IDEAS home Printed from https://ideas.repec.org/a/bpj/jossai/v2y2014i5p473-480n8.html
   My bibliography  Save this article

MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions

Author

Listed:
  • Liu Yumin
  • Zhou Haofei

    (Business School, Zhengzhou University, Zhengzhou450001, China)

Abstract

Recognition of quality abnormal patterns for a dynamic process has seen increasing demands nowadays in the real-time process fault detection and diagnosis. As the dynamic data from a quality abnormal process is linearly inseparable, the recognition efficiency of a support vector machine (SVM) model mainly depends on the selection of the kernel functions and the optimizing of their parameters. Based on the analysis of the quality abnormal patterns in a dynamic process, this paper presents a recognition framework of quality abnormal patterns by using a multi-SVM (MSVM). For the different quality abnormal patterns, the simulation results indicate that the recognition accuracies of the MSVM classifiers with the selected kernel functions are quite different. A MSVM recognition model for quality abnormal patterns in a dynamic process is proposed by the kernel functions being of high accuracies. It is shown that this MSVM model with suitable kernel functions can increase the recognition accuracy.

Suggested Citation

  • Liu Yumin & Zhou Haofei, 2014. "MSVM Recognition Model for Dynamic Process Abnormal Pattern Based on Multi-Kernel Functions," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 473-480, October.
    • Handle: RePEc:bpj:jossai:v:2:y:2014:i:5:p:473-480:n:8
    DOI: 10.1515/JSSI-2014-0473
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/JSSI-2014-0473
    Download Restriction: no
    File URL: https://libkey.io/10.1515/JSSI-2014-0473?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
    ---><---

    References listed on IDEAS

    as
    1. Jianjun Shi & Shiyu Zhou, 2009. "Quality control and improvement for multistage systems: A survey," IISE Transactions, Taylor & Francis Journals, vol. 41(9), pages 744-753.
    Full references (including those not matched with items on IDEAS)

    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. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    2. Chongyang Liu & Ryan Loxton & Kok Teo, 2014. "Optimal parameter selection for nonlinear multistage systems with time-delays," Computational Optimization and Applications, Springer, vol. 59(1), pages 285-306, October.
    3. Lin, Zu-Liang & Huang, Yeu-Shiang & Fang, Chih-Chiang, 2015. "Non-periodic preventive maintenance with reliability thresholds for complex repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 145-156.
    4. Kenneth J. Klassen & Reena Yoogalingam, 2019. "Appointment scheduling in multi-stage outpatient clinics," Health Care Management Science, Springer, vol. 22(2), pages 229-244, June.
    5. Chen-Fu Chien & Chiao-Wen Liu & Shih-Chung Chuang, 2017. "Analysing semiconductor manufacturing big data for root cause detection of excursion for yield enhancement," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5095-5107, September.
    6. Wei, Shuaichong & Nourelfath, Mustapha & Nahas, Nabil, 2023. "Analysis of a production line subject to degradation and preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Xiaoqin Wen & Chenhanzhi Wang, 2022. "Optimal-Quality Choice and Committed Delivery Time in Build-To-Order Supply Chain," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    8. Jinho Kim & Myong K. Jeong & Elsayed A. Elsayed, 2017. "Monitoring multistage processes with autocorrelated observations," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2385-2396, April.
    9. Angus Jeang & Yang-Kuei Lin, 2014. "Product and process parameters determination for quality and cost," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2042-2054, October.
    10. Lu, Biao & Chen, Zhen & Zhao, Xufeng, 2021. "Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors," Reliability Engineering and System Safety, Elsevier, vol. 212(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:bpj:jossai:v:2:y:2014:i:5:p:473-480:n:8. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.