Induction Motor Multi Incipient Fault Detection based on Gradient Boosting Algorithms

Induction motors are a necessity in many industries, which is why early fault detection is critical to account for damage and industrial downtime. Among the incipient damages, BF and stator winding faults are the most prevalent. Consequently, early detection and classification of these faults are gaining significant attention. This paper investigates the application of multiple gradient boosting machine learning (ML) algorithms, that are known for their robustness, and analyses the accuracy of the models on faulty induction motors (IM) using Motor Current Signal Analysis (MCSA). Five different Supervised machine learning algorithms such as Gradient Boosting Machines, XGBoosts, and LightGBM were used in this study and compared with strong models like RF and KNN. Overall, the experiments provided a classification accuracy of approximately 92% and were able to distinguish the normal, bearing, and stator winding faulty signals. The obtained results show that current signals are a viable option for observing IM electrical and mechanical faults with finetuned optimization of hyperparameters.