IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v12y2018i4p101-114.html
   My bibliography  Save this article

An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices

Author

Listed:
  • Lixiao Feng

    (School of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, China)

  • Guorong Chen

    (Chongqing University of Science and Technology, Chongqing, China)

  • Jun Peng

    (Chongqing University of Science and Technology, Chongqing, China)

Abstract

Due to high temperature, high pressure, high corrosion, and many other factors, the hazardous chemical device is facing more severe security challenges than other industries. Now, the monitoring methods have been very mature, which play a basic monitoring role, not a predictive fault diagnosis. In this article, the hazardous chemical device's status data will be collected from the existing industrial monitoring network, the real-time data will be preprocessed and then stored in a database, and the data will be imported to the real-time data into the ontology cognitive model; the data will be performed by big data processing and automatic reasoning so that real-time status of hazardous chemical device and the warning of security risks predict are easily obtained at any time. The model is proposed to solve the problem of knowledge representation and reasoning of the hazardous chemical device based on ontology. The model is analyzed and implemented in Protégé software.

Suggested Citation

  • Lixiao Feng & Guorong Chen & Jun Peng, 2018. "An Ontology-Based Cognitive Model for Faults Diagnosis of Hazardous Chemical Storage Devices," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 12(4), pages 101-114, October.
    • Handle: RePEc:igg:jcini0:v:12:y:2018:i:4:p:101-114
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2018100106
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jcini0:v:12:y:2018:i:4:p:101-114. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.