Machine Learning-based Fault Prediction and Diagnosis of Brushless Motors

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This paper presents a machine learning-based approach for predicting and diagnosing faults in brushless motors. By utilizing extensive sensor data and employing algorithms such as Support Vector Machines (SVM), Neural Networks (NN), and Random Forests (RF), the model monitors and diagnoses faults in real-time. Experimental results indicate that SVM achieves an accuracy of 95%, NN achieves 97%, and RF provides a balanced performance with an accuracy of 92%. The study not only analyzes different fault types and their severities but also proposes effective countermeasures. This research significantly enhances the efficiency, reliability, and maintenance of brushless motors, contributing to industrial advancements. Furthermore, it highlights the importance of integrating advanced machine learning techniques to ensure the robustness and accuracy of fault prediction systems, ultimately supporting the development of smarter, more resilient industrial machinery. This comprehensive approach paves the way for improved operational strategies and smarter maintenance protocols in industrial applications, ensuring long-term sustainability and performance.