Real-Time Assessment of the Technical Condition of Traction Motors Using Machine Learning and IoT Technologies - Manafov, E. K.; Guliyev, H. B.; Huseynov, F. H. (Science and Transport Progress, 2025)
Purpose. The purpose of this research is to analyze machine learning algorithms, select the most accurate and efficient algorithm for diagnosing the technical condition of an induction traction motor based on operating parameters such as temperature, noise, and vibration, and study the features of using Internet of Things (IoT) technology to assess technical conditions in real time. Methodology. The machine learning algorithm suitable for diagnosing the technical condition of asynchronous traction motors was identified through analysis and comparative methods. Findings. Machine learning algorithms were analyzed, and two distinct algorithms, K-means and Extreme Machine Learning (EML), were selected for diagnosing the technical condition of asynchronous motors. The algorithms were compared based on performance metrics such as accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. The results revealed that the EML algorithm outperformed K-means in these metrics, achieving an overall performance score of 93%. Originality. A novel system was proposed that integrates a machine learning model with IoT technology for real-time diagnostics of the technical condition of traction electric motors. This innovative approach enables dynamic monitoring of the motor’s technical state. Compared to traditional temperature diagnostic systems, such a multi-parameter system will allow you to determine a specific malfunction. Practical value. The proposed system, based on a machine learning model, evaluates the technical condition of traction motors in real-time using IoT. It provides recommendations on when maintenance should be performed, based on the actual condition of the motor. The system allows for maintenance planning based on real-time diagnostics, facilitating a shift from scheduled maintenance to predictive maintenance strategies. This, in turn, increases operational lifespan and minimizes unplanned downtime. By leveraging IoT, the diagnostic system can integrate with motor control systems or SCADA systems, enabling remote monitoring and control of motor operations.