Predictive Maintenance of Electric Motors Using Supervised Learning Models: A Comparative Analysis
It addresses predictive maintenance for electric motors in industrial applications to minimize unplanned downtimes, but it is incremental as it applies existing methods to a specific domain.
This study tackled the problem of diagnosing electric motor conditions by comparing supervised learning models, finding that one model emerged as the best-performing solution with notable differences in accuracy among them.
Predictive maintenance is a key strategy for ensuring the reliability and efficiency of industrial systems. This study investigates the use of supervised learning models to diagnose the condition of electric motors, categorizing them as "Healthy," "Needs Preventive Maintenance (PM)," or "Broken." Key features of motor operation were employed to train various machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Regression models, Random Forest, k-Nearest Neighbors (k-NN), and Gradient Boosting techniques. The performance of these models was evaluated to identify the most effective classifier for predicting motor health. Results showed notable differences in accuracy among the models, with one emerging as the best-performing solution. This study underscores the practicality of using supervised learning for electric motor diagnostics, providing a foundation for efficient maintenance scheduling and minimizing unplanned downtimes in industrial applications.