Experimental Comparative Evaluation of Machine Learning Methods for Early Multi-Fault Detection in Brushless DC Motors

Early and reliable fault detection in Brushless Direct Current (BLDC) motors is essential for improving system reliability and reducing unplanned industrial downtime. This study presents a controlled experimental investigation of data-driven machine learning approaches for the classification of multiple common BLDC motor faults. Four representative fault-related indicators were obtained under systematically designed operating conditions, and a consistent feature extraction procedure was applied prior to model development. A comparative evaluation was conducted using Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), k-Nearest Neighbour (kNN), and decision tree-based classifiers. All models were trained and tested on the same dataset using an identical validation protocol to ensure methodological fairness and reproducibility. Performance was assessed through standard classification metrics, enabling a transparent comparison of predictive capability and stability. The results show that the MLP model achieved the highest overall classification accuracy (91.6%), closely followed by SVM (91.4%) and kNN (90.2%). Although the performance differences are moderate, the neural network demonstrated more consistent behaviour in scenarios where fault signatures exhibited overlapping characteristics. These findings suggest that non-linear feature interactions play a significant role in BLDC fault discrimination and can be effectively captured by multi-layer architectures. The study provides a reproducible experimental framework and a balanced performance assessment that may support both academic research and the practical development of intelligent condition monitoring systems for BLDC-driven applications.