SavedAdd your journal

Journals / Researcher / 2026 / Cilt: 04 Sayı: 01

Parametric and Non-Parametric Reliability Analysis of The Propeller Unit of an Aircraft Fleet

Bir Uçak Filosunun Pervane Ünitesinin Parametrik ve Parametrik Olmayan Güvenilirlik Analizi

Mahmut Sami Şaşmaztürk(İSKENDERUN TEKNİK ÜNİVERSİTESİ)0000-0001-6812-5799
Abdulsamet Ertem0009-0001-7435-2362
Selda Kapan Ulusoy(ERCIYES UNIVERSITY)0000-0001-5604-0448
Journal
Researcher
Pages
94–106
Publication Date
2024-07-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Reliability measures are estimated using both models and results are compared to evaluate the existing preventive maintenance plan.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Preventive maintenance is performed to sustain the safe and reliable operation of industrial equipments.
📊
Key Findings & Data:In order to plan preventive maintenance or evaluate the existing maintenance plan, the failure behavior of the system must be modeled.
💡
Conclusion & Impact:Reliability measures are estimated using both models and results are compared to evaluate the existing preventive maintenance plan.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:repairable system reliability · MCF · counting process

Abstract

Preventive maintenance is performed to sustain the safe and reliable operation of industrial equipments. In order to plan preventive maintenance or evaluate the existing maintenance plan, the failure behavior of the system must be modeled. The failure behavior of a repairable system is modeled utilizing counting processes. In this study failure behavior of propellers belonging to a small aircraft fleet is modeled. First non-parametric estimate of population mean cumulative function (MCF) is obtained. MCF helps discovering the special features of the maintenance data. The parametric model selection depends on the result of the trend analysis of the time between failures. In the second part of the study trend analysis is performed on propeller maintenance data. Based on the trend analysis two prospect parametric models are selected. Reliability measures are estimated using both models and results are compared to evaluate the existing preventive maintenance plan.

Özet

Endüstriyel ekipmanların güvenli ve güvenilir çalışmasını sürdürmek için önleyici bakım yapılır. Önleyici bakımı planlamak veya mevcut bakım planını değerlendirmek için sistemin arıza davranışının modellenmesi gerekir. Onarılabilir bir sistemin arıza davranışı, sayma işlemleri kullanılarak modellenir. Bu çalışmada küçük bir uçak filosuna ait pervanelerin arıza davranışı modellenmiştir. Popülasyon ortalama kümülatif fonksiyonunun (MCF) ilk parametrik olmayan tahmini elde edilir. MCF, bakım verilerinin özel özelliklerinin keşfedilmesine yardımcı olur. Parametrik model seçimi, arızalar arasındaki sürenin trend analizinin sonucuna bağlıdır. Çalışmanın ikinci bölümünde pervane bakım verileri üzerinde trend analizi yapılmıştır. Trend analizine dayanarak iki olası parametrik model seçilir. Güvenilirlik ölçümleri her iki model kullanılarak tahmin edilir ve mevcut koruyucu bakım planını değerlendirmek için sonuçlar karşılaştırılır.

🔖

Cite This Article

Şaşmaztürk, M. S., Ertem, A., Ulusoy, S. K. (2024). Parametric and Non-Parametric Reliability Analysis of The Propeller Unit of an Aircraft Fleet. Researcher, Cilt: 04 Sayı: 01, 94–106. https://ajindex.com/articles/1537240

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (34)

  1. P. K. Andersen, O. Borgan, R. D. Gill, and N. Keiding, Statistical models based on counting processes. Springer Science & Business Media, 2012.
  2. S. E. Rigdon and A. P. Basu, "Statistical methods for the reliability of repairable systems," (No Title), 2000.
  3. Z.-S. Ye, M. Xie, and L.-C. Tang, "Reliability evaluation of hard disk drive failures based on counting processes," Reliability Engineering & System Safety, vol. 109, pp. 110-118, 2013.
  4. X. Yang, Y. He, R. Liao, Y. Cai, and W. Dai, "Mission reliability-centered opportunistic maintenance approach for multistate manufacturing systems," Reliability Engineering & System Safety, vol. 241, p. 109693, 2024.
  5. S. Majumdar, "Study on reliability modelling of a hydraulic excavator system," Quality and Reliability Engineering International, vol. 11, no. 1, pp. 49-63, 1995.
  6. Y. S. Huang, C. C. Fang, and S. Wijaya, "Condition‐based preventive maintenance with a yield rate threshold for deteriorating repairable systems," Quality and Reliability Engineering International, vol. 38, no. 8, pp. 4122-4140, 2022.
  7. S. Ali, "Time‐between‐events monitoring using nonhomogeneous Poisson process with power law intensity," Quality and Reliability Engineering International, vol. 37, no. 8, pp. 3157-3178, 2021.
  8. J. Cahoon, K. Sanborn, and A. Wilson, "Practical reliability growth modeling," Quality and Reliability Engineering International, vol. 37, no. 7, pp. 3108-3124, 2021.
  9. Y. Li, X. Zhang, Y. Ran, and G. Zhang, "Reliability modeling and analysis for CNC machine tool based on meta‐action," Quality and Reliability Engineering International, vol. 37, no. 4, pp. 1451-1467, 2021.
  10. U. Said and S. Taghipour, "Modeling failure process and quantifying the effects of multiple types of preventive maintenance for a repairable system," Quality and Reliability Engineering International, vol. 33, no. 5, pp. 1149-1161, 2017.
  11. M. Kijima, "Some results for repairable systems with general repair," Journal of Applied probability, vol. 26, no. 1, pp. 89-102, 1989.
  12. N. Jack, "Analysing event data from a repairable machine subject to imperfect preventive maintenance," Quality and Reliability Engineering International, vol. 13, no. 4, pp. 183-186, 1997.
  13. J.-f. Guo, Z.-y. Rui, R.-c. Feng, and X.-c. Wei, "Imperfect preventive maintenance for numerical control machine tools with log-linear virtual age process," Journal of Central South University, vol. 21, no. 12, pp. 4497-4502, 2014.
  14. P. D. Van and C. Bérenguer, "Condition‐based maintenance with imperfect preventive repairs for a deteriorating production system," Quality and Reliability Engineering International, vol. 28, no. 6, pp. 624-633, 2012.
  15. W. Kahle, "Imperfect repair in degradation processes: A Kijima‐type approach," Applied Stochastic Models in Business and Industry, vol. 35, no. 2, pp. 211-220, 2019.
  16. R. de Queiroz Souza and A. J. Álvares, "FMEA and FTA analysis for application of the reliability centered maintenance methodology: Case study on hydraulic turbines," in ABCM Symposium Series in Mechatronics, 2008, vol. 3, pp. 803-812.
  17. T. R. Fleming and D. P. Harrington, Counting processes and survival analysis. John Wiley & Sons, 2013.
  18. M. Rausand and A. Hoyland, System reliability theory: models, statistical methods, and applications. John Wiley & Sons, 2003.
  19. W. Kuo and M. J. Zuo, Optimal reliability modeling: principles and applications. John Wiley & Sons, 2003.
  20. A. Chehade, Z. Shi, and V. Krivtsov, "Power–law nonhomogeneous Poisson process with a mixture of latent common shape parameters," Reliability Engineering & System Safety, vol. 203, p. 107097, 2020.
  21. V. V. Krivtsov, "Practical extensions to NHPP application in repairable system reliability analysis," Reliability Engineering & System Safety, vol. 92, no. 5, pp. 560-562, 2007.
  22. Z. G. Asfaw and B. H. Lindqvist, "Unobserved heterogeneity in the power law nonhomogeneous Poisson process," Reliability Engineering & System Safety, vol. 134, pp. 59-65, 2015.
  23. W. B. Nelson, Recurrent events data analysis for product repairs, disease recurrences, and other applications. SIAM, 2003.
  24. Y. Tang, J. Fu, W. Liu, and A. Xu, "Bayesian analysis of repairable systems with modulated power law process," Applied Mathematical Modelling, vol. 44, pp. 357-373, 2017.
  25. S.-H. Sheu, T.-H. Liu, Z. G. Zhang, and Y.-H. Chien, "Extended optimal replacement policy for a two-unit system with shock damage interaction," IEEE Transactions on reliability, vol. 64, no. 3, pp. 998-1014, 2015.
  26. N. K. Srivastava and S. Mondal, "Development of predictive maintenance model for N-component repairable system using NHPP models and system availability concept," Global Business Review, vol. 17, no. 1, pp. 105-115, 2016.
  27. M. P. Kaminskiy and V. V. Krivtsov, "G-renewal process as a model for statistical warranty claim prediction," in Annual reliability and maintainability symposium. 2000 Proceedings. International symposium on product quality and integrity (Cat. No. 00CH37055), 2000, pp. 276-280: IEEE.
  28. K. D. Majeske, "A non-homogeneous Poisson process predictive model for automobile warranty claims," Reliability Engineering & System Safety, vol. 92, no. 2, pp. 243-251, 2007.
  29. J. Duane, "Learning curve approach to reliability monitoring," IEEE transactions on Aerospace, vol. 2, no. 2, pp. 563-566, 1964.
  30. L. H. Crow, "An extended reliability growth model for managing and assessing corrective actions," in Annual Symposium Reliability and Maintainability, 2004-RAMS, 2004, pp. 73-80: IEEE.
  31. J. Crocker, "Effectiveness of maintenance," Journal of Quality in Maintenance Engineering, vol. 5, no. 4, pp. 307-314, 1999.
  32. A. Mettas and W. Zhao, "Modeling and analysis of repairable systems with general repair," in Annual Reliability and Maintainability Symposium, 2005. Proceedings., 2005, pp. 176-182: IEEE.
  33. J. Dagpunar, "Some properties and computational results for a general repair process," Naval Research Logistics (NRL), vol. 45, no. 4, pp. 391-405, 1998.
  34. W. Q. Meeker, L. A. Escobar, and F. G. Pascual, Statistical methods for reliability data. John Wiley & Sons, 2022.