Strategi Pemeliharaan Berbasis CBM+ pada Mesin TPE331 untuk Meningkatkan Keandalan Operasional: Studi Kasus Pesawat CASA 212-200 di PT NTP

Agita Ramadhani; Gita Amperiawan; Maykel Manawan; Erzi Agson Gani; M Zainal Furqon

doi:10.35760/tr.2025.v30i3.17

Penulis

Agita Ramadhani Universitas Pertahanan Republik Indonesia Penulis
Gita Amperiawan Universitas Pertahanan Republik Indonesia Penulis
Maykel Manawan Universitas Pertahanan Republik Indonesia Penulis
Erzi Agson Gani Universitas Pertahanan Republik Indonesia Penulis
M Zainal Furqon Universitas Pertahanan Republik Indonesia Penulis

DOI:

https://doi.org/10.35760/tr.2025.v30i3.17

Kata Kunci:

CASA 212-200, Condition-Based Maintenance Plus (CBM+), operational efficiency, maintenance management, TPE331

Abstrak

Aircraft engine maintenance strategies have evolved from schedule-based approaches toward condition-driven systems that emphasize actual component health conditions. The TPE331 turboprop engine used on the CASA 212-200 aircraft is critical in supporting both military and civil aviation operations. However, its maintenance process faces challenges related to high operational intensity, diverse operating environments, spare part availability, and turnaround time. This research was conducted at PT Nusantara Turbin dan Propulsi (NTP) to analyze the existing maintenance system and to propose the implementation of Condition-Based Maintenance Plus (CBM+) as an optimization strategy. The research employed a qualitative descriptive approach using maintenance records, engine performance parameters, and operational cost data. The results show that CBM+ implementation has the potential to reduce unexpected downtime, improve cost efficiency by approximately 8–12%, and enhance fleet readiness through early detection of component degradation. This research demonstrates that CBM+ provides not only technical benefits but also strategic value in supporting the transformation of the national MRO industry toward data-driven maintenance practices.

Unduhan

Data unduhan tidak tersedia.

Referensi

International Air Transport Association (IATA), Airline Maintenance Cost Executive Commentary (FY2020 Data), IATA, Rep. FY2023 MCX. [Online]. Available: https://www.iata.org/contentassets/bf8ca67c8bcd4358b3d004b0d6d0916f/fy2023-mcx-report_public.pdf. [Accessed: Aug. 31, 2025].

Hu, X. Miao, J. Zhang, J. Liu, and E. Pan, “Reinforcement learning-driven maintenance strategy: A novel solution for long-term aircraft maintenance decision optimization,” Comput. Ind. Eng., vol. 153, Art. no. 107056, doi: 10.1016/j.cie.2020.107056.

Van den Bergh, P. De Bruecker, J. Beliën, and J. Peeters, “Aircraft maintenance operations: State of the Art,” FEB@HUB, Brussels, Belgium, Res. Paper 2013/09, 2013. [Online]. Available: https://scholar.google.com/scholar_lookup?title=Aircraft%20maintenance%20operations%3A%20state%20of%20the%20art&author=J.%20Van%20den%20Bergh&publication_year=2013.

Honeywell Aerospace, “TPE331 turboprop engine,” [Online]. Available: https://aerospace.honeywell.com/us/en/products-and-services/product/hardware-and-systems/engines/tpe331-turboprop-engine. [Accessed: May 20, 2025].

Kementerian Perhubungan Republik Indonesia, Laporan tahunan kementerian perhubungan 2021, 2021. [Online] Available: https://ppid.dephub.go.id/fileupload/informasi-setiap-saat/20241021182941.2021_LAPTAH_KEMENHUB_2021.pdf. [Accessed: Aug. 29, 2025].

R. Patibandla, “Predictive maintenance in aviation using artificial intelligence,” J. Artif. Intell. Gen. Sci. (JAIGS), vol. 4, no. 1, pp. 325–333, 2024, doi: 10.60087/jaigs.v4i1.214.

Stanton, K. Munir, A. Ikram, and M. El-Bakry, "Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities," Syst. Eng., vol. 26, no. 2, pp. 216–237, 2022, doi: 10.1002/sys.21651.

Manco, M. Caterino, R. Macchiaroli, M. Rinaldi, and M. Fera, "Aircraft maintenance: Structural health monitoring influence on costs and practices," Macromol. Symp., vol. 396, no. 1, Art. no. 2000302, 2021, doi: 10.1002/masy.202000302.

Tan, Q. Hu, C. Guo, D. Zhu, E. Dong, and F. Zhang,, “Operational readiness-oriented condition-based maintenance and spare parts optimization for multi-state systems,” Reliability Engineering & System Safety, vol. 264, 2025, doi: 10.1016/j.ress.2025.111367.

H. Hsu, Y. J. Chang, H. K. Hsu, T. T. Chen, and P.W. Hwang, "Predicting the remaining useful life of landing gear with prognostics and health management (PHM)," Aerospace, vol. 9, no. 8, Art. no. 462, 2022, doi: 10.3390/aerospace9080462.

Petrone et al., "An innovative health monitoring system for aircraft landing gears," in Proc. 8th Eur. Workshop Struc. Health Monit. (EWSHM), Bilbao, Spain, 2016, pp. 235–244.

Tseremoglou and B. F. Santos, “Condition-based maintenance scheduling of an aircraft fleet under partial observability: A Deep Reinforcement Learning approach,” Reliability Engineering & System Safety, vol. 241, 2024, doi: 10.1016/j.ress.2023.109582.

J. C. Verhagen, B. F. Santos, F. Freeman, P. van Kessel, D. Zarouchas, T. Loutas, R. C. K. Yeun, and I. Heiets, “Condition-based maintenance in aviation: Challenges and opportunities,” Aerospace, vol. 10, no. 9, 2023, doi: 10.3390/aerospace10090762.

Feng, X. Bi, X. Zhao, Y. Chen, and B. Sun, "Heuristic hybrid game approach for fleet condition-based maintenance planning," Reliab. Eng. Syst. Saf., vol. 157, pp. 166–176, 2017, doi: 10.1016/j.ress.2016.09.005.

Li, J. Guo, and R. Zhou, "Maintenance scheduling optimization based on reliability and prognostics information," in Proc. Annu. Rel. Maintainab. Symp. (RAMS), Tucson, AZ, USA, 2016, pp. 1–5, doi: 10.1109/RAMS.2016.7448069.

J. C Verhagen et al., "Condition-based maintenance in aviation: Challenges and opportunities," Aerospace, vol. 10, no. 9, Art. no. 762, 2023, doi: 10.3390/aerospace10090762.

R. Alla, R. Hall, and D. B. Apel, "Perfomance evaluation of near real-time condition monitoring in haul trucks," Int. J. Min. Sci. Technol., vol. 30, no. 6, pp. 909–915, 2020, doi: 10.1016/j.ijmst.2020.05.024.

Matyas, T. Nemeth, K. Kovacs, and R. Glawar, "A procedural approach for realizing prescriptive maintenance planning in manufacturing industries," CIRP Ann., vol. 66, no. 1, pp. 461–464, 2017, doi: 10.1016/j.cirp.2017.04.007.

Lee, J. Ni, J. Singh, B. Jiang, M. Azamfar, and J. Feng, “Intelligent maintenance systems and predictive manufacturing,” J. Manuf. Sci. Eng., vol. 142, no. 11, Art. no. 110805, 2020, doi: 10.1115/1.4047856.

Quatrini, F. Costantino, G. Di Gravio, and R. Patriarca, “Condition-based maintenance: An extensive literature review,” Machines, vol. 8, no. 2:31, 2020, doi: 10.3390/machines8020031.

Fu and N. P. Avdelidis, “Prognostic and health management of critical aircraft systems: A review,” Sensors, vol. 23, no. 19: 8124, 2023. doi: 10.3390/s23198124.

Cao, J. Yu, and F. Duan, "Condition-based maintenance in complex degradation systems : A review of modeling evolution, multi-component systems, and maintenance strategies," Machines, vol. 13, no. 8, Art. No. 714, 2025, doi: 10.3390/machines13080714.

S. Qulub, "Implementation of Condition-Based Maintenance (CBM) with FMEA approach to improve productivity of the auto insert machine in electronic component manufacturing," J. Appl. Res. Technol. Eng. (JARTE), vol. 6, no. 2, pp. 11–22, 2025, doi: 10.4995/jarte.2025.22952.

Xue, G. Jin, L. Tan, C. Zhang, and Y. Yu, “Predictive maintenance programs for aircraft engines based on remaining useful life estimation,” Scientific Reports, vol. 15, Art. no. 19957, 2025, doi: 10.21203/rs.3.rs-6732629/v1.