IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1405-d799833.html
   My bibliography  Save this article

Confidence-Interval-Based Fuzzy Testing for the Lifetime Performance Index of Electronic Product

Author

Listed:
  • Chun-Min Yu

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan)

  • Kuen-Suan Chen

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
    Institute of Innovation and Circular Economy, Asia University, Taichung 413305, Taiwan
    Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Ting-Hsin Hsu

    (Department of Finance, National Taichung University of Science and Technology, Taichung 404336, Taiwan)

Abstract

When the lifetime of an electronic component does not reach the required level, it can be enhanced by means of the paralleling current sharing backup system or the redundant backup system. The lifetime of the redundant backup system is the sum of lifetimes of all electronic components, which is the maximum of all the electronic components’ lifetimes, compared with the lifetime of the parallel current sharing backup system. For the purpose of enhancing products’ reliability, electronic goods are usually designed with spare electronic components. If it is assumed that there are m − 1 redundant backup components for each electronic product, then the lifetime of the electronic product will be distributed as a Gamma distribution with two parameters— m and λ , where λ is the mean for each lifetime of each electronic component. According to numerous studies, the sample size is not large, as it takes a long time to test the lifetime of an electronic product, and enterprises consider cost and timeliness. This paper concerns the performance index of the lifetime of the electronic product. Therefore, based on the confidence interval, this paper aims to develop a fuzzy testing model. As this model can integrate past data and expert experience, the testing accuracy can be retained despite small-sized samples. In fact, through adopting the testing model proposed by this paper, companies can make precise and intelligent decisions instantly with the use of small-sized samples to grasp the opportunities for improvement.

Suggested Citation

  • Chun-Min Yu & Kuen-Suan Chen & Ting-Hsin Hsu, 2022. "Confidence-Interval-Based Fuzzy Testing for the Lifetime Performance Index of Electronic Product," Mathematics, MDPI, vol. 10(9), pages 1-10, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1405-:d:799833
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1405/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1405/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chirumalla, Koteshwar, 2021. "Building digitally-enabled process innovation in the process industries: A dynamic capabilities approach," Technovation, Elsevier, vol. 105(C).
    2. Tavakkoli-Moghaddam, R. & Safari, J. & Sassani, F., 2008. "Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 550-556.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kuen-Suan Chen & Tsun-Hung Huang & Ruey-Chyn Tsaur & Wen-Yang Kao, 2022. "Fuzzy Evaluation Models for Accuracy and Precision Indices," Mathematics, MDPI, vol. 10(21), pages 1-12, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zaretalab, Arash & Sharifi, Mani & Guilani, Pedram Pourkarim & Taghipour, Sharareh & Niaki, Seyed Taghi Akhavan, 2022. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Galeone, Graziana & Ranaldo, Simona & Fusco, Antonio, 2024. "ESG and FinTech: Are they connected?," Research in International Business and Finance, Elsevier, vol. 69(C).
    3. Amirhossain Chambari & Javad Sadeghi & Fakhri Bakhtiari & Reza Jahangard, 2016. "A note on a reliability redundancy allocation problem using a tuned parameter genetic algorithm," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 426-442, June.
    4. Hadipour, Hassan & Amiri, Maghsoud & Sharifi, Mani, 2019. "Redundancy allocation in series-parallel systems under warm standby and active components in repairable subsystems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    5. Jia, Heping & Ding, Yi & Peng, Rui & Liu, Hanlin & Song, Yonghua, 2020. "Reliability assessment and activation sequence optimization of non-repairable multi-state generation systems considering warm standby," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    6. Gholinezhad, Hadi & Zeinal Hamadani, Ali, 2017. "A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 66-73.
    7. Xian Zhao & Jing Zhang & Xiaoyue Wang, 2019. "Joint optimization of components redundancy, spares inventory and repairmen allocation for a standby series system," Journal of Risk and Reliability, , vol. 233(4), pages 623-638, August.
    8. Li, Zhaojun & Liao, Haitao & Coit, David W., 2009. "A two-stage approach for multi-objective decision making with applications to system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1585-1592.
    9. Li, Chengming & Huo, Peng & Wang, Zeyu & Zhang, Weiguang & Liang, Feiyan & Mardani, Abbas, 2023. "Digitalization generates equality? Enterprises’ digital transformation, financing constraints, and labor share in China," Journal of Business Research, Elsevier, vol. 163(C).
    10. Hajipour, Yassin & Taghipour, Sharareh, 2016. "Non-periodic inspection optimization of multi-component and k-out-of-m systems," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 228-243.
    11. Prashanthi Boddu & Liudong Xing, 2013. "Reliability evaluation and optimization of series–parallel systems with k-out-of-n: G subsystems and mixed redundancy types," Journal of Risk and Reliability, , vol. 227(2), pages 187-198, April.
    12. Wei Wang & Yaofeng Xu & Liguo Hou, 2019. "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Journal of Risk and Reliability, , vol. 233(5), pages 791-802, October.
    13. Abou, Seraphin C., 2010. "Performance assessment of multi-state systems with critical failure modes: Application to the flotation metallic arsenic circuit," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 614-622.
    14. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2013. "Cold-standby sequencing optimization considering mission cost," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 28-34.
    15. Hui Zhang & Huiying Ding & Jianying Xiao, 2023. "How Organizational Agility Promotes Digital Transformation: An Empirical Study," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    16. Fernandes, Kiran Jude & Milewski, Simon & Chaudhuri, Atanu & Xiong, Yu, 2022. "Contextualising the role of external partnerships to innovate the core and enabling processes of an organisation: A resource and knowledge-based view," Journal of Business Research, Elsevier, vol. 144(C), pages 146-162.
    17. Seyed Mohsen Mousavi & Najmeh Alikar & Madjid Tavana & Debora Di Caprio, 2019. "An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1175-1194, March.
    18. Kim, Heungseob & Kim, Pansoo, 2017. "Reliability models for a nonrepairable system with heterogeneous components having a phase-type time-to-failure distribution," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 37-46.
    19. Kayedpour, Farjam & Amiri, Maghsoud & Rafizadeh, Mahmoud & Shahryari Nia, Arash, 2017. "Multi-objective redundancy allocation problem for a system with repairable components considering instantaneous availability and strategy selection," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 11-20.
    20. Attar, Ahmad & Raissi, Sadigh & Khalili-Damghani, Kaveh, 2017. "A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 177-191.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1405-:d:799833. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.