Motor Operation Fault Detection Using Genetic Algorithm

This project implements genetic algorithm for motor operation fault detection, specifically targeting ball bearing faults, inner raceway faults, and combined ball-inner race faults. The genetic algorithm implementation employs binary chromosome encoding where each gene represents specific fault characteristics extracted from vibration signals. We utilize 40 datasets as training samples to evolve the population through selection, crossover, and mutation operations, while 4 independent datasets serve as testing samples to validate the model's generalization capability. The fitness function calculates classification accuracy based on feature matching between chromosome patterns and fault signatures. This approach enables effective motor fault detection and provides accurate diagnostic results. Furthermore, the algorithm can be optimized through parameter tuning (mutation rate, population size) and advanced genetic operators to enhance detection accuracy and reliability for handling more complex fault scenarios.