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A novel reliability model for multi-component systems subject to multiple dependent competing risks with degradation rate acceleration

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Warianty tytułu
PL
Nowatorski model niezawodności dla systemów wieloelementowych narażonych na liczne zależne ryzyka konkurujące uwzględniający przyspieszenie tempa degradacji
Języki publikacji
EN
Abstrakty
EN
The purpose of this paper is to establish a new reliability model of the system subject to multiple dependent competing risks. For a system subject to multiple dependent competing risks, the total degradation consists of natural degradation amount and sudden degradation increments (SDIs) caused by random shocks arriving at the system. Most researchers on this topic only focus on the SDIs. However, the impact of random shocks on degradation rate is ignored. In this paper, a novel reliability model considering degradation rate acceleration (DRA) caused by random shocks is proposed, in which the degradation model is based on the degradation path. The dependence relationship between multiple degradation processes is dealt with by copula method, and the arrival time of shocks is assumed to follow a non-homogeneous Poisson process (NHPP). Finally, the effectiveness of the proposed reliability model is demonstrated by an example of a series system. Moreover, the effect of model parameters is evaluated through sensitivity analysis.
PL
Celem niniejszej pracy było stworzenie nowego modelu niezawodności systemu narażonego na liczne zależne ryzyka konkurujące. W przypadku systemu eksponowanego na wiele zależnych ryzyk konkurujących, na wartość całkowitą degradacji składa się wartość degradacji naturalnej oraz wartość nagłych przyrostów degradacji (sudden degradation increments, SDI) powodowanych przez losowe zaburzenia systemu. Większość badaczy tej tematyki koncentruje się wyłącznie na SDI, ignorując tym samym wpływ zaburzeń losowych na tempo degradacji. W niniejszym artykule zaproponowano nowy model niezawodności uwzględniający przyspieszenie tempa degradacji powodowane zaburzeniami losowymi, w którym model degradacji opiera się na krzywej degradacji. Zależność między mnogimi procesami degradacji rozpatrywano za pomocą metody funkcji kopuły przy założeniu, że czas wystąpienia zaburzenia odpowiada niejednorodnemu procesowi Poissona. Skuteczność proponowanego modelu niezawodności zademonstrowano na przykładzie systemu szeregowego. Ponadto, wykorzystano analizę czułości do oceny wpływu parametrów modelu na niezawodność systemu.
Rocznik
Strony
579--589
Opis fizyczny
Bibliogr. 32 poz, rys.
Twórcy
autor
  • School of Economics and Management Nanjing University of Science and Technology No. 200 Xiaolingwei, Xuanwu District, Nanjing, Jiangsu Province, China
autor
  • School of Economics and Management Nanjing University of Science and Technology No. 200 Xiaolingwei, Xuanwu District, Nanjing, Jiangsu Province, China
autor
  • School of Economics and Management Nanjing University of Science and Technology No. 200 Xiaolingwei, Xuanwu District, Nanjing, Jiangsu Province, China
autor
  • College of Economics and Management Nanjing University of Aeronautics and Astronautics No. 29 General Avenue, Jiangning District, Nanjing, Jiangsu Province, China
Bibliografia
  • 1. An Z, Sun D. Reliability modeling for systems subject to multiple dependent competing failure processes with shock loads above a certain level. Reliability Engineering and System Safety 2017; 157: 129-138, https://doi.org/10.1016/j.ress.2016.08.025.
  • 2. Bocchetti D, Giorgio M, Guida M. A competing risk model for the reliability of cylinder liners in marine diesel engines. Reliability Engineering and System Safety 2009; 94(8): 1299-1307, https://doi.org/10.1016/j.ress.2009.01.010.
  • 3. Bae S J, Kuo W, Kvam P H. Degradation models and implied lifetime distributions. Reliability Engineering and System Safety 2007; 92(5), 601-608, https://doi.org/10.1016/j.ress.2006.02.002.
  • 4. Barlow R E, Proschan F. Statistical theory of reliability and life testing: probability models. Florida: Florida State University Tallahassee, 1975.
  • 5. Cha J H, Pulcini G. A dependent competing risks model for technological units subject to degradation phenomena and catastrophic failures. Quality and Reliability Engineering International 2016; 32(2), 505-517, https://doi.org/10.1002/qre.1767.
  • 6. Durham S D, Padgett W J. Cumulative damage models for system failure with application to carbon fibers and composites. Technometrics 1997; 39(1): 34-44, https://doi.org/10.1080/00401706.1997.10485437.
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  • 8. Guo C, Wang W, Guo B. Maintenance optimization for systems with dependent competing risks using a copula function. Eksploatacja I Niezawodnosc - Maintenance and Reliability 2013; 15(1): 9-17.
  • 9. Huynh K T, Castro I T, Barros A, Berenguer C. Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks. European Journal of Operational Research 2012; 218(1): 140-151, https://doi.org/10.1016/j.ejor.2011.10.025.
  • 10. Jiang R. Discrete competing risk model with application to modeling bus-motor failure data. Reliability Engineering and System Safety 2010; 95(9): 981-988, https://doi.org/10.1016/j.ress.2010.04.009.
  • 11. Jiang L, Feng Q, Coit D W. Reliability and maintenance modeling for dependent competing failure processes with shifting failure thresholds. IEEE Transactions on Reliability 2012; 61(4): 932-948, https://doi.org/10.1109/TR.2012.2221016.
  • 12. Jiang L, Feng Q, Coit D W. Modeling zoned shock effects on stochastic degradation in dependent failure processes. IIE Transactions 2015; 47(5): 460-470, https://doi.org/10.1080/0740817X.2014.955152.
  • 13. Jiang C, Wang D. A sort of multiple life model under dependent causes of decrement. Statistical and Application 2015; 4(3): 169-175, https://doi.org/10.12677/SA.2015.43019.
  • 14. Li W, Pham H. An inspection-maintenance model for systems with multiple competing processes. IEEE Transactions on Reliability 2005; 54(2): 318-327, https://doi.org/10.1109/TR.2005.847264.
  • 15. Liu Z, Ma X, Shen L, Zhao Y. Degradation-shock-based reliability models for fault-tolerant systems. Quality and Reliability Engineering International 2016; 32(3), 949-955, https://doi.org/10.1002/qre.1805.
  • 16. Mann N R, Schafer R E, and Singpurwalla N D. Methods for Statistical Analysis of Reliability and Life Data. New York: John Wiley & Sones, 1974.
  • 17. Meeker W Q, Escobar L A. Statistical Methods for Reliability Data. New York: John Wiley & Sons, 1998.
  • 18. Nelsen R B. An introduction to copulas. New York: Springer Science & Business Media, 2006.
  • 19. Onar A, Padgett W J. Inverse Gaussian accelerated test models based on cumulative damage. Journal of Statistical Computation and Simulation 2000; 66(3): 233-247, https://doi.org/10.1080/00949650008812024.
  • 20. Peng H, Feng Q, Coit D W. Reliability and maintenance modeling for systems subject to multiple dependent competing failure processes. IIE Transactions 2010; 43(1): 12-22, https://doi.org/10.1080/0740817X.2010.491502.
  • 21. Padgett W J, Durham S D, and Mason A M. Weibull analysis of the strength of carbon fibers using linear and power law models for the length effect. Journal of Composite Materials 1995; 29(14): 1873-1884, https://doi.org/10.1177/002199839502901405.
  • 22. Park C, Padgett W J. Stochastic degradation models with several accelerating variables. IEEE Transactions on Reliability 2006; 55(2): 379-390, https://doi.org/10.1109/TR.2006.874937.
  • 23. Park C, Padgett W J. New cumulative damage models for failure using stochastic processes as initial damage. IEEE Transactions on Reliability 2005; 54(3): 530-540, https://doi.org/10.1109/TR.2005.853278.
  • 24. Song S, Coit D W, Feng Q, Peng H. Reliability analysis for multi-component systems subject to multiple dependent competing failure processes. IEEE Transactions on Reliability 2014; 63(1): 331-345, https://doi.org/10.1109/TR.2014.2299693.
  • 25. Song S, Coit D W, Feng Q. Reliability for systems of degrading components with distinct component shock sets. Reliability Engineering and System Safety 2014; 132: 115-124, https://doi.org/10.1016/j.ress.2014.06.020.
  • 26. Song S, Coit D W, Feng Q. Reliability analysis of multiple-component series systems subject to hard and soft failures with dependent shock effects. IIE Transactions 2016; 48(8), 720-735, https://doi.org/10.1080/0740817X.2016.1140922.
  • 27. Tanner D M, Walraven J A, Helgesen K, Irwin L W, Brown F, Smith N F, Masters N. MEMS reliability in shock environments. Proceedings of IEEE International Reliability Physics Symposium 2000; 129-138, https://doi.org/10.1109/RELPHY.2000.843903.
  • 28. Wang Z, Huang H Z, Li Y. An approach to reliability assessment under degradation and shock process. IEEE Transactions on Reliability 2011; 60(4): 852-863, https://doi.org/10.1109/TR.2011.2170254.
  • 29. Wang Y, Pham H. A multi-objective optimization of imperfect preventive maintenance policy for dependent competing risk systems with hidden failure. IEEE Transactions on Reliability 2011; 60(4): 770-781, https://doi.org/10.1109/TR.2011.2167779.
  • 30. Wang Y, Pham H. Modeling the dependent competing risks with multiple degradation processes and random shock using time-varying copulas. IEEE Transactions on Reliability 2012; 61(1):13-22, https://doi.org/10.1109/TR.2011.2170253.
  • 31. W. Nelson. Accelerated Testing: Statistical Models, Test Plans, and Data Analyses. New York: John Wiley & Sons, 1990, https://doi.org/10.1002/9780470316795.
  • 32. Zhu Y, Elsayed E A, Liao H. Availability optimization of systems subject to competing risk. European Journal of Operational Research 2010; 202(3): 781-788, https://doi.org/10.1016/j.ejor.2009.06.008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-74e8e603-75a5-4ef3-9977-7a11ec1b4459
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