Machine Learning-Based Multi-Fault Diagnosis in Brushless DC Motors Using Electrical and Vibration Signal Features

Brushless DC (BLDC) motors play a key role in electric vehicles, robotics, unmanned aerial platforms, and industrial automation due to their compact construction, high efficiency, and minimal maintenance requirements. However, unexpected faults in these motors can cause downtime, reduced performance, and increased maintenance expenses. This work introduces a machine learning-based multi-fault diagnosis framework for BLDC motors that leverages both electrical and vibration signal features. The proposed system incorporates real-time acquisition of current, voltage, Hall sensor, and vibration data, followed by feature extraction in both the time and frequency domains. Statistical features such as RMS, kurtosis, spectral energy, and FFT components are used to train a Support Vector Machine (SVM) classifier for fault identification. The method is evaluated through MATLAB/Python simulation as well as hardware experiments. The results demonstrate strong classification performance across multiple fault types and operating conditions, highlighting the suitability of the proposed approach for low-cost and scalable predictive maintenance in BLDC motor-driven systems.