Performance Analysis of Different Machine Learning Algorithms for Predictive Maintenance

Abstract

This research offers an extensive study of different machine learning issues for predictive maintenance applications for motor condition ranging from AC motors. The research establishes a scenario that represents the real world to identify which algorithms can be trusted to predict both the time of failure and the actual type of failure in the AC motors. The employed machine learning algorithms include the Random Forest (RFC), Support Vector Classifier (SVC), K-Nearest Neighbor (KNN), Logistic Regression (LR), and XGBoost (XGB) in our work. The assessment includes the comparison of algorithms in terms of the predictive accuracy educated with different size training data. Before the model is developed, thorough data preprocessing methods will be applied that will allow the breaking down of the model assumptions and the optimization of the performance. For preprocessing step the following two steps are made including the removal of unclear samples, label encoding used for categorical columns, and column scaling. Intriguingly, the identification of seemingly outlier data points is revisited, revealing their integral role in capturing the natural variance of the dataset and enhancing classification tasks. These identified features are observed to be pivotal contributors to predictive models. The study shows that in both algorithm failure cases and failure type identification, their performances are comparable. Regarding training time, K-Nearest Neighbors (KNN) algorithm yields the top-performing model for both datasets ( 4 sec and 3 sec) respectively, whereas Random Forest (RFC) performs the worst training time (151 sec) which belong to the binary failure prediction task and XGBoost (XGB) in multi-class failure prediction task (276 sec), which is contributed. Finally, this paper emphasizes that deciding on which machine learning model is appropriate for predictive maintenance can be quite a challenge due to the necessity to balance between accuracy and training time. The findings constitute important tipping point for those companies that aim to implement a solution for predictive maintenance with the KNN model being faster and efficient at the same time.