<![CDATA[Machinery failure occurs when equipment can no longer perform its intended function, either partially or completely, leading to production delays and increased maintenance costs. While machine learning has been used to predict equipment failure, existing approaches tend to focus on predicting failure at the component level or the timing of failure without classifying maintenance types. This study addressed this gap by classifying maintenance events at the machine level for a heavy equipment remanufacturing company in Balikpapan, Indonesia. Historical records from January 2023 to May 2025 were analyzed, comprising 29 monthly performance logs; 2,484 equipment records; and 7,721 maintenance tickets. Maintenance events were categorized as either breakdown (complete loss of function) or corrective (partial functional degradation allowing continued operation). Key features included physical availability (PA), mean time between failures (MTBF) and mean time to repair (MTTR). Four supervised machine learning models, namely support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) and logistic regression (LR), were trained and evaluated using accuracy, precision, recall and F1 score. XGBoost performed best, achieving an accuracy of 65.27%, a precision of 71.17%, a recall of 71.54%, and an F1 score of 71.36%, outperforming RF, LR, and SVM. Although the classification task is challenging due to real-world variability in maintenance conditions, the results demonstrate the effectiveness of ensemble-based models, particularly gradient boosting, for classifying maintenance events. These findings highlight the potential of data-driven analytics to support operational decision-making and maintenance planning in industrial environments.]]>
Copyrights © 2026
