Prediction of remaining useful life and downtime of induction motors with supervised machine learning

This research aims to use a vibration monitoring system along with machine learning techniques to predict the downtime and Remaining Useful Life (RUL) of three-phase induction motors in the manufacturing sector. The study obtains measurement data from accelerometer sensors that collect various parameters related to motor performance. The research includes a data preprocessing stage to handle missing data, select predictor attributes, and remove duplicates. Supervised learning algorithms are applied, including Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Artificial Neural Network (ANN). The results show that DT and NB models have the best performance in downtime classification, achieving 100% accuracy, recall, precision and F1 values. In terms of predicting Remaining Useful Life (RUL), the RF model outperforms the base model and ANN, showing better results in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and correlation coefficient.