Bivariate constant stress degradation model: Led lighting system reliability estimation with two-stage modelling

Quality and Reliability Engineering International

Volumn 25, Issue 8, 2009, Pages 1067-1084

  Sari, J K ^a,b,d   Newby, M J ^b,c   Brombacher, A C ^b   Tang, L C ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^c CITY UNIVERSITY   (United Kingdom)

^d Knowledge Centre Wind Turbine Materials and Constructions   (Netherlands)

Author keywords

Bivariate CSDT; Copula function; Generalized linear model; Led lighting system

Indexed keywords

BIVARIATE; CONSTANT STRESS; COPULA FUNCTIONS; DEGRADATION FAILURE; DEGRADATION TESTING; EXPERIMENT DATA; GENERALIZED LINEAR MODEL; LED LAMPS; LED LIGHTING SYSTEM; LIGHTING SYSTEMS; LIGHTING TECHNOLOGY; LONG LIFETIME; MAIN COMPONENT; SYSTEM RELIABILITY; SYSTEM'S PERFORMANCE; TWO STAGE;

CORRELATION THEORY; DEGRADATION; DISTRIBUTION FUNCTIONS; LIGHTING; LIGHTING FIXTURES; RELIABILITY;

LIGHT EMITTING DIODES;

EID: 73449108422     PISSN: 07488017     EISSN: 10991638     Source Type: Journal    
DOI: 10.1002/qre.1022     Document Type: Article

References (32)

1. Wiedmann T, Minx J. A definition of ̀carbon footprint', integrated sustainability analysis™ U.K. Research Report 07-101, Durham, U.K., June 2007.
2. Energy Information Administration. International Energy Outlook 2007, Report #DOE/EIA-0484(2007), 2007.
3. Narendran N, Deng L, Pysar RM, Gu Y, Yu H. Performance characteristics of high-power light-emitting diodes. Third International Conference on Solid State Lighting, Proceedings of SPIE, San Diego, vol.5187, 2004; 267-275.
4. Meeker WQ, Escobar LA, Lu CJ. Accelerated degradation tests: Modelling and analysis. Technometrics 1998; 40(2): 89-99.
5. Lu CJ, Meeker WQ. Using degradation measures to estimate a time-to-failure distribution. Technometrics 1993; 35(2):161-174.
6. Chen Z, Zheng S. Lifetime distribution based degradation analysis. IEEE Transactions on Reliability 2005; 54(1):3-10.
7. Lu CJ, Meeker WQ, Escobar LA. A comparison of degradation and failure-time analysis methods for estimating a time-to-failure distribution. Statistica Sinica 1996; 6:531-546.
8. Oliveira VRB, Colosimo EA. Comparison of methods to estimate the time-to-failure distribution in degradation tests. Quality and Reliability Engineering International 2004; 20:363-373.
9. Lin TI, Lee JC. On modelling data from degradation sample path over time. Australia and New Zealand Journal of Statistics 2003; 45(3):257-270.
10. Tang LC, Chang DS. Reliability prediction using nondestructive accelerated-degradation data: Case study on power supplies. IEEE Transactions on Reliability 1995; 44(4):562-566.
11. Yang G. Accelerated degradation-tests with tightened critical values. IEEE Transactions on Reliability 2002; 51(4): 463-468.
12. Tseng ST, Wen ZC. Step-stress accelerated degradation analysis for highly reliable products. Journal of Quality Technology 2000; 32(3):209-216.
13. Tang LC, Yang GY, Xie M. Planning of step-stress accelerated degradation test. Proceedings of the Annual Reliability and Maintainability Symposium, Los Angeles, 2004; 287-292.
14. Crk V. Reliability assessment from degradation data. 2000 Proceedings Annual Reliability and Maintainability Symposium, Los Angeles, 2000; 155-161.
15. Huang W, Askin RG. Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation. Quality Reliability Engineering International 2003; 19:241-254.
16. Zhao W, Elsayed EA. An accelerated life testing involving performance degradation. 2004 Proceedings Annual Reliability and Maintainability Symposium, Los Angeles, 2004; 324-329.
17. Wang P, Coit DW. Reliability prediction based on degradation modelling for systems with multiple degradation measures. 2004 Proceedings Annual Reliability and Maintainability Symposium, Los Angeles, 2004; 302-307.
18. Bagdonavicius V, Bikelis A, Kazakevicius V. Statistical analysis of linear degradation and failure time data with multiple failure modes. Lifetime Data Analysis 2004; 10:65-81.
19. Xu D, Zhao W. Reliability prediction using multivariate degradation data. 2005 Proceedings Annual Reliability and Maintainability Symposium, Alexandria, 2005; 337-341.
20. Crowder MJ. Classical Competing Risks. Chapman & Hall: U.S.A., 2001.
21. Tsiatis A. A nonidentifiability aspect of the problem of competing risks. Proceedings of the National Academy of Sciences of the United States of America 1975; 72(1):20-22.
22. McCullagh P, Nelder JA. Generalized Linear Models (2nd edn). Chapman 7amp; Hall: Cambridge, 1989.
23. Lu J, Park J, Yang Q. Statistical inference of a time-to-failure distribution derived from linear degradation data. Technometrics 1997; 39(4):391-400.
24. Wu S, Shao J. Reliability analysis using the least squares method in nonlinear mixed-effect degradation model. Statistica Sinica 1999; 9:855-877.
25. Nelsen RB. Lecture Notes in Statistics: An Introduction to Copulas. Springer: New York, 1999.
26. Joe H. Multivariate Models and Dependence Concepts. Chapman & Hall: London, 1997.
27. Joe H, Xu JJ. The estimation method of inference functions for margins for multivariate models. Technical Report, No.166, Department of Statistics, University of British Columbia, Canada, 1996.
28. Xu JJ. Statistical modelling and inference for multivariate and longitudinal discrete response data. PhD Thesis, Department of Statistics, University of British Columbia, 1996.
29. Joe H. Asymptotic efficiency of the two-stage estimation method for copula based models. Journal of Multivariate Analysis 2005; 94:401-419.
30. Andersen EW. Two-stage estimation in copula models used in family studies. Lifetime Data Analysis 2005; 11:333-350.
31. Bullough JD. ASSIST recommends...LED Life for General Lighting. Lighting Research Center, Ransselaer Polytechnic Institute: Troy, Revision April, 2006.
32. Lumileds P. Understanding Power LED Lifetime Analysis, Technology White Paper, Philips Lumileds Lighting Company, San Jose, 2007.