on hybrid optimization strategies combining PSO with local search algorithms for enhanced robustness.