Study of Technology for Detecting Pre-Ignition Conditions of ... - NIST

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Michigan UNV., Ann Arbor. Dept. of Environmental and Industrial Health. National Inst. for Occupational Safety and Healt& Cincinnati, OH. NTIS Accession Numbec PB93-217115LXAB 30 Mm 93 140p Ziolkowski, C. J. ; Huebler, J. E. ; Rusiz W. F. Sensors for Advanced Gas Distribution Systems. Final Repo~ March 1987-February 1988 Instituteof Gas Technology, Chicago, IL. Gas Research Inst, Chicago, IL. Report No.: GRI-90/0322 NTIS Accession Number PB91-159566LXAB Jan 91 43p Artificial Intelligence, Smart Sensors, Fuzzy Logic Bcyrcis, J. R State of Detection Tecbnolog. Underwriters Laboratories, Inc., NorthbrooL IL CPSC-IAG95-1145 U. S./Japan Government Cooperative Program on Natural Resources (UJNR). FR Research and Safety. L2th Jornt Panel Meeting. October 27-November 2, 1992, Tsukub% Japaq Building Research Inst-, IbaraQ Japm 89-92 pp, 1994. Fox, P.; Girling, K. New Generation of H@ Sensitivity Smoke Detectors. Product Profile. Air Sensor Technology Ltd. Fire Surveyor, Vol. 22, No. 4, 22-26, August 1993. Harvey, hf. Focus on Fire Detectiorx Sensing Intelligence. Thorn Security Ltd., UK Fire Prevention, No. 249, 27-29, May 1992. Heskesta& G.; Newmanj J. S. Fm Detection Using Signal Cross Comelation Tectilques. Tectilcal Report Factory Mutual Research Corp., Norwood, MA FMRC J.I. 0ROR8.RU. 129P. May 1990. Hosokaw~ M.; Satoh K.; Kouzel@ D.; Tamura, H.; OkU@ Y.; Kna~agi, S.; Fujioh T.; Watibe, Y.; KUriO,T-; N~is~L S. Study of Fuzzy-Reasoning Intelligent Fire Detection System. Fire Research Institute,Tokyo, Japan Matushha Electric Worlq Osa@ Japan U.S./Japan Government Cooperative Program on Natural Resources (UJNR). Fue Research and Saiety. 12th Joint Panel Meeting. October 27-November 2, 1992, Tsukub% Japaoj Building Research Inst., Ibar@ Japa 110-113 pp, 1994. Is@ Hwmitsw, One, TakashL YamauchL Yukio, OhtanL Shlgeru Algorithm for improving the reliability of detection with processing of multiple sensors’ signal. Nihon Univ, Tokyo, Jpn 9th InternationalConference on Automatic Fire Detection - AUBE ’89 Duisb~ Ger Conference Date: 1989 Scp Fire Safety Journal v 17 n 61991. p 469-484 1991 Isbiii H.; One, T.; Yamauchi, Y: Ohtani, S. Fire Detection System by Multi-Ixtycred Neural Network With Delay Circuit. Nihon Univ., Tokyo, Japan Hocbiki Corp., Tokyo, Japan InternationalAssociation for F~e Safety Science. Fire Safety Science. Proceedings. 4th InternationalSymposium. July 13-17, 1994, Ottaw% Ontario, Cana@ Intl. ASSOC.for Fire %fe~ Science, Boston, M.% Kashiwag~ T., Edhor, 761-772 pp, 1994. Kouzeki, D. Regress Report on Suppression and Detection Research in Japan. Fue Research Institute,Tokyo, Jspsn 71
CPS(XAG95-1145 U. S./Japan Government Cooperative Program on Natural Resources (UJNR). Fire Research and Safety. 12th Joint Panel Meeting. October 27-November 2, 1992, Tsukub% Japan, Building Research Inst-, Ibarti JaparL81-82 pp, 1994. Kozeki, D.; Satok IL; Takemoto, A.; Nom% J.; Knrio, T.; NakanishL S. Study of Fm Detection System Introducing AI Technology. Part 2. Role of Fuzzy Expert System to Reduce False Aku-ms. Matsushita Electric Works, Ltd., Osak% Japan Report of Research Jnstitnteof Japq No. 71, 21-31, March 1991. [Abstract in English] The objective of this study is to develop a new fn detection system which is extremely reliable, i.e., far less alarmingfalsely, using modern Artii3cialIntelligence (AI) technology. Inthis study, therole of Fuzzy Expert System incorporated in onrprototype AI tire detection system was examined to reduce false rdarrns.Fuzzy membership functions corresponding to flaming fires, smoldering fwes, smoke of tobacco, and steam flowing out of a bathroom in a hotei guest room were defined in consistency with the rules of expert system. Experimental resuhs showed thatthe Fuzzy membership functions defined in this study and tbe Fuzzy Expert System are usefid to reduce fake alarms. It was easy to iden@ flaming fires and steam born bathroom, but not always easy to ident@ smolde+g fres of bed sheets with the tobacco smoke before amdjust when the smoke concentration reaches the value of 10Ydm. However, in case of smoldering fires the fire growth rate is much smaller than in case of flaming fires. Therefore, the system can wait a M$letrntilthe final decision of either fuss or non-fires. By delaying the final decision a Little,the system could judge either fms or non-f~es clearly. Listcr, E. Product Profile: An Introduction to MultisensoryTechnology. ADT Security Systems, UK Fire %fe~ Engineering, Vol. 2, No. 3, 14-17, June 1995. Meachq B. J. Use of MIcial Intelligence Teehriques for Signal Discrimination in %e Detection Systems. FireTech, Meilen, Switzerland Journal of Fire protection Engineering, Vol. 6, No. 3, 125-136, 1994. Mike, J. A Application of Neural Netwosks for Diserirninsting Fire Detectmx. Msnyland Univ., College Park University of Duiiburg. InternationalConference on Automatic Fire Detection “AUBE ‘95”, loth. April 4-6, 1995, Duisburg, Germany, Luck, H., E&or, 213-222 pp, 1995. Research is being conducted to describe the characterktica of an improved fze detector which promptly reacts to smoke while distimating between smoke and odors from fwe and non-fue sources. This study is investigating signature patterns associated with f~e and environmental sources via small- and large-scale tests toward tbe development of an improved fire detector. On the tests, smoke and odors are produced fkom a variety of conditions: flaming, pyrolyzing and heated samples, and nuisance sources, such as aerosols, household products and cooked food. Measurements include light obscrsratio% temperature,mass km. CO, C02, 02 and oxidizable gas concentmtiorss. The feasibility of an elementary expert system to classify the source of the signature=from small-scale ezTeriments was demonstrated in the f@ phase. In the recently completed second phase, a similar expert system correctly classified the source of the signatures in large-scale expcrimets in 85 OjO of the cases. Neural networks have been applied to both sets of data from tie small- and large-scale tests providing an even greater successful classtilcation rate. Miie, J. A; McAvoy, T. J. &-ialysis of Signature Patternsfor Discriminating Fire Detection With Multiple Sensors. Maryland Univ., College Park Fire Technology, Vol. 31, No. 2, 120-136, May 1995. Incorporating intelligence into a fmedetector so thatit can recognize signaturepatternsis intendedto permitprompt fw detection whale allowing the detector to d~crimiiate between signatures tiom f~ and nonfiie sources. The primary purpose of this preliminary study is to investigatethe patiems of signaturesassociated with fme and environmental sources using small-scale experiments. We geaerated products from a wide range of conditions, from flaming or pyrolyzing samples, to heated samples and samples obtained with an atomizer. We aJsomeasured gas concentrations, tight obscuration and temperatureto characterize the products. By analyzing the da~ we identfled trends fi-om which au elementary expert system can be formulated to identi& the source of the airborne products. Several patternsere evident. The maximum C02 concentrations achieved during experiments with flaming fues =e si~lcantly ~eater than the maximum C02 concentrations achieved during expedients with nonfkumingfues @s-ol@ng fwes, heated liquids, and environmental odors). ~e mmflaming sources can be identified based on the CO and metal oxide sensor peak measurements.Except for three experiments using pyrolyzing solids, tke peak CO concentration is greater - though the Taguchi detector respomseis less - for nontlarningfwes than for environmental sources. Subsequent application of a neursdnetwork properly classifies all except one pyrolyzing free. Oizumi T.; Saito, K.; Gnrver, W. .& Fue Detection and Extinguishment System Using Fuzzy Inference rmdNeuraJNetworks. Kentucky Univ., Lexington 72 ---
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