Proportional Hazards Analysis of Electronic Component Reliability Data

  • J. M. Marshall
  • D. W. Wightman
  • S. J. Chester


This paper addresses the challenge of analysing electronic component reliability data arising in diverse environments and applications. Most reliability data has such potential associated explanatory variables, the electronic component reliability data analysed in this paper is no exception. MIL HDBK 217E accounts for these explanatory variables by incorporating n factors. These factors are determined a-priori and give the magnitude and effect of the explanatory variables.

The data analysed in this paper originates from the field failure of electronic components database held at Loughborough University of Technology (LUT). This database is part of a joint project funded by the British MOD, with participants from both electronics companies and academic institutions, namely, STC, GEC, Plessey, LUT, Nottingham Polytechnic and the Danish Engineering Academy (DIA).

The proportional Hazards Model (PHM) has been employed in an exploratory approach and includes such covariates as system operational environment and data source. This paper presents the results on the application of PHM to field data on Bipolar Transistors (T1A) and provides an insight into those factors which significantly influence the failure of these types of devices.


Electronic Component Component Type Bipolar Transistor Electronic Company Accelerate Life Test 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Campbell, D.S., Hayes, J.A. and Hetherington, D.R. The organisation of a study of the field failure of electronic components. Quality and Reliability Engineering International, 3 pp 251–258, 1987.Google Scholar
  2. 2.
    Marshall, J.M., Hayes, J.A., Campbell, D.S., Bendell, A. An Electronic Component Reliability Database. 10th Arts Bradford, pp 40 53, 1988.Google Scholar
  3. 3.
    Marshall, J.M., Hayes, J.A., Campbell, D.S., Bendell, A. The Analysis of Electronic Component Reliability Data. 6th Euredata Siena, pp 286–309, 1989.Google Scholar
  4. 4.
    . Marshall, J.M. The Organisation and Statistical An alysis of an Electronic Component Field Failure Database. PHD Thesis, Loughborough University of Technology1990.Google Scholar
  5. 5.
    Wightman, D. and Bendell, A. The Practical Applications of Proportional Hazards Modelling. Reliability Engineering15, 1986.Google Scholar
  6. 6.
    Cox, D.R. Regression models and life tables (with discussion). J.R. Stat. Soc, b34 1974.Google Scholar
  7. 7.
    Wightman, D.W. The Application of Proportional Hazards Modelling to Reliability Problems.PhD Thesis, Trent Polytechnic1987.Google Scholar
  8. 8.
    Mil Handbook 217E. Reliability Prediction of Electronic Equipment. October 1986.Google Scholar
  9. 9.
    Landers, T.L. and Kolarik, W.J. Proportional Hazards Models and MIL HDBK 217. Microelectronics and Reliability 26 No.4.Google Scholar
  10. 10.
    Ascher, H.E. The use of regression techniques for matching reliability models to the real world. Software System Design Methods - The Challenge of Advanced Computing Technology. Springer - Verlag.Google Scholar
  11. 11.
    Chester, S.J. and Bendell, A. A discussion of Usage information within the LUT Electronic Component. Database. Internal Report, Nottingham Polytechnic, 1989.Google Scholar
  12. 12.
    Bryson, M.C. and Johnson, M.E. The incidence of monotone likelihood in the Cox model. Technometrics Vol. 23, 381–383, 1981.Google Scholar
  13. 13.
    Cox, D.R. and Snell, E.J. A general definition of residuals (with discussion). J.R. Statist.Soc., B.30, 248–275, 1968.Google Scholar
  14. 14.
    Cox, D.R.Analysis of Binary Data. Methuen, London.Google Scholar
  15. 15.
    Clayton, D and Curick, J. The EM algorithm for Cox’s regression model using GLIM.Appl. Statist, 34 No.2 148–156, 1985.Google Scholar
  16. 16.
    Breslow’s Discusion: Cox, D.R. Regression model and life-tables (with discussion). J.R.Statist.Soc.B.34 187–220, 1972.Google Scholar
  17. 17.
    Breslow, N. E. Covariate analysis of censored survival data. Biometrics, 30 89–99, 1974.Google Scholar
  18. 18.
    Kalbfleisch, J.D. and Prentice, R.L. The Statistical Analysis of Failure Time Data.John Wiley and Sons, Chichester, 1980.Google Scholar
  19. 19.
    Kalbfleisch, J.D. and Prentice, R.L. Marginal likelihoods based on Cox’s regression and life model.Biometrika, 60.267–278, 1973.Google Scholar
  20. 20.
    Kay,R. Proportional hazards regression models and the analysis of censored survival data. Appl. Statist.26, 227, 237, 1977.Google Scholar
  21. 21.
    Walker, E.V.Google Scholar
  22. 22.
    Schoenfeld, D. Partial residuals for the proportional hazards regression model. Biometrika, 69.239–241, 1982.Google Scholar
  23. 23.
    Cain, K.C. and Lange, N.T. Approximate case influence for the proportional hazards regression model with censored data. Biometrics40, 493–499, 1984.Google Scholar
  24. 24.
    Reid, N and Chapman, H. Influence functions for proportional hazards regression. Biometrika, 72 1–9, 1985.Google Scholar

Copyright information

© Elsevier Science Publishers Ltd 1990

Authors and Affiliations

  • J. M. Marshall
    • 1
  • D. W. Wightman
    • 1
  • S. J. Chester
    • 1
  1. 1.Department of Mathematics, Statistics and Operational ResearchNottingham PolytechnicNottinghamUK

Personalised recommendations