Product Durability/Reliability Design and Validation Based on Test Data Analysis

  • Zhigang Wei
  • Limin Luo
  • Fulun Yang
  • Burt Lin
  • Dmitri Konson
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


Better quality leads to less waste, improved competitiveness, higher customer satisfaction, higher sales and revenues, and eventually higher profitability. Meeting the quality and performance goals requires that decisions be based on reliable tests and quantitative test data analysis. Statistical process control (SPC) is such a fundamental quantitative approach to quality control and improvement. Walter Shewhart in 1920s and 1930s pioneered the use of statistical methods as a tool to manage and control production.


Probabilistic Density Function Failure Mode Statistical Process Control Lower Stress Level Design Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Prof. Kamran Nikbin, Prof. D. Gary Harlow, Mr. Kay Ellinghaus, Mr. Markus Pieszkalla, Mr. Marek Rybarz, Dr. Pierre Olivier Santacreu, Mr. Maleki Shervin, Mr. Herry Cheng, Mr. Tim Gardner, Mr. Joesph Berkemeier, and Mr. Richard Voltenburg for their helpful comments and contributions to works summarized in this chapter.


  1. ASME. (1969). Criteria of the ASME boiler and pressure vessel code for design by analysis in Sections III and VIII, Division 2. New York: The American Society of Mechanical Engineers.Google Scholar
  2. ASTM. (1962). ASTM manual on fitting straight lines, STP 313. ASTM International.Google Scholar
  3. ASTM. (1975). Chapter 3-planning S-N and response tests, in manual on statistical planning and analysis for fatigue experiments, STP 588. ASTM International.Google Scholar
  4. ASTM. (2010). Standard practice for statistical analysis of linear or linearized stress-life (\( S - N \)) and strain-life (\( \varepsilon - N \)) fatigue data. ASTM Designation: E739-10.Google Scholar
  5. Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.Google Scholar
  6. BS 7608. (1993). Code for practice for fatigue design and assessment of steel structures.Google Scholar
  7. Cashman, G. (2007). A statistical methodology for the preparation of a competing modes fatigue design curve. Journal of Engineering Materials and Technology, 129, 159–168.CrossRefGoogle Scholar
  8. Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In Proceedings of the International Conference on Electronics and Electrical Engineering (vol. 140, pp. 107–113).Google Scholar
  9. Harlow, D. G. (2011). Statistical characterization of bimodal behavior. Acta Materialia, 59, 5048–5053.CrossRefGoogle Scholar
  10. Jha, S. K., Larsen, J. M. M, & Rosenberger, A. H. (2009). Towards a physics-based description of fatigue variability behavior in probabilistic life-prediction. Engineering Fracture Mechanics, 76, 681–694Google Scholar
  11. Lee, Y. L., Pan, J., Hathaway, R., & Barkey, M. (2005). Fatigue testing and analysis: Theory and practice. Oxford: Elsevier, Butterworth-Heinemann. ISBN 978-0-12-385204-5.Google Scholar
  12. Lin, S. (2011). Exhaust system reliability evaluation. International Journal of Reliability, Quality and Safety Engineering, 18, 327–340.CrossRefGoogle Scholar
  13. Link, C. (1985). An equation for one-sided tolerance limits for normal distributions, research paper FPL 458 (pp. 1–4). Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory.Google Scholar
  14. Makam, S., Lee, Y. L., & Attibele, P. (2013). Estimation of one-sided lower tolerance limits for a Weibull distribution using the Monte Carlo pivotal simulation technique. SAE International Journal of Materials and Manufacturing, 6(3). doi: 10.4271/2013-01-0329.
  15. Meeker, W. Q., & Escobar, L. A. (1998). Statistical methods for reliability data. New York: Wiley Series in Probability and Statistics.MATHGoogle Scholar
  16. Natrella, M. (1966). Experimental statistics, handbook 91. National Bureau of Standards.Google Scholar
  17. Nelson, W. (2004). Accelerated testing: Statistical models, test plans, and data analysis. New York: Wiley.Google Scholar
  18. Neter, J., Wasserman, W., & Kutner, M. (1990). Applied linear statistical models. Homewood, IL: Richards D. Irwin Inc.Google Scholar
  19. Owen, D. (1968). A survey of properties and applications of the non-central t-distribution. Technometrics, 10, 445–472.MathSciNetMATHGoogle Scholar
  20. Pham, H. (Ed.). (2006). Springer handbook of engineering statistics. London: Springer.MATHGoogle Scholar
  21. Rice, R. C. (Ed.). (1997). SAE Fatigue design handbook (3rd ed., AE-22). Warrendale, PA: Society of Automotive Engineers, Inc.Google Scholar
  22. Ryan, B. F., Joiner, B. L., & Ryan, T. A. (1985). Minitab handbook (2nd ed.). Boston: Duxbury Press.Google Scholar
  23. Shen, C. L., Wirsching, P. H., & Cashman, G. T. (1996). Design curve to characterize fatigue strength. Journal of Engineering Materials and Technology, 118, 535–541.CrossRefGoogle Scholar
  24. Smith, A. F. M., & Gelfand, A. E. (1992). Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician, 46, 84–88.MathSciNetGoogle Scholar
  25. Wei, Z., Lin, B., Luo, L., Yang, F., & Dmitri, K. (2012a). Accelerated durability testing and data analysis for products with multiple failure mechanisms. International Journal of Reliability, Quality and Safety Engineering, 19, 1240003.CrossRefGoogle Scholar
  26. Wei, Z., Yang, F., Luo, L., Avery, K., & Dong, P. (2012b). Fatigue life assessment of welded structures with the linear traction stress analysis approach. SAE International Journal of Materials and Manufacturing, 5, 183–194.CrossRefGoogle Scholar
  27. Wei, Z., Yang, F., Lin, B., & Harlow, D. G. (2012c). Failure modes analysis of fatigue S-N test data with small sample size. In Proceedings of the 18th ISSAT International Conference on Reliability and Quality in Design, 26–28 July 2012, Boston, Massachusetts, USA.Google Scholar
  28. Wei, Z., Yang, F., Maleki, S., & Nikbin, K. (2012d). Equilibrium based curve fitting method for test data with nonuniform variance. In Proceedings of the ASME 2013 Pressure Vessels & Piping Division Conference, PVP2012-78234, 15–19 July 2012, Toronto, Canada.Google Scholar
  29. Wei, Z., Dogan, B., Luo, L., Lin, B., & Dmitri, K. (2013a). Design curve construction based on tolerance limit concept. Journal of Engineering Materials and Technology, 135, 014501.Google Scholar
  30. Wei, Z., Luo, L., Ellinghaus, K., Pieszkalla, M., Harlow, D.G., & Nikbin, K. (2013b). Statistical and probabilistic analysis of thermal-fatigue test data generated using V-shape specimen testing method. In Proceedings of the ASME 2013 Pressure Vessels & Piping Division Conference, PVP2013-97628, 14–18 July 2013, Paris, France.Google Scholar
  31. Wei, Z., Luo, L., Lin, B., Konson, D., & Nikbin, K. (2013c). Design curve construction based on Monte Carlo simulation. In Proceedings of the ASME 2013 Pressure Vessels & Piping Division Conference, PVP2013-97631, 14–18 July 2013, Paris, France.Google Scholar
  32. Wei, Z., Yang, F., Cheng, H., Maleki, S., & Nikbin, K. (2013d) Engineering failure data analysis:revisiting the standard linear approach. Engineering Failure Analysis, 30, 27–42.Google Scholar
  33. Yang, G. (1994). Optimum constant-stress accelerated life-test plans. IEEE Transactions on Reliability, 43, 575–581.CrossRefGoogle Scholar
  34. Yang, G. (2007). Life cycle reliability engineering. New Jersey: Wiley.CrossRefGoogle Scholar
  35. Yang, G., & Jin, L. (1994). Best compromise test plans for Weibull distributions with different censoring times. Quality and Reliability Engineering International, 10, 411–415.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Zhigang Wei
    • 1
  • Limin Luo
    • 1
  • Fulun Yang
    • 1
  • Burt Lin
    • 1
  • Dmitri Konson
    • 1
  1. 1.Tenneco Inc.Grass LakeUSA

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