A case-based knowledge system for safety evaluation decision making of thermal power plants

  • Authors:

  • Dong-Xiao Gu;Chang-Yong Liang;Isabelle Bichindaritz;Chun-Rong Zuo;Jun Wang

  • Affiliations:

  • School of Management at Hefei University of Technology, Hefei, Anhui 230009, PR China and Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education of China, ...;School of Management at Hefei University of Technology, Hefei, Anhui 230009, PR China and Department of Computer Science at University of Illinois at Chicago, Chicago, IL 60607, USA and Engineerin ...;Institute of Technology at University of Washington, Tacoma, WA 98402, USA;School of Management at Hefei University of Technology, Hefei, Anhui 230009, PR China and Engineering Research Center of Intelligent Decision-Making and Information System Technology of Ministry o ...;Department of Computer Science at University of Wisconsin at Milwaukee, Milwaukee, WI 53201, USA

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

Safety assessment of thermal power plants (TPP) is an important means to ensure the safety of production in thermal power production enterprises. Modern information technology can play an important role in TPP safety assessment. The evaluation of power plant systems relies, to a large extent, on the knowledge and experience of the experts undertaking the task. Case-based reasoning (CBR) is introduced for the safety assessment of TPP since it models expertise through experience management. This paper provides a case-based approach for the Management System safety assessment decision making of TPP (MSSATPP). We introduce a case matching method named CBR-Grey, which integrates the Delphi approach and grey system theory. Based on this method, we implement a prototype of case-based knowledge system (CBRSYS-TPP) for the evaluation decision making of the panel of experts. Our experimental results based on a real-world TPP safety assessment data set show that CBRSYS-TPP has high accuracy and systematically good performance.