Aug 2026· INAJEEE Indonesian Journal of Electrical and Eletronics Engineering· Vol 9, pp. 71-78· 0 citations
Abstract
The Spiral pipe mill machine is a vital asset in the steel pipe manufacturing industry that often experiences downtime due to maintenance strategies that are still reactive or corrective maintenance. This approach leads to operational uncertainty and high repair costs. This study aims to create a maintenance schedule for the spiral pipe mill machine using a random forest machine learning algorithm model. This research analyzes 168 historical machine failure data points from January to December 2024 to calculate machine failure intervals. The model was trained using 500 decision trees with an 80% training data and 20% testing data split. Evaluation results show precise model performance with an R-Squared (R²) value of 0.9916, Mean Absolute Percentage Error (MAPE) of 1.84%, and Mean Absolute Error (MAE) of 0.3993 days. Based on these calculations, an annual maintenance schedule can be compiled to provide accurate maintenance time recommendations to minimize sudden machine failures and increase production efficiency.
An integrated framework combining supervised machine learning classification with mathematical optimization to predict equipment failures and minimise maintenance costs under prediction uncertainty is developed, ensuring prediction uncertainty propagates into scheduling decisions and bridging predictive analytics with...
Nooshin Salehabadi, Ming-Yuan Chen· Journal of Quality in Mainte...· 0 citations
General Background Manufacturing continuity in continuous liquid production systems relies heavily on machinery reliability and structured maintenance management. Specific Background PT X faced severe operational disruptions due to high downtime concentrations on the Vacuum Feeder Line 4 machine, reflecting a reactive...
Youngky Odies S, A. Cahyana· Indonesian Journal of Innova...· 0 citations
This study optimized data-driven predictive maintenance in gas plant using machine learning
based Support Vector Machines (SVM) in predicting failures. The application software was
developed to improve the operational efficiency and reliability of turbo-compressors in gas
injection plants. The Gas injection plant produ...
Nathaniel Iyalla· International Journal of Eng...· 0 citations
This study develops an integrated Reliability-Centered Maintenance (RCM) framework combining Failure Mode and Effects Analysis (FMEA), Weibull reliability modeling, and cost-based preventive-maintenance interval optimization for critical production machinery. Using 48 corrective-maintenance records collected between Ma...
S. H. Yuningsih, Kiki Zakaria, V. Rusyn· Operations Research: Interna...· 0 citations
This work presents a novel approach to constructing a predictive model using machine learning techniques for the early identification of machine faults for industrial maintenance purposes. The topic is justified by the importance of predictive maintenance in increasing operational efficiency, reducing costs, and improv...
