OPTIMIZING GENERATOR SET MAINTENANCE FOR EMERGENCY BACKUP POWER SUPPLY AT BAROMBONG POLYTECHNIC OF MARITIME STUDIES
Keywords:
Generator set, preventive maintenance, maintenance optimization, Failure Mode and Effects Analysis (FMEA), Risk-Based Maintenance, emergency backup power.Abstract
Electrical power is essential for supporting educational, administrative, and operational activities at Barombong Polytechnic of Maritime Studies. Frequent interruptions in the electricity supply from the national utility company (PLN) may disrupt campus operations, making generator sets (gensets) indispensable as emergency backup power sources. This study aimed to optimize the generator maintenance system to improve its reliability, operational efficiency, and long-term performance. A descriptive qualitative approach was employed using observations, interviews, and documentation involving generator operators and maintenance technicians. The collected data were analyzed using the Failure Mode and Effects Analysis (FMEA) method to identify potential failure modes, evaluate associated risks, and establish maintenance priorities through Risk Priority Number (RPN) analysis. The results revealed that the most critical failures involved the Under Voltage Release (UVR) of the MCCB on the Automatic Transfer Switch (ATS) panel, radiator leakage, and fuel filter leakage. These failures were primarily attributed to inadequate preventive maintenance, limited spare-part availability, and insufficient technical competence among operators. The study concludes that integrating Risk-Based Maintenance (RbM), strengthening preventive maintenance practices, and enhancing technicians’ competencies can significantly improve generator reliability, operational efficiency, and the continuity of emergency power supply in maritime higher education institutions.
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References
Abdullah, M. (2021). Teknologi Sistem Tenaga Listrik. Deepublish.
Ahmad, R., Kamaruddin, S., Azid, I. A., & Almanar, I. P. (2021). Preventive maintenance decision models for industrial equipment: A review. Journal of Quality in Maintenance Engineering, 27(4), 765–789.
Al-Najjar, B., & Alsyouf, I. (2004). Enhancing a company's profitability and competitiveness using integrated vibration-based maintenance: A case study. European Journal of Operational Research, 157(3), 643–657.
Alief Yuliyanto, M. (2023). Optimalisasi Perawatan Cooling System terhadap Kinerja Mesin Diesel Generator di Kapal MT Mundu (Undergraduate thesis). Politeknik Ilmu Pelayaran Semarang.
Ardhi Dharmawan. (2020). Optimasi Pemeliharaan Komponen Gas Turbine untuk Meningkatkan Keandalan Unit Pembangkit Listrik. Institut Teknologi Sepuluh Nopember.
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. P., Basto, J. P., & Alcalá, S. G. (2020). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
Daryanto. (2020). Dasar-Dasar Teknik Tenaga Listrik dan Pemeliharaan Mesin. Alfabeta.
Daffa Rizky Aulia, M. (2023). Optimalisasi Kinerja Emergency Generator terhadap Situasi Blackout di Kapal MV Phoenix Sky (Undergraduate thesis). Politeknik Ilmu Pelayaran Semarang.
Hidayat, R., & Nugroho, A. (2023). Penerapan metode FMEA dalam analisis kegagalan sistem genset. Jurnal Teknik Elektro Terapan, 11(1), 12–21.
Huda, M. N. (2018). Perawatan dan Pemeliharaan Mesin. Deepublish.
Jasiulewicz-Kaczmarek, M., Legutko, S., & Kluk, P. (2021). Maintenance 4.0 technologies for sustainable manufacturing: An overview. IFAC-PapersOnLine, 54(1), 117–122.
Kadir, A. (2021). Pembangkit Energi Listrik. Erlangga.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2021). Industrial artificial intelligence for smart maintenance in Industry 4.0. Manufacturing Letters, 28, 1–5.
Liu, Y., Yang, B., & Chen, X. (2022). Intelligent maintenance decision-making using Failure Mode and Effects Analysis and machine learning techniques. Reliability Engineering & System Safety, 220, 108287.
McDermott, R. E., Mikulak, R. J., & Beauregard, M. R. (2009). The Basics of Failure Mode and Effect Analysis (2nd ed.). Productivity Press.
Meissner, E., Hees, A., & Nyhuis, P. (2021). Prescriptive maintenance in manufacturing: A framework and system architecture for smart maintenance. Procedia CIRP, 104, 684–689.
Mulyadi, T., & Rahman, S. (2022). Manajemen Pemeliharaan Mesin Industri. Polinema Press.
Nasir, F., & Pratama, D. (2020). Optimalisasi pemeliharaan generator set pada sistem cadangan daya rumah sakit. Jurnal Energi dan Keteknikan, 6(1), 33–42.
Pinciroli, L., Baraldi, P., & Zio, E. (2023). Maintenance optimization in Industry 4.0. Reliability Engineering & System Safety, 234, 109204.
Sahin, A. Z., Ali, H., & Yilbas, B. S. (2015). Exergy analysis of a thermoelectric power generator: Influence of bi-tapered pin geometry on device characteristics. International Journal of Exergy.
Sugiyono. (2015). Metode Penelitian Pendidikan: Pendekatan Kuantitatif, Kualitatif, dan R&D. Alfabeta.
Supriyadi, E. (2021). Perawatan dan Perbaikan Mesin Diesel Generator. Mitra Wacana Media.
Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Systems with Applications, 36(2), 1195–1207.
Yadav, O. P., Jayaraman, R., & Rathore, A. (2023). Risk-based maintenance planning using Failure Mode and Effects Analysis: Recent developments and future directions. Journal of Quality in Maintenance Engineering, 29(2), 245–266.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2016). Deep learning and its applications to machine health monitoring: A survey. Mechanical Systems and Signal Processing, 115, 213–237.
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 106889.
