EFFICIENCY INCREASE FOR ELECTRICAL FIRE DETECTION AND ALARM SYSTEMS THROUGH IMPLEMENTATION OF FUZZY EXPERT SYSTEMS

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1 U.P.B. Sc. Bull., Seres C, Vol. 75, Iss. 1, 2013 ISSN x EFFICIENCY INCREASE FOR ELECTRICAL FIRE DETECTION AND ALARM SYSTEMS THROUGH IMPLEMENTATION OF FUZZY EXPERT SYSTEMS Ionuţ-Lucan HOMEAG 1, Radu PÂRLOG-CRISTIAN 2, Mrcea COVRIG 3 Securtatea la ncendu reprezntă un element fundamental în contextul cernţelor ş exgenţelor actuale. Ca o consecnţă medată a acestu fapt apare necestatea de îmbunătăţre a securtăţ la ncendu a construcţlor ş ocupanţlor prn mplementarea sstemelor electrce de detectare ş alarmare la ncendu. Detecţa ncendulu depnde în mare măsură de modul cum se efectuează procesarea semnalelor prmte de la senzor ş luarea decze de alarmare. Folosrea detectoarelor multsenzor oferă ma multe nformaţ despre condţle exstenţe în spaţul supravegheat ş permte o detecţe precsă, lmtând alarmele false. În acest artcol se propune un algortm de detecţe a ncendlor bazat pe ssteme expert fuzzy ce înglobează experenţa factorulu uman ş concluzle rezultate în urma desfăşurăr unor teste expermentale la scară reală. Fre safety represents a mlestone n the context of nowadays requrements. As an mmedate follow up comes the necessty of mprovng the fre safety by ntroducng and nstallng fre detecton and alarm systems. Fre detecton generally depends on how the sgnals from sensors are processed and the alarm decson s taken. The use of multsensor fre detectors gves more nformaton about the envronmental condtons and allows an accurate detecton wth fewer false alarms. Ths paper presents a fre detecton algorthm proposal, based on fuzzy expert systems whch nclude the human experence and whose desgn s based on expermental data followng real scale fre tests. Keywords: ntellgent Buldng Management System, automatc fre detecton, electrcal fre detecton and alarm systems, artfcal ntellgence, expert systems, membershp functons, fuzzy sets, detecton algorthms 1 Eng., General Inspectorate for Emergency Stuatons, Bucharest, Romana, e-mal: 2 Prof., Faculty of Electrcal Engneerng, Unversty POLITEHNICA of Bucharest, Romana, e- mal: 3 Prof., Faculty of Electrcal Engneerng, Unversty POLITEHNICA of Bucharest, Romana, e- mal:

2 252 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg 1. Introducton Modern buldngs rase multple safety and securty ssues that needs to be addressed (fre, authorzed access, control of envronmental condtons, emergency evacuaton). Complance wth safety and securty requrements s acqured by mplementng systems and components, more or less complex, ultmately sad ntellgent control. Electrcal fre detecton and alarm systems are among the crtcal components and encompass complex equpment and electrcal components. For such systems, the hardware component s supported by a dedcated software component whch gves a ntellgent behavour of the entre buldng management system. The practcal use of electrcal fre detecton and alarm systems s greatly wde, havng specal applcaton n large and complex buldngs where t s part of an ntellgent control as ntroduced by the new concept of Buldng Management System (BMS) [1]. BMS or Buldng Management System refers to ntellgent control whch represents ultmately a chan of nterconnected systems for montorng and control of a large varety of equpment and buldng functons, havng a certan level of effcency. Systems nterconnecton can be done takng nto account the varous ntegraton levels, startng wth basc functons lke fre protecton, ant-theft, lghtng, heatng, ventlaton, etc., gong to a superor ntegraton level among systems accordng ther functons and partculartes, and n the end we may consder the global ntegraton. In the last decade there has been a tendency for standardzaton of dfferent solutons for ensurng users safety and securty. Among the most recent solutons for ncreasng effcency of electrcal fre detecton and alarm systems s the use of fuzzy expert systems due to ther flexblty, easy functonng and the possblty of naturally ntegratng human experence for decson makng. 2. Electrcal fre detecton and alarm systems (EFDAS) The general archtecture of an electrcal fre detecton and alarm system s shown n Fg.1. The man components are [3]: - control and ndcatng equpment (ECS); - automatc fre detectors; - manual call ponts; - electrcal connecton crcutry; - auxlary equpment rechargeable batteres, repeaters, sounders, optcal alarm ndcators.

3 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 253 CONTROL AND INDICATING EQUIPMENT (ECS) CALL FIRST RESPONDERS FIRE DETECTOR LOCAL FIRE ALARM GENERAL FIRE ALARM MANUAL CALL POINT TIMER ALERT FIRE BRIGADE STOP HVAC SYSTEMS A B CLOSURE OF FIRE DOORS ACTIVATION OF SMOKE EVACUATION SYSTEMS ACTIVATION OF FIRE EXTINGUISHING SYSTEMS TRANSMISSION / RECEPTION OF FAULT SIGNALS Fg.1. The general archtecture of an electrcal fre detecton and alarm system [3] The system s structured on two man levels: - A: detecton level whch comprses the feld equpment such as fre detectors, manual calls, repeaters; - B: decson makng and nterventon management level whch comprses the control and ndcatng equpment wth ts output functons desgned for a proper nterventon. The fre detectors are nstalled and selected, n prncple, accordng to the nature of fre danger, the requred speed of detecton and the need for lmtaton of false alarms. They are connected to the control and ndcatng equpment and provde contnuous survellance of the protected spaces. The control and ndcatng equpment s provdng power to the network and s processng the sgnals from the fre detectors. Dependng on the ncomng sgnals t can trgger a set of nterventon measures, prevously confgures n the mplemented software. Regardless the producer or the protected objectve, the control and ndcatng equpment s ensurng the followng man functons [4]:

4 254 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg - Recepton and processng of the ncomng sgnals from the fre detectors, manual call ponts or any other devces (e.g. nput/output unts), to determne whether these sgnals correspond to a fre alarm condton and to ndcate any such fre alarm condton audbly and vsually; - Regular check and control of system operatng status, connectvty between devces (auto-control functon), ncludng the rescan of an ndvdual detector that has sgnalled a momentary alarm ndcaton. Ths ablty helps to cut false alarms due to sngle transent events; - Power the network (man power, auxlary power). In a fre alarm stuaton, the control and ndcatng equpment may trgger a local alarm and actvate a searchng procedure for verfyng the fre condtons by the local servce. If the fre alarm sgnal perssts after a gven tmeframe, the general fre alarm wll de trggered. Ths ncludes the nternal fre alarm (acoustc and optc) and on a case by case bass a fre alarm sgnal wll be sent to the fre brgade. In a fre alarm condton, the system may also trgger specal nterventon actons lke closng the fre resstant doors, openng the smoke evacuaton hatches, cuttng the power n certan areas of the objectve and startng the fre extngushng systems (water spray, carbon doxde, ntrogen, etc.). The sgnals that are dealt wth n such systems are of electrcal nature by precedence, thus justfyng the name of electrcal fre detecton and alarm systems (usng the acronym EFDAS). Actual approaches focus on the effcency of the fre detecton process (.e. tmely detecton of physcal and chemcal parameters assocated to the fre) and the detecton algorthm (.e. the way n whch sgnals from the fre sensors are processed and the fre alarm decson s trggered). Dfferent generatons of EFDAS can be characterzed by: - the nature of electrcal sgnals comng from sensors, the dgtal form beng the most used n modern systems; - dgtal sgnals allows the mplementaton of varous software for decson makng, drft compensaton, detector verfcaton, detector senstvty adjustment, communcaton wth the user or wth an upper management level; - fre detector electrcal sgnals can dstngush between dfferent fre alarm condtons accordng to the operatonal procedures such as: o Pre-alert (early warnng sgnal) dentfcaton of sutable condtons for fre development whch mples a local, on the ground verfcaton of the protected envronment; o Fre alarm sgnal persstence of fre condtons and transgresson of user safety levels.

5 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) Implementaton of fuzzy expert systems n EFDAS Expert systems are applcatons desgned for enablng certan expert competences to a non-expert. Expert systems try to emulate the human expert reasonng and for ths are consdered to be part of artfcal ntellgence feld [5]. Artfcal ntellgence offers excellent premses for usng fuzzy sets and fuzzy reasonng because most of the tme the knowledge belongs to human experts, beng by precedence fuzzy, ambguous or mprecse [6]. An expert system can provde solutons to problems that do not accept a determnstc soluton and ts reasonng s based on the exstng knowledge stored n a data base (rule base) n combnaton wth a specfc nference mechansm. The response analyss of electrcal fre detecton and alarm systems mples n many stuatons mprecse and fuzzy data whch can have serous consequences on the response tme and unacceptably hgh rates of false alarms. Very often the analyzed sgnals from the protected envronment returns mprecse data ("hghly possble...") or wthout certan valdty ("n 90% of cases..."). The use of fuzzy data lke "medum smoke densty" or "hgh temperature" are very smlar wth human percepton of fre effects, thus beng excellent nputs for a fre detecton algorthm of an EFDAS usng fuzzy expert systems. Fuzzy sets and fuzzy logc are used to heurstcally quantfy the meanng of lngustc varables, lngustc values and lngustc rules that are specfed by the expert. The concept of a fuzzy set s ntroduced by frst defnng a membershp functon. Let j X denote a unverse of dscourse and A ~ ~ A denote a specfc x~. The functon f x ) assocated wth lngustc value for the lngustc varable j A ~ that maps the unverse X to [0,1] s called a membershp functon. Ths membershp functon descrbes the certanty that an element of X, denoted x, wth a lngustc descrpton x~, may be classfed as j A ~. Membershp functons are subjectvely specfed n an ad-hoc (heurstc) manner from experence or ntuton [7]. It s mportant not to mx up the term certanty wth probablty. A membershp functon does not represent a probablty densty functon. There s nothng stochastc about the fuzzy system and membershp functons are not restrcted to obey the laws of probablty. In fuzzy logc, the term certanty means degree of truth. For nstance, let X = [0,100 C], x~ = temperature, j A ~ = medum, then f ( x ) may be a Gaussan curve (Fg. 2) that peaks at 1 at x = 50 C and s near 0 when x < 50 C or x > 50 C. Then f x = 50 C, f ( x ) = 1, so t s absolutely (

6 256 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg certan that x s medum. If x = 10 C then f ( x ) s very near zero, whch means that t s very certan that x s not medum. Ths approach s clearly dfferent from a standard Gaussan probablty densty functon. Recall that t s possble that a Gaussan probablty functon reach a maxmum value at a value other than 1. The standard Gaussan membershp functon always has ts peak value at 1. Clearly, many other choces for the shape of the membershp functon are possble (e.g. trangular, trapezodal, sgmod,...) and each of these wll provde a dfferent meanng for the lngustc values that they quantfy. j Then a fuzzy set denoted A s defned as: j A = {( x, f j ( x )), x X } (1) A A more n depth mathematcs of fuzzy sets, fuzzy logc and fuzzy expert systems are presented n [8] and [9]. Fg.2. Membershp functon for the lngustc value medum of the lngustc varable temperature One of the major ssues for an EFDAS s the optmal adjustment of ts standardzed components to a dverse and sometmes contradctory envronment n terms of fre detecton requrements. Usng fuzzy systems has the advantage of not operatng wth strct and crsp alarm thresholds, thus by usng lngustc varables, values and rules the user can set and assgn dfferent prortes to phenomenon observed durng fres wthout affectng the nput varables mappng and the way fuzzy sets are defned on the dscourse unverses.

7 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 257 For nstance, the use of multsensor fre detectors such as optcal/thermal/chemcal (OTC) n complex fre applcatons (offce tme, restaurant, ktchen, ndustral hall, etc.) rase the ssue of how to best nference the electrcal sgnals comng from the three sensors n order to take an accurate alarm decson. Wthout a flexble system, that would mply nstallng specalsed sngle pont detectors and multsensor on certan applcatons, whch s not at all an economc approach from both angles: operatonal and ensurng fre safety. Usng the expert fuzzy systems solved n an elegant way the two contradctory requrements: (1) operatng n specal applcatons and (2) the use of standardzed electrcal components, thus addng value to specalzed software and customzed for specal applcatons. Antcpatng ths need and opportunty, through authors research, a fuzzy expert system has been developed n order to be mplemented as a detecton algorthm for electrcal fre detecton and alarm systems, whose man components wll be presented n the followng sectons. 4. Fuzzy fre detecton and alarm expert system Fuzzy expert systems (FES) offer the flexblty of operatng standard electrcal components from an EFDAS wthout makng adjustments to the fre detectors. Applyng such technques mples the mplementaton of response functons n whch the pre-alert and fre alarm thresholds are adjusted accordngly to the fre condtons from the protected envronment and n concordance wth the fre rsk. In Fg.3 s depcted the archtecture of an EFDAS, havng as central element a fuzzy expert system (FES). The conclusons drawn by the authors after conductng varous real scale fre detecton tests revealed that the man parameters for trggerng a pre-alert or a fre alarm status are the followng: smoke densty (S a ), smoke densty varaton (S d ), temperature (T a ), dfferental temperature (T d ) and concentraton of carbon monoxde (CO). These fve nput varables wll be used n the desgn process of a fuzzy expert system whch wll serve as the fre detecton algorthm for a multsensor fre detector type optcal/thermc/chemcal (OTC). Input varables temperature (Ta), smoke densty (Sa) and concentraton of carbon monoxde (CO) are enterng drectly nto the fuzzfcaton block of the ~ ~ ~ fuzzy expert system, thus resultng the lngustc varables { Ta, S a, CCO }, each of them havng three lngustc values {low, medum, hgh}. It s well known that false alarms may occur due to sudden varaton of one or more fre parameters as a consequence of some nterferences or dsturbances n the protected envronment. The authors consdered that by

8 258 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg applyng some attenuaton / dumpng flters wll provde stablty to transent phenomenon and reslence to generatng false alarms. Consequently, the nputs dfferental temperature (T d ) and smoke densty varaton (S d ) wll pass frstly through an attenuaton flter to elmnate sudden varatons, whch normally are responsble for false alarms. T a T d S a S d Crsp nputs Fuzzfcaton Fuzzy expert system (FES) Inference Mechansm Rule Base Defuzzfcaton Crsp output/ Alarm decson EFDAS output CO Fg.3. The general archtecture of the fuzzy expert system The attenuaton flter s controlled by a parameter τ generated by a fuzzy controller whch set the tmeframe, n seconds, for applyng the attenuaton over the orgnal sgnal from the sensors. The archtecture of the attenuaton fler s depcted n Fg.4. u(t) Attenuaton flter y(t) Fuzzy controller τ Fg.4. The archtecture of the attenuaton flter The output y (t) s gven by the followng equaton: 0, t < t + τ y( t) = (2) u( t + τ ), t = t + τ where u(t) nput sgnal (T d or S d ) at the tme moment t

9 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 259 y(t) output attenuated sgnal (T d or S d ) τ attenuaton tme duraton [s] Bascally the attenuaton flter wll operate n a sequental manner: 1. Frstly, wll cut to zero the nput sgnal ampltude u(t); 2. After the tmeframe gven by τ wll let pass the sgnal unspoled at the moment t + τ ; 3. Return to step 1. After the attenuaton flter the varables T d and S d are enterng nto the fuzzfcaton block of the fuzzy expert system, thus resultng the lngustc varables { T ~ S ~ d, d }, each of them havng three lngustc values {low, medum, hgh}. The FES output represents the EFDAS decson to trgger the fre alarm or not, whch s bascally an electrcal sgnal whose characterstcs express the status of EFDAS. In Fg.5 s depcted the fre detectors response under a fre stuaton gven by a wooden smoulderng fre (wood pyrolyss). Dependng on the fre safety scenaro whch should take nto account the fre behavour, occupants reactons, the fre brgade summonng tme, ths can mply havng a shorter tmeframe between the pre-alert (AP) and the fre alarm (AI) than the one performed naturally by the fre detector. The fre detecton response n concordance wth the fre safety scenaro (deal response) s depcted wth the green lne. Such mplementaton based on fuzzy technques can solve the ssue n a very smple way by re-adjustng few parameters usng the user nterface. Fg. 5. Fre detecton response: natural (blue) and n concordance wth the fre safety scenaro (green)

10 260 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg Takng ths nto consderaton, the authors propose a fre detecton algorthm whose desgn mpled the completon of the followng steps: - selecton of most relevant nput varables we used the fve nput varables mentoned above as beng the most relevant for determnng the fre condtons; - Desgn of the attenuaton flters drven by dedcated fuzzy controllers; - Selecton of most sutable type of membershp functons for nput / output varables and calculaton of specfc parameters we used a combnaton of membershp functon types: trangle, trapeze, gauss and dfference sgmod; - Elaborate the rule base startng from the requrement of ensurng the fre safety and effcency of actve fre safety measures the rule base s composed of 9 fundamental rules: (1) IF S ~ a s Low and S ~ d s Low and T ~ a s Low and d s Low THEN ~ y s NU. (2) IF S ~ a s Medum and T ~ a s Medum and AP. (3) IF S ~ a s Hgh or a T ~ s Hgh or C ~ CO C ~ CO (4) IF S ~ a s Low and S ~ d s Hgh THEN ~ y s AP. T ~ s Low and C ~ CO s Medum THEN y ~ s s Hgh THEN y ~ s AI. (5) IF S ~ a s Low and S ~ d s Low and T ~ a s Medum and THEN ~ y s AI. (6) IF T ~ a s Medum and T ~ d s Hgh THEN ~ y s AI. (7) IF S ~ a s Medum and S ~ d s Hgh THEN ~ y s AI. C ~ CO s Hgh (8) IF S ~ a s Medum and S ~ d s Low and T ~ a s Medum and T ~ d s Low and C ~ CO s Low THEN y ~ s NU. (9) IF S ~ a s Low and S ~ d s Low and T ~ a s Low and T ~ d s Hgh and C ~ CO s Low THEN ~ y s NU. 5. Expermental valdaton of the proposed algorthm For establshng a relable data base the authors performed a set of 20 realscale fre detecton tests followng varous fre scenaros wth dfferent compact / lqud fuels. The result was a data base wth more than 280,000 data representng values of fre parameters such as temperature, smoke densty, concentraton of carbon monoxde and ther varaton n tme (gradent). For measurement and montorng t was used professonal equpment, as well as a modern analogue

11 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 261 addressable EFDAS. Pont type fre detectors were nstalled, havng the followng sensor combnaton: - D1(OTC) multsensor optcal/thermc/chemcal - D5(OT) multsensor optcal/thermc - D3, D4, D6 (O) optcal smoke detector - D2 (T) heat detector The temperature was montored by usng four thermocouples type K, wth the measurement unverse C and an adequate montorng and recordng system (DataLogger). The tests were run n a dedcated space from a real buldng (S+P+2) under constructon. The enclosure s dmensons were 506x430x305 cm (LxWxH) havng a vertcal openng 220x90cm and a central beam wth 23cm heght and 35cm depth. Fg.6 presents a drawng of the test enclosure on whch s depcted the lay-out of the used equpment. Fg. 6. Sketch of the test enclosure, the exact geometry and the lay-out of the fre detecton and montorng equpment The central beam separates the enclosure nto two fre compartments and wll play a crucal role n the transport of smoke and hot gases from one compartment to the other. The tests were run wth varous combustble materals, followng the specfcatons of the standard test fres TF1 TF5: burnng flame beech wood, smoulderng beech wood, cotton fre, polyurethane fre and lqud fre (mx of desel and gasolne). Throughout the tests, the fuel quantty was modfed, as well as the burnng place n the enclosure and the senstvty of fre detectors.

12 262 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg Usng the fuzzy toolbox and Smulnk tool from Matlab, the functonng of the fre detectors was smulated by runnng the proposed detecton algorthm wth the values of the nput parameters obtaned from the expermental tests. OBTINUTE Output EFDAS / t 16 Measured output / t 16 Fre Alarm (AI) Output EFDAS / t 7 Measured output / t 7 Fre Alarm (AI) Output EFDAS Pre-Alert (AP) Output EFDAS Pre-Alert (AP) Tme [s] a Tme [s] b Output EFDAS / t 1 Measured output / t 1 Output EFDAS / t 18 Measured output / t 18 Fre Alarm (AI) Fre Alarm (AI) Output EFDAS Pre-Alert (AP) Output EFDAS Pre-Alert (AP) Tme [s] c Tme [s] d Output EFDAS / t 6 Measured output / t 6 Fre Alarm (AI) Output EFDAS Pre-Alert (AP) e e Fg.7. Output EFDAS: a) beech wood flamng fre; b) beech wood smoulderng fre; c) cotton smoulderng fre; d) polyurethane fre; e) lqud fre (mx of desel and gasolne) Tme [s]

13 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 263 In Fg.7 are depcted the results for each category of tests, accordng to the burnng nature blue lne shows the EFDAS output as gven by the proposed algorthm and the green lne represents the behavour of the tested system (the output values as measured durng the tests). It was notced that for all fre types the fre detecton s faster for both prealert (AP) and fre alarm (AI) thresholds. At the same tme the system mantans a good reslence towards false alarm producton. The bggest dfference between the calculated response tme and the one measured durng the tests peaked at 94 seconds (s) n the case of the smoulderng beech wood test (Fg.7.b), whch represents a substantal reducton of the detecton tme and a major ncrease of the evacuaton tme for occupants. Equally, n the case of lqud fres due to the fre dynamcs and massve smoke producton, wth an ncreased rate of optcal densty, the proposed algorthm turns the system nto pre-alert status followed shortly by the fre alarm (Fg.7.e). The dfference between the calculated response tme and the measured tme s 26s for pre-alert and 59 s for the fre alarm. A specal case was the beech wood flamng fre (Fg.7.a) n whch the tested system ddn t trggered the fre alarm but only rasng twce the pre-alert level even though the fre detectors were confgured at maxmum senstvty. By usng the proposed algorthm the fre was accurately detected and the fre alarm was trggered at 602 s, manly as a consequence of hgh carbon monoxde concentraton. The results are centralzed n Table 1 and graphcally depcted n Fg. 8 and 9, accordng to the fre alarm category (pre-alert or fre alarm). For a quanttatve comparson, on the same graph are depcted the response tme values measured durng the fre test for a multsensor fre detector, type optcal/thermc/chemcal (OTC). Table 1 Response tme values: calculated (EFDAS) and measured (expermental fre tests) Test Response tme Response tme Combustble materal category EFDAS [s] measured [s] AP AI AP AI t 1 Cotton smoulderng fre t 6 Lqud fre (mx of desel and gasolne) t 7 Beech wood smoulderng fre t 16 Beech wood flamng fre N/A t 18 Polyurethane fre * N/A not applcable (fre detector ddn't reach that state)

14 264 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg Response tme pre-alert (AP) Response Tme [s] t 1 t 6 t 7 t 18 t 16 Fg. 8. Comparson response tme values to pre-alert threshold (AP) EFDAS [s] Measured [s] Fre test category Response tme fre alarm (AI) Response Tme [s] EFDAS [s] Measured [s] t 1 t 6 t 7 t 18 t 16 Fg. 9. Comparson response tme values to fre alarm threshold (AI) Fre test category The proposed fuzzy expert system allows easy adjustment of the detecton algorthm n order to be used for other applcatons as well. For nstance, by convenent adjustment of membershp functons parameters for nput varables, cuttng-off the attenuaton flters for S d and T d, deleton from the rule base of the last two rules (8) and (9), as well as reducng the weght of the rule (4) to 50%, a new fre detecton algorthm wll be obtaned whch can be used for an EFDAS confgured for a hgher level of senstvty, to be appled n protected objectves

15 Effcency ncrease for electrcal fre detecton and alarm systems through mplementaton (...) 265 where human actvty s lmted and the envronment s more stable wthout temperature varaton, lmted dust crculaton, etc. 6. Conclusons Electrcal fre detecton and alarm systems (EFDAS) based on classcal fre detectors response, presents several lmtatons for certan applcatons due to the necessty of usng fre detector whch are not standardzed for the tme beng. Fuzzy expert systems due to ther specfc flexblty proved to be very useful for fre detecton. The fuzzy expert system proposed n ths paper showed a faster fre detecton capablty and a better reslence to transent phenomenon responsble for false alarms producton. The use of several types of membershp functons for each varable nvolved n the nference process allow the optmzaton of an EFDAS for a specfc applcaton wthout nvolvng specalzed components. It s to be emphaszed that an EFDAS based on fuzzy expert systems, n order to be hghly relable, requre access to data bases wth expermental data as accurate as possble and n accordance wth the real envronmental condtons. R E F E R E N C E S [1] Pârlog-Crstan, R., Homeag, I. Aspecte prvnd gestunea tehncă a clădrlor moderne, Electrcanul nr. 8/2003 [2] Homeag, I., Consderaţ prvnd utlzarea sstemelor de detectare ş alarmare la ncendu în zone rezdenţale. A IX-a Confernţă Internaţonală de Apărare Împotrva Incendlor ş Catastrofelor, septembre 2008, Băle Felx, Româna [3] Homeag, I. Referat doctorat: Ssteme expert pentru stngerea automată a ncendlor, Facultatea de Ingnere Electrcă, UPB, Bucureşt 2009, p.7-17 [4] Şerban M. Ssteme de detecţe ş alarmă la ncendu, Edtura Mnsterulu Admnstraţe ş Internelor, Bucureşt, 2009, ISBN [5] Sler, W., Buckley, J. Fuzzy expert systems and fuzzy reasonng. USA, 2005, ISBN , p.2-3 [6] Caluanu, S. Intelgenţă Artfcală în nstalaţ / Logca fuzzy ş teora posbltăţlor, Matrx Rom Buc 2000, ISBN , p.8-9 [7] Passno, K., Yurkovch, S. Fuzzy control, 1998, ISBN X, p [8] Klr G. J., Yuan B. Fuzzy Sets and Fuzzy Logc: Theory and Applcatons. Prentce-Hall, Englewood Clffs, NJ, 1995.

16 266 Ionuţ-Lucan Homeag, Radu Pârlog-Crstan, Mrcea Covrg [9] Svanandam S. N., Sumath S., Deepa S. N. Introducton to Fuzzy Logc usng MATLAB, 2007, ISBN Sprnger Berln Hedelberg New York

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