MACHINE LEARNING-DRIVEN PREDICTIVE MAINTENANCE: OPTIMIZING NAIVE BAYES WITH SYNTHETIC DATA AND CLASS IMBALANCE TECHNIQUES

Integrating machine learning into predictive maintenance has steadily gained traction across industries, revolutionizing maintenance strategies. While numerous studies have investigated this topic, and many sectors have successfully implemented machine learning models, some industries rely on inefficient and costly traditional methods. This project aims to bridge that gap by advancing machine learning-driven predictive maintenance research utilizing a synthetic dataset. Specifically, the study explores a novel approach by combining binarisation techniques with the Naive Bayes algorithm—an area largely underexplored in existing literature. Additionally, Naive Bayes is enhanced with Bagging and Boosting techniques to improve performance. At the same time, SMOTE (Synthetic Minority Over-Sampling Technique) and under-sampling are applied to address the class imbalance in predicting machine failure. Six models were developed: Naive Bayes [SMOTE], Naive Bayes with Bagging [SMOTE], Naive Bayes with Boosting [SMOTE], Naive Bayes [Under sampling], Naive Bayes with Bagging [Under sampling], and Naive Bayes with Boosting [Under sampling]. Among these, the Naive Bayes [SMOTE] model achieved outstanding results, with an accuracy of 0.999 and a precision of 1.0, outperforming previous studies and setting a new benchmark in predictive maintenance research. These findings highlight the potential of advanced machine learning techniques in significantly improving predictive maintenance accuracy and efficiency across industries.