Failure Prediction Models using Vibration Data Motor and Gearbox: A Case Research in The Mining Industry PT Angsana Coal

Abstract

PT Angsana Coal (PT AC) is a major coal mining company in South Kalimantan, Indonesia, which is a subsidiary of PT Energi Sinar Dunia Tbk under the Energi Mas Group. With a vision to be a leading energy provider, PT AC has increased its coal production from 6 million tons per annum (MTPA) in 2015 to 42 MTPA in 2023, with a target of reaching 54 MTPA through strategic expansion and infrastructure improvement. The company's operations prioritize sustainable resource management, advanced technology, and coal extraction efficiency. This study aims to evaluate the effectiveness of vibration analysis-based predictive maintenance implementation in improving Bunati system reliability, reducing downtime, and lowering maintenance costs. The research method includes historical data collection related to reactive maintenance and comparative analysis of system performance before and after the implementation of predictive maintenance. The results showed that PT AC's reactive maintenance strategy led to a significant increase in unscheduled maintenance downtime, peaking at 706.90 hours in 2018 due to frequent motor and gearbox breakdowns. The implementation of predictive maintenance is able to detect early signs of wear and tear, enabling timely intervention, thus improving system reliability, lowering downtime, and reducing overall maintenance costs. The implications of this study show that investment in monitoring tools, personnel training, and a robust data framework can improve PT AC's operational efficiency, support the achievement of production targets, and promote the sustainability of the energy industry.