IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v28y2017i1d10.1007_s10845-014-0960-1.html
   My bibliography  Save this article

Heuristic particle swarm optimization approach for test point selection with imperfect test

Author

Listed:
  • Sen Deng

    (Air Force Engineering University)

  • Bo Jing

    (Air Force Engineering University)

  • Hongliang Zhou

    (Air Force Engineering University)

Abstract

The problem of near-optimal test point set selection with imperfect test is solved by using the heuristic particle swarm optimization (HPSO) algorithm. First, to describe the uncertainty of each test, the testability analysis model and such indexes as fault detection rate, fault isolation rate, and false alarm rate are redefined. A heuristic function is then established to evaluate the detection isolation capability and uncertainty of the test point, which can provide heuristic information to improve the searching efficiency of particle swarm optimization (PSO). The heuristic function and least test cost principle are used as bases to design a fitness function of PSO algorithm for test point selection. Finally, the HPSO algorithm is proposed to select the optimal test point set for two practical systems. Simulation and experiment results show that the method can determine the global optimal test point accurately and effectively while meeting the requirements of testability indexes with least cost.

Suggested Citation

  • Sen Deng & Bo Jing & Hongliang Zhou, 2017. "Heuristic particle swarm optimization approach for test point selection with imperfect test," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 37-50, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0960-1
    DOI: 10.1007/s10845-014-0960-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-014-0960-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-014-0960-1?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.

    Citations

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


    Cited by:

    1. Xiaofeng Lv & Deyun Zhou & Yongchuan Tang & Ling Ma, 2018. "An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    2. Tian, Heng & Duan, Fuhai & Fan, Liang & Sang, Yong, 2019. "Novel solution for sequential fault diagnosis based on a growing algorithm," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    3. Wang, Jingyuan & Liu, Zhen & Wang, Jiahong & Long, Bing & Zhou, Xiuyun, 2022. "A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

    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:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0960-1. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.