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

Lixiao Feng, Guorong Chen and Jun Peng
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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

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2018, vol. 12, issue 4, 101-114

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:12:y:2018:i:4:p:101-114

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