IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v202y2020ics0951832020305032.html
   My bibliography  Save this article

Introduction to formal concept analysis and its applications in reliability engineering

Author

Listed:
  • Rocco, Claudio M.
  • Hernandez-Perdomo, Elvis
  • Mun, Johnathan

Abstract

Formal Analysis of Concepts (FCA) is a method of data analysis that helps to study the relationship between a set of objects and a set of attributes (the formal context). FCA not only allows detecting data groups (concepts) and their graphical visualization, but also extracting rules that could reveal the underlying structure of the analyzed context. The main idea of this paper is to present the fundamentals of FCA and how it can be used in reliability engineering problems. To this aim, examples in reliability engineering, from both the literature and authors’ experience, have been selected for analysis. Comments on the new insights provided by FCA are also highlighted. Finally, the results from the examples selected show that other reliability areas could benefit from using an FCA-based approach.

Suggested Citation

  • Rocco, Claudio M. & Hernandez-Perdomo, Elvis & Mun, Johnathan, 2020. "Introduction to formal concept analysis and its applications in reliability engineering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305032
    DOI: 10.1016/j.ress.2020.107002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020305032
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    2. Lenca, Philippe & Meyer, Patrick & Vaillant, Benoit & Lallich, Stephane, 2008. "On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid," European Journal of Operational Research, Elsevier, vol. 184(2), pages 610-626, January.
    3. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2017. "AC power flow importance measures considering multi-element failures," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 89-97.
    4. Peter Butka & Jozef Pócs & Jana Pócsová, 2013. "Representation of Fuzzy Concept Lattices in the Framework of Classical FCA," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-7, November.
    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. Jianfeng Xu & Chenglei Wu & Jilin Xu & Lan Liu & Yuanjian Zhang, 2023. "Stream Convolution for Attribute Reduction of Concept Lattices," Mathematics, MDPI, vol. 11(17), pages 1-19, August.

    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. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    3. Lyu, Dong & Si, Shubin, 2021. "Importance measure for K-out-of-n: G systems under dynamic random load considering strength degradation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Liu, Hanchen & Wang, Chong & Ju, Ping & Li, Hongyu, 2022. "A sequentially preventive model enhancing power system resilience against extreme-weather-triggered failures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    5. Yongzheng Zhang & Chunming Ye & Xiuli Geng, 2022. "A Hesitant Fuzzy Method for Evaluating Risky Cold Chain Suppliers Based on an Improved TODIM," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    6. Wang, Xin & Liu, Xiaodong & Pedrycz, Witold & Zhu, Xiaolei & Hu, Guangfei, 2012. "Mining axiomatic fuzzy set association rules for classification problems," European Journal of Operational Research, Elsevier, vol. 218(1), pages 202-210.
    7. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    8. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    9. A. Pérez-Alonso & I. J. Blanco & J. M. Serrano & L. M. González-González, 2021. "Incremental maintenance of discovered fuzzy association rules," Fuzzy Optimization and Decision Making, Springer, vol. 20(4), pages 429-449, December.
    10. Li, Jian & Shi, Congling & Chen, Changkun & Dueñas-Osorio, Leonardo, 2018. "A cascading failure model based on AC optimal power flow: Case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 313-323.
    11. Wang, Shuliang & Gu, Xifeng & Chen, Jiawei & Chen, Chen & Huang, Xiaodi, 2023. "Robustness improvement strategy of cyber-physical systems with weak interdependency," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Zhou, Dongyue & Hu, Funian & Wang, Shuliang & Chen, Jun, 2021. "Power network robustness analysis based on electrical engineering and complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    13. Scherb, Anke & Garrè, Luca & Straub, Daniel, 2019. "Evaluating component importance and reliability of power transmission networks subject to windstorms: methodology and application to the nordic grid," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    14. Gjorgiev, Blazhe & Sansavini, Giovanni, 2022. "Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    15. Li-Pin Chi & Zheng-Yun Zhuang & Chen-Hua Fu & Jen-Hung Huang, 2018. "A Knowledge Discovery Education Framework Targeting the Effective Budget Use and Opinion Explorations in Designing Specific High Cost Product," Sustainability, MDPI, vol. 10(8), pages 1-37, August.
    16. Huang, Chuangxia & Zhao, Xian & Deng, Yunke & Yang, Xiaoguang & Yang, Xin, 2022. "Evaluating influential nodes for the Chinese energy stocks based on jump volatility spillover network," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 81-94.
    17. Chakhar, Salem & Ishizaka, Alessio & Thorpe, Andy & Cox, Joe & Nguyen, Thang & Ford, Liz, 2020. "Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding," European Journal of Operational Research, Elsevier, vol. 286(2), pages 689-712.
    18. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Lyu, Dong & Si, Shubin, 2020. "Dynamic importance measure for the K-out-of-n: G system under repeated random load," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    20. Liu, Honglu & Tian, Zhihong & Huang, Anqiang & Yang, Zaili, 2018. "Analysis of vulnerabilities in maritime supply chains," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 475-484.

    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:eee:reensy:v:202:y:2020:i:c:s0951832020305032. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.