Enhancing Bearing Fault Diagnosis Using Random Forest Optimized by Metaheuristic Algorithms

This study investigated the classification of bearing faults in electric machines using machine learning and optimization techniques. A Random Forest (RF) classifier was first applied to the dataset, followed by the integration of various metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and the Jellyfish Algorithm (JFA). These algorithms were employed to optimize the hyperparameters of the RF model and improve classification performance. The models were evaluated based on accuracy, F1 score, precision, and recall. Experimental results demonstrated that the RF-ACO hybrid model reached the highest performance across all metrics, with an accuracy of 93.53%, outperforming the other approaches. The obtained results demonstrated that optimizing algorithms significantly enhances the fault detection capability of the Random Forest classifier. This study provides valuable insights into the application of hybrid machine-learning models for effective fault diagnosis in electric motor systems.