Skip to main content
Log in

Reliability Evaluation of Two-Phase Degradation Process with a Fuzzy Change-Point

  • Published:
Journal of Shanghai Jiaotong University (Science) Aims and scope Submit manuscript

Abstract

For some products, degradation mechanisms change during testing, and therefore, their degradation patterns vary at different points in time; these points are called change-points. Owing to the limitation of measurement costs, time intervals for degradation measurements are usually very long, and thus, the value of change-points cannot be determined. Conventionally, a certain degradation measurement is selected as the change-point in a two-phase degradation process. According to the tendency of the two-phase degradation process, the change-point is probably located in the interval between two neighboring degradation measurements, and it is a fuzzy variable. The imprecision of the change-point may lead to the incorrect product’s reliability evaluation results. In this paper, based on the fuzzy theory, a two-phase degradation model with a fuzzy change-point and a statistical analysis method are proposed. First, a two-phase Wiener degradation model is developed according to the membership function of the change-point. Second, the reliability evaluation is carried out using maximum likelihood estimation and a fuzzy simulation approach. Finally, the proposed methodology is verified via a case study. The results of the study show that the proposed methodology can achieve more believable reliability evaluation results compared with those of the conventional approach.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. YE Z S, XIE M. Stochastic modelling and analysis of degradation for highly reliable products [J]. Applied Stochastic Models in Business and Industry, 2015, 31(1): 16–32.

    Article  MathSciNet  Google Scholar 

  2. PAROISSIN C. Inference for the Wiener process with random initiation time [J]. IEEE Transactions on Reliability, 2016, 65(1): 147–157.

    Article  Google Scholar 

  3. TSAI C C, LIN C T, BALAKRISHNAN N. Optimal design for accelerated-stress acceptance test based on Wiener process [J]. IEEE Transactions on Reliability, 2015, 64(2): 603–612.

    Article  Google Scholar 

  4. ZHAI Q, YE Z S. RUL prediction of deteriorating products using an adaptive Wiener process model [J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2911–2921.

    Article  Google Scholar 

  5. RODRÍGUEZ-PICÓN L A, PERÉZ-DOMÍNGUEZ L, MEJIA J, et al. A deconvolution approach for degradation modeling with measurement error [J]. IEEE Access, 2019, 7: 143899–143911.

    Article  Google Scholar 

  6. BAE S J, YUAN T, NING S, et al. A Bayesian approach to modeling two-phase degradation using change-point regression [J]. Reliability Engineering & System Safety, 2015, 134: 66–74.

    Article  Google Scholar 

  7. BAE S J, YUAN T, KIM S J. Bayesian degradation modeling for reliability prediction of organic light-emitting diodes [J]. Journal of Computational Science, 2016, 17: 117–125.

    Article  MathSciNet  Google Scholar 

  8. YAN W A, SONG B W, DUAN G L, et al. Reliability evaluation of LCD based on two-phase Wiener degradation process [J]. Systems Engineering and Electronics, 2014, 36(9): 1882–1886 (in Chinese).

    Google Scholar 

  9. KONG D J, BALAKRISHNAN N, CUI L R. Two-phase degradation process model with abrupt jump at change point governed by Wiener process [J]. IEEE Transactions on Reliability, 2017, 66(4): 1345–1360.

    Article  Google Scholar 

  10. ZHANG J X, HU C H, HE X, et al. A novel lifetime estimation method for two-phase degrading systems [J]. IEEE Transactions on Reliability, 2019, 68(2): 689–709.

    Article  Google Scholar 

  11. WEN Y, WU J, DAS D, et al. Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity [J]. Reliability Engineering & System Safety, 2018, 176: 113–124.

    Article  Google Scholar 

  12. JAVED K, GOURIVEAU R, ZERHOUNI N. A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering [J]. IEEE Transactions on Cybernetics, 2015, 45(12): 2626–2639.

    Article  Google Scholar 

  13. PURBA J H, LU J, ZHANG G, et al. A fuzzy reliability assessment of basic events of fault trees through qualitative data processing [J]. Fuzzy Sets and Systems, 2014, 243: 50–69.

    Article  MathSciNet  Google Scholar 

  14. LIN Y H, LI Y F, ZIO E. Fuzzy reliability assessment of systems with multiple-dependent competing degradation processes [J]. IEEE Transactions on Fuzzy Systems, 2015, 23(5): 1428–1438.

    Article  Google Scholar 

  15. GONZALEZ-GONZALEZ D S, PRAGA ALEJO R J, CANTÚ-SIFUENTES M, et al. A non-linear fuzzy regression for estimating reliability in a degradation process [J]. Applied Soft Computing, 2014, 16: 137–147.

    Article  Google Scholar 

  16. GONZALEZ-GONZALEZ D S, PRAGA ALEJO R J, CANTÚ-SIFUENTES M. A non-linear fuzzy degradation model for estimating reliability of a polymeric coating [J]. Applied Mathematical Modelling, 2016, 40(2): 1387–1401.

    Article  MathSciNet  Google Scholar 

  17. LI X Y, WU J P, MA H G, et al. A random fuzzy accelerated degradation model and statistical analysis [J]. IEEE Transactions on Fuzzy Systems, 2018, 26(3): 1638–1650.

    Article  Google Scholar 

  18. KHALIFA T R, EL-NAGAR A M, EL-BRAWANY M A, et al. A novel fuzzy Wiener-based nonlinear modelling for engineering applications [J]. ISA Transactions, 2020, 97: 130–142.

    Article  Google Scholar 

  19. LEE M Y, HU C H, TANG J. A two-stage latent variable estimation procedure for time-censored accelerated degradation tests [J]. IEEE Transactions on Reliability, 2017, 66(4): 1266–1279.

    Article  Google Scholar 

  20. WANG P, TANG Y, BAE S J, et al. Bayesian approach for two-phase degradation data based on change-point Wiener process with measurement errors [J]. IEEE Transactions on Reliability, 2018, 67(2): 688–700.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tianji Zou  (邹田骥).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 61703391)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, K., Dang, W., Zou, T. et al. Reliability Evaluation of Two-Phase Degradation Process with a Fuzzy Change-Point. J. Shanghai Jiaotong Univ. (Sci.) 27, 867–872 (2022). https://doi.org/10.1007/s12204-021-2323-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12204-021-2323-3

Key words

CLC number

Navigation