Predictive Maintenance: Machine Learning Approaches for Enhanced Equipment Reliability

Predictive maintenance is an innovative approach that predicts equipment failure before it happens to allow organizations to schedule the maintenance activities at optimal times. The strategy eliminates unexpected downtime and overall operational costs, so it is one of the most effective solutions in industries with on-going continuous machinery operation. In this paper, machine learning techniques in predictive maintenance using the publicly available data from Kaggle is examined. The proposed work investigates critical steps undertaken for the development of PdM systems-including data preprocessing, feature engineering, model selection, and evaluation. Due care is taken at every stage to enhance predictive accuracy and reliability as effective data preparation and relevant feature extraction are essential to the success of a model. Several models are tested and compared, the best one coming out to be the Random Forest classifier. This model achieves the highest accuracy at 96 percent, though its precision, at 93 percent, is slightly less than that of some other models. These results show that it is indispensable to balance accuracy and precision to realize the potential of PdM. This study has shown how predictive analytics based on ML significantly strengthen maintenance strategies by making decisions based on data, thereby saving time, money, and preventing machinery from breaking down when least expected.