Research article Special Issues

Non-parametric accelerated life testing estimation for fuzzy life times under fuzzy stress levels

  • Received: 29 December 2022 Revised: 14 March 2023 Accepted: 22 March 2023 Published: 19 April 2023
  • MSC : 62N05, 94D05

  • Uncompleted developments in the fields of measurement sciences are categorically agreed on the fact that measurements obtained from continuous phenomena cannot be measured precisely. Therefore, these measurements cannot be considered precise numbers but are nonprecise or fuzzy. For this purpose, it is compulsion of the time that such estimators need to be developed to cover both the uncertainties. The classical accelerated life testing (ALT) approaches are based on precise life times and precise stress levels, but in fact, these are not precise numbers but fuzzy. In this study, the nonparametric procedure of ALT is generalized in such a manner that in addition to random variation, fuzziness of the lifetime observations and stress levels are integrated in the developed estimators. The developed generalized nonparametric estimators for accelerated life time analysis utilize all the obtainable information that is present in the form of fuzziness in single observations and random variation among the observations to make suitable inferences. On the other hand, classical estimators only deal with random variation, which is a strong reason to conclude that the developed estimators should be preferred over classical estimators.

    Citation: Muhammad Shafiq, Syed Habib Shah, Mohammad Abiad, Qamruz Zaman. Non-parametric accelerated life testing estimation for fuzzy life times under fuzzy stress levels[J]. AIMS Mathematics, 2023, 8(6): 14475-14484. doi: 10.3934/math.2023739

    Related Papers:

  • Uncompleted developments in the fields of measurement sciences are categorically agreed on the fact that measurements obtained from continuous phenomena cannot be measured precisely. Therefore, these measurements cannot be considered precise numbers but are nonprecise or fuzzy. For this purpose, it is compulsion of the time that such estimators need to be developed to cover both the uncertainties. The classical accelerated life testing (ALT) approaches are based on precise life times and precise stress levels, but in fact, these are not precise numbers but fuzzy. In this study, the nonparametric procedure of ALT is generalized in such a manner that in addition to random variation, fuzziness of the lifetime observations and stress levels are integrated in the developed estimators. The developed generalized nonparametric estimators for accelerated life time analysis utilize all the obtainable information that is present in the form of fuzziness in single observations and random variation among the observations to make suitable inferences. On the other hand, classical estimators only deal with random variation, which is a strong reason to conclude that the developed estimators should be preferred over classical estimators.



    加载中


    [1] L. E. Lee, J. W. Wang, Statistical methods for survival data analysis, New Jersey: Willy, 2013.
    [2] R. G. Miller, Survival analysis, New York: Willy, 2011.
    [3] G. J. Levenbach, Accelerated life testing of capacitors, IEEE T. Reliab. Qual. Control, 1957, 9–20. https://doi.org/10.1109/IRE-PGRQC.1957.5007129 doi: 10.1109/IRE-PGRQC.1957.5007129
    [4] R. Viertl, Statistical methods for fuzzy data, Chichester: Willy, 2011.
    [5] R. Viertl, D. Hareter, Beschreibung und analyse unscharfer information: Statistische methoden für unscharfe daten, Wien: Springer, 2006.
    [6] F. Aqlan, E. M. Ali, Integrating lean principles and fuzzy bow-tie analysis for risk assessment in chemical industry, J. Loss Prevent. Proc., 29 (2014), 39–48. https://doi.org/10.1016/j.jlp.2014.01.006 doi: 10.1016/j.jlp.2014.01.006
    [7] S. Sharif, M. Akbarzadeh, Distributed probabilistic fuzzy rule mining for clinical decision making, Fuzzy Inform. Eng., 13 (2021), 436–459. https://doi.org/10.1080/16168658.2021.1978803 doi: 10.1080/16168658.2021.1978803
    [8] S. Hussain, Y. Kim, S. Thakur, J. Breslin, Optimization of waiting time for electric vehicles using a fuzzy inference system, IEEE T. Intell. Transp. Syst., 23 (2022), 15396–15407. https://doi.org/10.1109/TITS.2022.3140461 doi: 10.1109/TITS.2022.3140461
    [9] S. Hussain, M. Ahmed, Y. Kim, Efficient power management algorithm based on fuzzy logic inference for electric vehicles parking lot, IEEE Access, 7 (2019), 65467–65485. https://doi.org/10.1109/ACCESS.2019.2917297 doi: 10.1109/ACCESS.2019.2917297
    [10] S. Hussain, S. Thakur, S. Shukla, J. Breslin, Q. Jan, F. Khan, et al., A two-layer decentralized charging approach for residential electric vehicles based on fuzzy data fusion, J. King Saud Univ.-Com., 34 (2022), 7391–7405. https://doi.org/10.1016/j.jksuci.2022.04.019 doi: 10.1016/j.jksuci.2022.04.019
    [11] S. Hussain, M. Ahmed, K. Lee, Y. Kim, Fuzzy logic weight based charging scheme for optimal distribution of charging power among electric vehicles in a parking lot, Energies, 13 (2020), 3119. https://doi.org/10.3390/en13123119 doi: 10.3390/en13123119
    [12] S. Hussain, L. Ki-Beom, M. Ahmed, B. Hayes, Y. Kim, Two-stage fuzzy logic inference algorithm for maximizing the quality of performance under the operational constraints of power grid in electric vehicle parking lots, Energies, 13 (2020), 4634. https://doi.org/10.3390/en13184634 doi: 10.3390/en13184634
    [13] R. Viertl, Parametric and semiparametric models with applications to reliability, survival analysis, and quality of life, Boston: Birkhäuser, 2004.
    [14] M. Shafiq, M. Atif, R. Viertl, Beyond precision: Accelerated life testing for fuzzy life time data, Soft Comput., 22 (2018), 7355–7365. https://doi.org/10.1007/s00500-018-3067-3 doi: 10.1007/s00500-018-3067-3
    [15] M. Shafiq, M. Atif, On the survival models for step-stress experiments based on fuzzy life time data, Qual. Quant., 51 (2017), 79–91. https://doi.org/10.1007/s11135-015-0295-9 doi: 10.1007/s11135-015-0295-9
    [16] M. Shafiq, A. Khalil, M. Atif, Q. Zaman, Empirical acceleration functions and fuzzy information, Int. J. Uncertain. Quan., 6 (2016), 215–228. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2016016285 doi: 10.1615/Int.J.UncertaintyQuantification.2016016285
    [17] H. Xu, X. Li, L. Liu, Statistical analysis of accelerated life testing under Weibull distribution based on fuzzy theory, Palm Harbor, FL: IEEE, 2015.
    [18] L. Liu, X. Y. Li, W. Zhang, T. M. Jiang, Fuzzy reliability prediction of rotating machinery product with accelerated testing data, J. Vibroeng., 17 (2015), 4193–4210.
    [19] M. Shaked, W. J. Zimmer, C. A. Ball, A nonparametric approach to accelerated life testing, J. Am. Stat. Assoc., 74 (1979), 694–699. https://doi.org/10.2307/2286993 doi: 10.2307/2286993
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1012) PDF downloads(33) Cited by(0)

Article outline

Figures and Tables

Figures(6)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog