Electrical Fire Cause Diagnosis System Using a Knowledge Base

  • Lee, Jong-Ho (Department of Safety Engineering, Chungbuk National University) ;
  • Kim, Doo-Hyun (Department of Safety Engineering, Chungbuk National University) ;
  • Kim, Sung-Chul (Department of Safety Engineering, Chungbuk National University)
  • Published : 2007.12.31


For last several decades with the achievement of fast economic development, the electrical fires occupies over 30 percent of total fire incidents almost every year in Korea and not decreased in spite of much times and efforts. Electrical fire cause diagnostics are to confirm a cause for the fire by examination of fire scene. Cause diagnosis methods haven't been systematized yet, because of limits for available information, investigator's biased knowledge, etc. Therefore, in order to assist the investigators and to find out the exact causes of electrical fires, required is research for an electrical fire cause diagnosis system using DB, computer programming and some mathematical tools. The electrical fire cause diagnosis system has two functions of DB and electrical fire cause diagnosis. The cause diagnosis is conducted by a case-based reasoning on a case base and rule-based reasoning on a rule base. For the diagnosis with high reliability, a mixed reasoning approach of a case-based reasoning and fuzzy rule-based reasoning has been adopted. The electrical fire cause diagnosis system proposes the electrical fire causes inferred from the diagnosis processes, and possibility of the causes as well.


  1. NFPA 921 Guide for Fire and Explosion Investigations 2004 edition, NFPA, 2004
  2. Fire Investigation Team compilation, 'Fire cause investigation method for the scene work', Incheon Metropolitan Fire and Disaster Management Department, 2003
  3. John D. Dehaan, Kirk's Fire Investigation 5th Edition, Prentice Hall, 2004
  4. Niamh nic daeid, Fire Investigation, CRC Press, 2004
  5. Youg-Kee Paek, Jun`gyun Seo, and Gil-Chang Kim, 'A Case-Based Reasoning Approach to Relation Database Schema Design', 1994
  6. Zhi-Wei Ni, Shan-Lin Yang, et al., 'Integrated Case- Based Reasoning', Proceedings of ICMLC, pp.1845- 1849, 2003
  7. G. Dvir, G. Langholz, M. Schneider, 'Matching Attributes in a Fuzzy Case Based Reasoning', 1999
  8. M. Koyuncu, A. Yazici, 'A Fuzzy Knowledge-Based System for Intelligent Retrieval', IEEE Trans. Fuzzy Syst., Vol.13, No.3, 2005 https://doi.org/10.1109/TFUZZ.2004.839670
  9. Lawrence O. Hall, 'Rule Chaining in Fuzzy Expert Systems', IEEE Trans. Fuzzy Syst., Vol.9, No.6, 2001