EFEKTIFITAS DIGITALISASI IOT VIBRATION ONLINE MONITORING DALAM PENINGKATAN AKURASI PREDICTIVE MAINTENANCE PADA MAIN FUEL OIL PUMP MESIN PLTG MITSUBISHI MW-701

Abstract

PT PLN Indonesia Power UBP Bali manages 24 power generation units that play a critical role in ensuring a stable electricity supply for the island of Bali. The large number of generators necessitates an effective maintenance strategy. Predictive Maintenance, which was previously performed manually, required significant time and human resources. However, the digitalization of maintenance practices through the Internet of Things (IoT) has opened up new opportunities for enhancing machine maintenance effectiveness in the industry. This paper explores the effectiveness of an IoT-based Vibration online monitoring system in improving the accuracy of Predictive Maintenance for the Main Fuel Oil Pump of the Mitsubishi MWH-701 Gas Turbine engine. By utilizing vibration sensors integrated into the IoT system, the operational data of the pump can be monitored in real-time, enabling early detection of potential issues. This study analyzes the data generated from online vibration monitoring to enhance the accuracy of manual measurements regarding the pump's mechanical condition and to identify predictive patterns that may trigger maintenance interventions. The findings demonstrate that the implementation of this system not only improves the accuracy of damage prediction but also significantly reduces equipment downtime and unexpected maintenance costs. These results highlight the importance of digitalization in enhancing operational efficiency and machine reliability. This paper is expected to serve as a reference for the industry in adopting IoT technology technology to improve machine maintenance practices, particularly in Predictive Maintenance.