Predictive Maintenance Testing in Machine Learning: Combining Manual Insights, Java Programming and Data Science for Automation

<p>A predictive maintenance approach has developed as a revolutionary means to minimize downtime and operational costs in industrial systems. Machine learning techniques are used in this study to design a robust predictive maintenance framework for potential equipment failures based on relevant operational parameters. Random Forest and XGBoost models were trained and evaluated using the AI4I 2020 dataset and the machine failure prediction is highly accurate. Based on domain expertise, these key features, namely Torque [Nm] and Tool Wear [min], were identified as pivotal indicators since they can provide actionable insights for maintenance teams within the domain. The analysis of the two models shows XGBoost performing better on imbalanced data and predicting minority failure cases.</p>