Predicting Remaining Useful Life of Induction Motor Bearings from Motor Current Signatures Using Machine Learning

Ensuring the reliability of induction motors is essential for industrial applications, as motor failures can lead to unplanned downtime and significant financial losses. Motor current signature analysis (MCSA) has emerged as an effective and non-intrusive technique for diagnosing motor health, particularly for monitoring bearing conditions, which account for a significant percentage of motor failures. However, the MCSA technique can only assess the status of the bearings: whether they are healthy or unhealthy. Regular maintenance activities are necessary to avoid unplanned downtime due to bearing failure. Furthermore, this analysis cannot help proactively replace the bearings before they fail. Therefore, this research develops a predictive maintenance framework by integrating motor current signature analysis with machine learning techniques to estimate the remaining useful life (RUL) of induction motor bearings. The methodology involves analyzing historical motor current data using $I_{sd}-I_{sq}$ trajectory analysis and fast Fourier transform (FFT) to extract relevant health indicators. $I_{sd}-I_{sq}$ analysis identifies deviations in motor behavior, whereas FFT detects harmonics that indicate potential faults. A machine learning model is employed to classify the health status of motor bearings and estimate their RUL based on extracted signal features. This approach effectively differentiates healthy from faulty bearings, enabling proactive maintenance to reduce failures and boost efficiency.