Skip to main content
Springer Nature Link
Log in

Degradation Modeling, Analysis, and Applications on Lifetime Prediction

  • Chapter
  • First Online:
Statistical Modeling for Degradation Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

Degradation signals provide more information for product life status than failure data, when specific degradation mechanism can be identified. Modeling and analysis with the degradation signal is helpful to extrapolate for product lifetime prediction. In this chapter, comprehensive review has been conducted for different kinds of modeling and analysis approaches, together with the corresponding lifetime prediction results. Furthermore, discussions over related issues like product initial performance are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 89.00
Price excludes VAT (USA)
Softcover Book
USD 119.99
Price excludes VAT (USA)
Hardcover Book
USD 119.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nelson W (1981) Analysis of performance-degradation data from accelerated tests. IEEE Trans Reliab 30(2):149–155

    Article  MATH  Google Scholar 

  2. Wang FK, Chu TP (2012) Lifetime predictions of led-based light bars by accelerated degradation test. Microelectron Reliab 52(7):1332–1336

    Article  Google Scholar 

  3. Escobar LA, Meeker WQ, Kugler DL, Kramer LL (2015) Accelerated destructive degradation tests: data, models and analysis. Math Stat Methods Reliab:319–337

    Google Scholar 

  4. Shiau JJH, Lin HH (1999) Analyzing accelerated degradation data by nonparametric regression. IEEE Trans Reliab 48(2):149–158

    Article  Google Scholar 

  5. Rohner M, Kerber A, Kerber M (2006) Voltage acceleration of TBD and its correlation to post breakdown conductivity of N- and P-Channel MOSFETs. IEEE Int Reliab Phys Symp Proc:76–81

    Google Scholar 

  6. Zhang J, Li W, Cheng G, Chen X, Wu H, Shen MHH (2014) Life prediction of OLED for constant-stress accelerated degradation tests using luminance decaying model. J Lumin 154:491–495

    Article  Google Scholar 

  7. Guan Q, Tang Y, Xu A (2015) Objective Bayesian analysis accelerated degradation test based on Wiener process models. Appl Math Model 40(4):2743–2755

    Article  MathSciNet  Google Scholar 

  8. Pan Z, Balakrishnan N, Sun Q (2011) Bivariate constant-stress accelerated degradation model and inference. Commun Stat Simul Comput 40(2):259–269

    Article  MathSciNet  MATH  Google Scholar 

  9. Wan F, Zhang C, Tan Y, Chen X (2014) Data analysis and reliability estimation of step-down stress accelerated degradation test based on Wiener process. Prog Sys Health Manage Conf:41–45

    Google Scholar 

  10. Pan Z, Sun Q (2014) optimal design for step-stress accelerated degradation test with multiple performance characteristics based on Gamma processes. Commun Stat Simul Comput 43(2):298–314

    Article  MathSciNet  MATH  Google Scholar 

  11. Ge Z, Li XY, Jiang TM, Huang TT (2011) optimal design for step-stress accelerated degradation testing based on D-optimality. Reliab Maint Symp:1–6

    Google Scholar 

  12. Guo CS (2011) Online degradation model based on process-stress accelerated test. Acta Phys Sin 60:128501

    Google Scholar 

  13. Peng CY, Tseng ST (2010) Progressive-stress accelerated degradation test for highly-reliable products. IEEE Trans Reliab 59(1):30–37

    Article  Google Scholar 

  14. Boulanger M, Escobar LA (1994) Experimental design for a class of accelerated degradation tests. Technometrics 36(3):260–272

    Article  MATH  Google Scholar 

  15. Park C, Padgett WJ (2005) Accelerated degradation models for failure based on geometric Brownian motion and Gamma processes. Lifetime Data Anal 11(4):511–527

    Article  MathSciNet  MATH  Google Scholar 

  16. Tang S, Guo X, Yu C, Xue H, Zhou Z (2014) Accelerated degradation tests modeling based on the nonlinear Wiener process with random effects. Math Probl Eng 2014(2):1–11

    Google Scholar 

  17. Sang BK, Kim SH (2015) Estimation of elevator wire life using accelerated degradation model. J Korean Soc Qual Manag 43(3):409–420

    Article  Google Scholar 

  18. Liu HC (2014) Gauss-power law accelerated degradation model about reliability assessment of electronic products. Journal of Guiyang University

    Google Scholar 

  19. Yang Z, Chen YX, Li YF, Zio E, Kang R (2013) Smart electricity meter reliability prediction based on accelerated degradation testing and modeling. Int J Elect Power Energy Syst 56(3):209–219

    Google Scholar 

  20. Hayes KC, Wolfe DL, Trujillo SA, Burkell JA (2010) On the interaction of disability and aging: accelerated degradation models and their influence on projections of future care needs and costs for personal injury litigation. Disabil Rehabil 32(5):424–428

    Article  Google Scholar 

  21. Yang G, Zaghati Z (2006) Accelerated life tests at higher usage rates: a case study. Reliability and maintainability symposium, 313–317

    Google Scholar 

  22. Yellamati D, Arthur E, James S, Morris G, Heydt T, Graf E (2013) Predictive reliability models for variable frequency drives based on application profiles. 2013 Proceedings Reliability and Maintainability Symposium (RAMS), 1–6

    Google Scholar 

  23. Hwang DH, Park JW, Jung JH (2011) A study on the lifetime comparison for electric double layer capacitors using accelerated degradation test. International Conference on Quality, Reliability, Risk, Maintenance and Safety Engineering, 302–307

    Google Scholar 

  24. Shen CN, Xu JJ, Chao MC (2014) A study for the relationship between drive level and the activation energy in arrhenius accelerated aging model for small size quartz resonators. 2014 IEEE International Frequency Control Symposium (FCS), 1–3

    Google Scholar 

  25. Cooper MS (2005) Investigation of arrhenius acceleration factor for integrated circuit early life failure region with several failure mechanisms. IEEE Trans Compon Packag Technol 28(3):561–563

    Article  Google Scholar 

  26. Zhou J, Yao J, Song Y, Hu HH (2014) The step-down-stress accelerated storage testing evaluation methods of small sample electronic products based on arrhenius model. 2014 10th international conference on reliability, maintainability and safety (ICRMS), 908–912

    Google Scholar 

  27. Dai HF, Zhang XL, Gu WJ, Wen XZ, Sun ZC (2013) A semi-empirical capacity degradation model of ev li-ion batteries based on eyring equation. IEEE Vehicle Power and Propulsion Conference (VPPC), 1–5

    Google Scholar 

  28. Endicott H, Hatch B, Sohmer R (1965) Application of the eyring model to capacitor aging data. IEEE Trans Compon Parts 12(1):34–41

    Article  Google Scholar 

  29. Meeker WQ, Escobar LA (1998) Statistical methods for reliability data

    Google Scholar 

  30. Wu EY, Sune J (2009) On voltage acceleration models of time to breakdown-part I: experimental and analysis methodologies. IEEE Trans Electron Devices 56(7):1433–1441

    Google Scholar 

  31. Hu LC, Kang AC, Wu TY, Shih JR, Lin YF, Wu K, King YC (2006) Efficient low-temperature data retention lifetime prediction for split-gate flash memories using a voltage acceleration methodology. IEEE Trans Device Mater Reliab 6(4):528–533

    Article  Google Scholar 

  32. Padgett WJ, Durham SD, Mason AM (1995) Weibull analysis of the strength of carbon fibers using linear and power law models for the length effect. J Compos Mater 29(14):1873–1884

    Article  Google Scholar 

  33. Escobar LA, Meeker WQ (2007) A review of accelerated test models. Stat Sci 21(4):552–577

    Article  MathSciNet  MATH  Google Scholar 

  34. Zuo MJ, Renyan J, Yam R (1999) Approaches for reliability modeling of continuous-state devices. IEEE Trans Reliab 48(1):9–18

    Article  Google Scholar 

  35. Wang L, Zhang H, Xue H (2012) Life prediction based on degradation amount distribution using composite time series analysis. Ieri Procedia 1(9):217–224

    Article  Google Scholar 

  36. Ye ZS, Xie M (2014) Stochastic modelling and analysis of degradation for highly reliable products. Appl Stoch Model Bus Ind 31(1):16–32

    Article  MathSciNet  Google Scholar 

  37. Tseng ST, Tang J, Ku IH (2003) Determination of burn-in parameters and residual life for highly reliable products. Nav Res Logist 50(1):1–14

    Article  MathSciNet  MATH  Google Scholar 

  38. Joseph VR, Yu IT (2006) Reliability improvement experiments with degradation data. IEEE Trans Reliab 55(1):149–157

    Article  Google Scholar 

  39. Si XS, Wang W, Hu CH, Chen MY, Zhou DH (2013) A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech Syst Signal Process 35(1–2):219–237

    Article  Google Scholar 

  40. Wang H, Xu T, Mi Q (2015) Lifetime prediction based on gamma processes from accelerated degradation data. Chin J Aeronaut 28(1):172–179

    Article  Google Scholar 

  41. Iervolino I, Giorgio M, Chioccarelli E (2013) Gamma degradation models for earthquake-resistant structures. Struct Saf 45(45):48–58

    Article  Google Scholar 

  42. Noortwijk JMV (2009) A survey of the application of gamma processes in maintenance. Reliab Eng Syst Saf 94(1):2–21

    Article  Google Scholar 

  43. Wang X, Xu D (2010) An inverse Gaussian process model for degradation data. Technometrics 52(2):188–197

    Article  MathSciNet  Google Scholar 

  44. Peng CY (2015) Inverse Gaussian processes with random effects and explanatory variables for degradation data. Technometrics 57(1):100–111

    Article  MathSciNet  Google Scholar 

  45. Ye ZS, Chen LP, Tang LC, Xie M (2014) Accelerated degradation test planning using the inverse Gaussian process. IEEE Trans Reliab 63(63):750–763

    Article  Google Scholar 

  46. Wang X (2010) Wiener processes with random effects for degradation data. J Multivar Anal 101(2):340–351

    Article  MathSciNet  MATH  Google Scholar 

  47. Ye ZS, Wang Y, Tsui KL, Pecht M (2013) Degradation data analysis using wiener processes with measurement errors. IEEE Trans Reliab 62(4):772–780

    Article  Google Scholar 

  48. Li J, Wang Z, Liu X, Zhang Y, Fu H, Liu C (2016) A Wiener process model for accelerated degradation analysis considering measurement errors. Microelectron Reliab 65:8–15

    Article  Google Scholar 

  49. Tang LC, Yang GY, Xie M (2004) Planning of step-stress accelerated degradation test, Reliability and Maintainability Annual Symposium, 287–292

    Google Scholar 

  50. Padgett WJ, Tomlinson MA (2004) Inference from accelerated degradation and failure data based on Gaussian process models. Lifetime Data Anal 10(2):191–206

    Article  MathSciNet  MATH  Google Scholar 

  51. Lawless J, Crowder M (2004) Covariates and random effects in a Gamma process model with application to degradation and failure. Lifetime Data Anal 10(3):213–227

    Article  MathSciNet  MATH  Google Scholar 

  52. Kallen MJ, Noortwijk JMV (2005) Optimal maintenance decisions under imperfect inspection. Reliab Eng Syst Saf 90(2–3):177–185

    Article  Google Scholar 

  53. Meeker WQ (2009) Trends in the statistical assessment of reliability. Adv Degrad Model:3–16

    Google Scholar 

  54. Hu CH, Lee MY, Tang J (2014) Optimum step-stress accelerated degradation test for Wiener degradation process under constraints. Eur J Oper Res 241(2):412–421

    Article  MathSciNet  MATH  Google Scholar 

  55. Lu CJ, Meeker WQ (1993) Using degradation measures to estimate a time-to-failure distribution. Technometrics 35(2):161–174

    Article  MathSciNet  MATH  Google Scholar 

  56. Wu SJ, Shao J (1999) Reliability analysis using the least squares method in nonlinear mixed-effect degradation model. Stat Sin 9(3):855–877

    MathSciNet  MATH  Google Scholar 

  57. Robinson ME, Crowder MJ (2001) Bayesian methods for a growth-curve degradation model with repeated measures. Lifetime Data Anal 6(4):357–374

    Article  MathSciNet  MATH  Google Scholar 

  58. Wakefield JC, Smith AFM, Racine-Poon A, Gelfand AE (1994) Bayesian analysis of linear and nonlinear population models using the Gibbs sampler. J R Stat Soc 43(1):201–221

    MATH  Google Scholar 

  59. Peng W, Li YF, Yang YJ, Huang HZ, Zuo MJ (2014) Inverse Gaussian process models for degradation analysis: a Bayesian perspective. Reliab Eng Syst Saf 130(1):175–189

    Article  Google Scholar 

  60. Chen Z, Zheng S (2005) Lifetime distribution based degradation analysis. IEEE Trans Reliab 54(1):3–10

    Article  Google Scholar 

  61. Bae SJ, Kuo W, Kvam PH (2007) Degradation models and implied lifetime distributions. Reliab Eng Syst Saf 92(5):601–608

    Article  Google Scholar 

  62. Bae SJ, Kvam PH (2012) A nonlinear random-coefficients model for degradation testing. Technometrics 46(4):460–469

    Article  MathSciNet  Google Scholar 

  63. Weaver BP et al (2011) Methods for planning repeated measures degradation studies. Technometrics 55(2):122–134

    Article  MathSciNet  Google Scholar 

  64. Yuan XX, Pandey MD (2009) A nonlinear mixed-effects model for degradation data obtained from in-service inspections. Reliab Eng Syst Saf 94(2):509–519

    Article  Google Scholar 

  65. Lu J, Park J, Yang Q (1997) Statistical inference of a time-to-failure distribution derived from linear degradation data. Technometrics 39(4):391–400

    Article  MATH  Google Scholar 

  66. Weaver BP, Meeker WQ (2014) Methods for planning repeated measures accelerated degradation tests. Appl Stoch Model Bus Ind 30(6):658–671

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Quality and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Lirong Hu & Qingpei Hu

  2. Search and Big Data Division, Bejing Jingdong Shangke Information Technology Co., LTD, Beijing, China

    Lingjiang Li

Authors
  1. Lirong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingjiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingpei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingpei Hu .

Editor information

Editors and Affiliations

  1. School of Social Work & Department of Biostatistics, Department of Statistics, University of North Carolina, University of Pretoria, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Department of Mathematical Sciences, University of South Dakota, Vermillion, South Dakota, USA

    Yuhlong Lio

  3. Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA

    Hon Keung Tony Ng

  4. Department of Statistics, Tamkang University, New Taipei City, Taiwan

    Tzong-Ru Tsai

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Hu, L., Li, L., Hu, Q. (2017). Degradation Modeling, Analysis, and Applications on Lifetime Prediction. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_3

Download citation

Publish with us

Policies and ethics

Search

Navigation