基于小波分析及数据融合的电气火灾预报系统及应用研究
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摘要
火推动了人类社会的文明进步，而火灾却给人类带来了巨大的危害。随着现代社会经济的快速发展以及工业的不断繁荣，各种电气化产品的层出不穷，给火灾的发生提供了更大的可能性。多年来电气火灾的数量一直呈现居高不下的局面，而且损失惨重的重特大火灾往往也由电气火灾造成。传统的火灾预报由于探测技术、信号处理方法和理论研究的局限性，在电气火灾监测过程中时常会出现误报及漏报。
论文深入分析了电气火灾形成机理，在分析出电弧（电火花）和高温为电气火灾火源的根本形式的基础上，通过大量实验，深入研究了不同负载形式下的交流故障电弧燃烧时的电弧电压、电流波形特性后发现，交流电弧在燃烧过程中有潜在着的“零休现象”。故障电弧的“零休现象”特性，给故障电弧的检测拓宽了思路。提出了利用故障电弧检测与分析监测及预报电气火灾的方法。
运用小波函数对故障电弧电流信号进行了小波奇异性分析。构造了正交二次样条小波为小波函数，利用多孔算法的二进小波变换实现了快速小波变换算法。故障电弧周期零休现象这一特征信息用小波分析时表现为周期性的奇异点，因此提出了周期性奇异点检测故障电弧的新算法，并分析了该故障电弧检测算法的可行性和有效性。
在检测故障电弧发生的基础上，对电气火灾早期现场的主要特征信号进行了多参数实时监测，运用多信息融合技术完成了对所探测的电气火灾特征信息的融合，实现了电气火灾的准确辨识。设计了基于故障电弧的信息融合的三层模型，并运用我国标准火数据以及典型干扰数据进行了实验仿真，仿真结果表明，该融合模型能够很好地完成电气火灾的快速准确预报，有效地避免了电气火灾的误报和漏报。
采用集散控制方法，完成了基于故障电弧和多信息融合的电气火灾预报系统的系统设计。整个系统分为上下位机，下位机又分为主机和从机。下位机主要完成信号的采集、预处理以及传输，其中的主机可完成一定的信号处理与判断；上位机主要完成各种信号处理算法的实现、存储以及监控系统画面的实现。所研发的“电气火灾预报系统”经过了反复试验、调试并在多家应用单位进行了推广使用，较好地实现了电气火灾的预防。
论文在电气火灾预报方面进行了一定的研究工作，取得了一定的进展，但是，电气火灾仍然有许多值得研究的热点，例如，在故障电弧进一步与电气火灾其他参量的融合方面、电气火灾融合模型结构的优化方面、采用新型探测技术和探测器扩展现有系统的能力方面、其它领域的新技术（如激光图像粒径分群、激光前向/后向散射的应用）引发电气火灾探测技术的新途径方面以及电气火灾监测技术在与自动化、现代通讯技术、智能大厦技术的进一步结合使得电气火灾探测系统更趋于自动化、开放性和模块化等方面还会有更进一步的发展。
Fire promotes the civilization and progress of human society，while fire disaster givesmankind a great deal of harm. With the rapid development of economy and the growingprosperity of industry in modern society, various electrical products are emerged in anendless stream, which provides greater possibilities for the occurrence of the fire. In recentyears, there always occur a large number of electrical fires, and the disastrous&major firewith great loss always caused by electrical fire. Due to the limitations of detectiontechnology, signal processing method and theory research, the false positives and falsenegatives always happened in the process of electrical fire monitoring.
Paper deeply analyzed the electrical fire formation mechanism, based on the analysisof arc (spark) and high temperature are the fire source's fundamental form of electrical fire,thorough studied the different load form's AC arc fault voltage and current waveformscharacteristics through a lot of experiments, found out that AC arc has potential"zero-off-phenomenon" in the process of combustion.The arc fault's characteristics of"zero-off phenomenon" broaden the thinking of arc fault detection. Proposed the methodof the use of arc fault detection and monitoring to predict electrical fire.
Analyzed arc fault current signal through wavelet function, constructed orthogonalquadratic spline wavelet as wavelet function, used porous algorithm's dyadic wavelettransform to achieve fast wavelet transform algorithm. If use wavelet analysis, thecharacteristic of "cycle zero-off" of arc fault is expressed as periodic singular points,therefore proposed a new algorithm of periodic singular points to detect arc fault, andanalyzed the feasibility and effectiveness of the arc fault detection new algorithm.
On the basis of arc faults’ detection, multiple-parameter real-time monitor theelectrical fire scene's main feature signals, using multi-information fusion technology tocomplete the integration of fire information characteristics, realized electrical firerecognition. Designed information fusion's three-layer model and the experimentalsimulation is carried out by using our country’s standard fire data and typical disturbancedata, the simulation results showed that the fusion model can realize the electrical fire's quick and accurate alarm, effectively avoid the false positives and false negatives ofelectrical fire.
Distributed control concept is used to complete electric fire early warning system'sdesign based on arc fault and multiple information fusion technology. The whole system isdivided into upper and lower machine, and hypogynous machine is also divided into hostmachine and slave machine. The hypogynous machine's main task is signal acquisition,pretreatment and transmission, and the host machine can complete some signal processingand judgment; the upper machine's main task is the realization of all kinds of signalprocessing algorithms, data storage and the realization of monitoring system picture. Afterrepeated testing and debugging, the electrical fire forecast system has been used in severalapplication units, realized the prevention of electrical fire.
The paper finished some research work in electrical fire prediction and made someprogress, but there still has many hot spots worthwhile to research about electrical fire, forexample, there have further developments in arc fault further fusion with other electricalfire parameters, the optimization of electrical fire's fusion model structure, the use of newdetection technology and detector to extend the capabilities of existing systems, the leadof new ways of detecting technology of electrical fire by new technology in otherfields(such as laser image size grouping, laser forward/backward scattering applications,etc), electrical fire monitoring technology in further combine with automation, moderncommunication technology, intelligent building technology to make electrical firedetection system more and more automatic, openness and modularization, etc.
论文深入分析了电气火灾形成机理，在分析出电弧（电火花）和高温为电气火灾火源的根本形式的基础上，通过大量实验，深入研究了不同负载形式下的交流故障电弧燃烧时的电弧电压、电流波形特性后发现，交流电弧在燃烧过程中有潜在着的“零休现象”。故障电弧的“零休现象”特性，给故障电弧的检测拓宽了思路。提出了利用故障电弧检测与分析监测及预报电气火灾的方法。
运用小波函数对故障电弧电流信号进行了小波奇异性分析。构造了正交二次样条小波为小波函数，利用多孔算法的二进小波变换实现了快速小波变换算法。故障电弧周期零休现象这一特征信息用小波分析时表现为周期性的奇异点，因此提出了周期性奇异点检测故障电弧的新算法，并分析了该故障电弧检测算法的可行性和有效性。
在检测故障电弧发生的基础上，对电气火灾早期现场的主要特征信号进行了多参数实时监测，运用多信息融合技术完成了对所探测的电气火灾特征信息的融合，实现了电气火灾的准确辨识。设计了基于故障电弧的信息融合的三层模型，并运用我国标准火数据以及典型干扰数据进行了实验仿真，仿真结果表明，该融合模型能够很好地完成电气火灾的快速准确预报，有效地避免了电气火灾的误报和漏报。
采用集散控制方法，完成了基于故障电弧和多信息融合的电气火灾预报系统的系统设计。整个系统分为上下位机，下位机又分为主机和从机。下位机主要完成信号的采集、预处理以及传输，其中的主机可完成一定的信号处理与判断；上位机主要完成各种信号处理算法的实现、存储以及监控系统画面的实现。所研发的“电气火灾预报系统”经过了反复试验、调试并在多家应用单位进行了推广使用，较好地实现了电气火灾的预防。
论文在电气火灾预报方面进行了一定的研究工作，取得了一定的进展，但是，电气火灾仍然有许多值得研究的热点，例如，在故障电弧进一步与电气火灾其他参量的融合方面、电气火灾融合模型结构的优化方面、采用新型探测技术和探测器扩展现有系统的能力方面、其它领域的新技术（如激光图像粒径分群、激光前向/后向散射的应用）引发电气火灾探测技术的新途径方面以及电气火灾监测技术在与自动化、现代通讯技术、智能大厦技术的进一步结合使得电气火灾探测系统更趋于自动化、开放性和模块化等方面还会有更进一步的发展。
Fire promotes the civilization and progress of human society，while fire disaster givesmankind a great deal of harm. With the rapid development of economy and the growingprosperity of industry in modern society, various electrical products are emerged in anendless stream, which provides greater possibilities for the occurrence of the fire. In recentyears, there always occur a large number of electrical fires, and the disastrous&major firewith great loss always caused by electrical fire. Due to the limitations of detectiontechnology, signal processing method and theory research, the false positives and falsenegatives always happened in the process of electrical fire monitoring.
Paper deeply analyzed the electrical fire formation mechanism, based on the analysisof arc (spark) and high temperature are the fire source's fundamental form of electrical fire,thorough studied the different load form's AC arc fault voltage and current waveformscharacteristics through a lot of experiments, found out that AC arc has potential"zero-off-phenomenon" in the process of combustion.The arc fault's characteristics of"zero-off phenomenon" broaden the thinking of arc fault detection. Proposed the methodof the use of arc fault detection and monitoring to predict electrical fire.
Analyzed arc fault current signal through wavelet function, constructed orthogonalquadratic spline wavelet as wavelet function, used porous algorithm's dyadic wavelettransform to achieve fast wavelet transform algorithm. If use wavelet analysis, thecharacteristic of "cycle zero-off" of arc fault is expressed as periodic singular points,therefore proposed a new algorithm of periodic singular points to detect arc fault, andanalyzed the feasibility and effectiveness of the arc fault detection new algorithm.
On the basis of arc faults’ detection, multiple-parameter real-time monitor theelectrical fire scene's main feature signals, using multi-information fusion technology tocomplete the integration of fire information characteristics, realized electrical firerecognition. Designed information fusion's three-layer model and the experimentalsimulation is carried out by using our country’s standard fire data and typical disturbancedata, the simulation results showed that the fusion model can realize the electrical fire's quick and accurate alarm, effectively avoid the false positives and false negatives ofelectrical fire.
Distributed control concept is used to complete electric fire early warning system'sdesign based on arc fault and multiple information fusion technology. The whole system isdivided into upper and lower machine, and hypogynous machine is also divided into hostmachine and slave machine. The hypogynous machine's main task is signal acquisition,pretreatment and transmission, and the host machine can complete some signal processingand judgment; the upper machine's main task is the realization of all kinds of signalprocessing algorithms, data storage and the realization of monitoring system picture. Afterrepeated testing and debugging, the electrical fire forecast system has been used in severalapplication units, realized the prevention of electrical fire.
The paper finished some research work in electrical fire prediction and made someprogress, but there still has many hot spots worthwhile to research about electrical fire, forexample, there have further developments in arc fault further fusion with other electricalfire parameters, the optimization of electrical fire's fusion model structure, the use of newdetection technology and detector to extend the capabilities of existing systems, the leadof new ways of detecting technology of electrical fire by new technology in otherfields(such as laser image size grouping, laser forward/backward scattering applications,etc), electrical fire monitoring technology in further combine with automation, moderncommunication technology, intelligent building technology to make electrical firedetection system more and more automatic, openness and modularization, etc.
引文
[1]景国勋,张悦.火灾事故致因的多因素灰色关联分析[J].中国安全科学学报.2009,19(3):93-97.
[2]公安部消防局指挥中心.6月30日,天津市蓟县城关镇文安街中昌北路莱德商厦发生火灾[EB/OL].(2012-07-01)[2012-07-01]. http://www.119.gov.cn/xiaofang/hzsd/17741.htm.
[3] Liew S.C, Lim A, Kwoh L.K. A stochastic model for active fire detection using the thermalbands of MODIS data[J]. IEEE Geoscience and Remote Sensing Letters.2005,2(3):337-341.
[4] Wang S, Berentsen M, Kaiser T. Signal processing algorithms for fire localization usingtemperature sensor arrays[J]. Fire Safety Journal.2005,40(8):689-697.
[5] Zhang X.F., Cao A.Y., Sun Q.H., et al. Patterned growth of well-alignedcarbon nanotube arrayson quartz substrates by chemical vapor deposition[J]. Journal of Inorganic Materials.2003,18(3):613-618.
[6]张嵩,王剑.光纤光栅传感技术在隧道火灾监控中的应用[J].激光与红外,2010,40(2):179-180.
[7]魏世明,柴敬,许力.煤矿用光纤Bragg光栅火灾探测系统研究[J].工矿自动化,2010,5:40-42.
[8]陆振华.光纤温度传感系统在火灾探测中的应用[J].低压电器,2007,20:27-31.
[9]胡玉祥,王俊山.感温线缆式火灾定位报警控制器的研制[J].工矿自动化,2006,3:42-45.
[10] Jun F, Yuan H.Y. Experimental measurements, integral modeling and smoke detection of earlyfire in thermally stratified environments[J]. Fire Safety Journal,2007,42(1):11-24.
[11] Xie Q.Y., Yuan H.Y., Song L.W., et al. Experimental studies on time-dependent size distributionsof smoke particles of standard test fires[J].Building and Environment.2007,42(2):640-646.
[12] Jun F, Jie J, Yuan H.Y., et al. Early fire smoke movements and detection in high large volumespaces[J]. Building and Environment.2006,41(11):1482-1493.
[13] Li Z.Q., Khananian A, Fraser R.H., et al. Automatic detection of fire smoke using artificial neuralnetworks and threshold approaches applied to AVHRR imagery[J]. IEEE Transactions onGeoscience and Remote Sensing.2001,39(9):1859-1870.
[14] Keller A, Ruegg M, Forster M, et al. Open photoacoustic sensor as smoke detector[J].Sensorsand Actuators B-Chemical.2005,104(1):1-7.
[15] Shu X.M., Yuan H.Y., Su G.F., et al. A new method of laser sheet imaging-based fire smokedetection[J]. Journal of Fire Sciences.2006,24(2):95-104.
[16] Hall J.R. The latest statistics on U.S home smoke detectors[J]. Fire Journal.1989,83(1):39-41.
[17] Pfister G. Multisensor/multicriteria fire detection: A new trend rapidly become state of the art[J].Fire technology,1997,33:115-139.
[18]吴仲城,戈瑜,虞承端,等.嵌入式智能型火灾报警探测器的设计[J].传感器技术,2000,19(2):20-22.
[19]龚伟.复合型智能火灾探测器的设计与研究[J].低压电器,2009,24:25-28.
[20]黄晓玲.紫外型火焰检测器的研制[J].仪器仪表学报,1999,20(5):523-525,550.
[21]陈珏.散射式感烟红外报警探测器机理的探讨[J].激光与红外,2000,30(6):355-357.
[22]张晓华,张认成,龚雪,等.火灾早期过程特征的红外光谱探测和诊断[J].红外与毫米波学报.2006,25(5):382-385.
[23]姚秋霞,李民,莫崇典.红外技术探测原理及其在工业消防领域的应用[J].电气应用,2006,8:40-43,47.
[24]杜建华,张认成.基于傅里叶变换红外光谱探测的火灾早期过程特征信息研究[J].光谱学与光谱分析,2008,28(3):559-563.
[25]庞建莹,施云波,修德斌,等.基于红外传感器的电气火灾预警系统[J].仪表技术与传感器,2010,3:64-66,110.
[26]董政,黄大贵,黄逸平,等.紫外火焰探测器响应时间测试系统的研究[J].电子科技大学学报,2006,35(4):682-685.
[27] Gottuk D.T., Lynch J.A., Rose-Pehrsson S.L., et al. Video image fire detection for shipboarduse[J]. Fire Safety Journal.2006,41(4):321-326.
[28] Marbach G, Loepfe M, Brupbacher T. An image processing technique for fire detection in videoimages[J]. Fire Safety Journal.2006,41(4):285-289.
[29]马宗方,程咏梅,潘泉,等.基于颜色模型和稀疏表示的图像型火焰探测[J].光子学报.2011,40(8):1220-1224.
[30]廖勇,王慧琴.肖立波.基于边界链码幅度谱的图像型火灾探测算法[J].计算机工程,2011,37(24):210-212.
[31]卢结成,吴龙标,宋卫国.一种火灾图像探测系统的研究[J].仪器仪表学报,2001,22(4):437-440
[32]史海山,吕厚余,仲元红,等.基于遗传神经网络的火灾图像识别及应用[J],计算机科学,2006,33(11):233-236.
[33]杨娜娟,王慧琴,马宗方.基于支持向量机的图像型火灾探测算法[J].计算机应用,2010,30(4):1129-1131,1140.
[34]王媛彬,马宪民.基于特征融合的图像型火灾探测方法[J],计算机工程,2011,37(19):166-167,176.
[35]季萍,卢结成.一种总线制火灾图像探测系统的设计与实现[J].计算机工程与应用,2004,26:212-215.
[36] Pohle R, Simon E, Schneider R, et al. Fire detection with low power fet gas sensors[J]. Sensorsand Actuators B-Chemical.2007,120(2):669-672.
[37] Nebiker P.W., Pleisch R.E. Photoacoustic gas detection for fire warning[J]. Fire Safety Journal.2002,37(4):427-429.
[38] Takei Y, Tashiro T, Misawa T, et al. Odor sensor system for early fire detection and its applicationto utility mobile robot[J]. Sensors and Materials.2005,17(7):413-421.
[39]黄湘莹,张认成. CO气体传感器在火灾探测中的应用[J].仪表技术与传感器.2006,6:6-8,11.
[40]纪新明,吴飞蝶,王建业,等.用于火灾探测的非色散红外吸收气体传感器[J].传感技术学报,2006,19(3):602-605,610.
[41]杜建华,张认成,黄湘莹,等. CO和CO2气体红外光谱技术在火灾早期探测中的应用研究[J].光谱学与光谱分析.2007,27(5):899-903.
[42] Chen T, Su G.F., Yuan H.Y. In situ gas filter correlation: photoacoustic CO detection method forfire warning[J]. Sensors and Actuators B-Chemical.2005,109(2):233-237.
[43] Ryan M.A., Zhou H.Y., Buehler M.G., et al. Monitoring space shuttle air quality using the jetpropulsion laboratory electronic nose[J]. IEEE Sensors Journal.2004,4(3):337-347.
[44]陈东,刘文清,张玉钧.可调谐半导体激光光谱火灾气体探测系统[J].中国激光,2006,33(11):1552-1556.
[45]夏慧,刘文清,张玉钧,等.可调谐半导体激光吸收光谱法监测燃烧过程中CO浓度的变化[J].光谱学与光谱分析,2008,28(11):2478-2481.
[46]陈涛,苏国锋,袁宏永.光声和气体滤波技术相结合的CO探测方法[J].传感技术学报,2004,6:292-294,288.
[47]陈涛,苏国锋,申世飞.火灾烟气中CO和烟颗粒的光声复合探测方法[J].传感技术学报.2006,19(3):597-601.
[48]夏滑,董凤忠,涂郭结,等.基于新型长光程多次反射池的CO高灵敏度检测[J].光学学报,2010,30(9):2596-2601.
[49] Austin C.C., Wang D, Ecobichon D.J., et al. Characterization of volatile organic compounds insmoke at experimental fires[J].Journal of Toxicology and Environmental Health-Part A,2001,63(3):191-208.
[50] Austin C.C., Wang D, Ecobichon D.J., et al. Characterization of volatile organic compounds insmoke at municipal structural fires[J].Journal of Toxicology and Environmental Health-Part A.2001,63(6):437-458
[51]吴方堃,王跃思,安俊琳,等.北京奥运时段VOCs浓度变化、臭氧产生潜势及来源分析研究[J].环境科学,2010,31(1):10-16.
[52]慈晓琳,张海涛,瞿贤,等.上海化学工业区污水处理厂废气中挥发性有机物主要成分的测定[J].中国环境监测,2010,26(2):16-20.
[53]刘兴隆,曾立民,陆思华,等.大气中挥发性有机物在线监测系统[J].环境科学学报,2009,29(12):2471-2477.
[54]杜振辉,翟雅琼,李金义,等.空气中挥发性有机物的光谱学在线监测技术[J].光谱学与光谱分析.2009,29(12):3199-3203.
[55]晏乃强,吴祖成,施耀,等.催化剂强化脉冲放电治理有机废气[J].中国环境科学,2000,20(2):136-140.
[56]王海林,张国宁,聂磊,等.我国工业VOCs减排控制与管理对策研究[J].环境科学.2011,32(12):3462-3469.
[57]蒋静学,官洪运,范泳文,等.时频分析方法在燃烧音频信号分析中的应用研究[J].现代电子技术,2011,34(23):67-69.
[58]张雷,陆建华,梁新刚.微波探火及其在低空平台中的系统设计[J].计算机科学,2010,37(3):141-143.
[59]王芳,马幼军,蒋国平.智能化住宅防盗防火报警系统设计[J].传感器技术,2002,21(10):25-27,33.
[60]杨帆,彭宏伟,赵党军,等.基于微波技术的森林防火监控系统[J].华中科技大学学报(自然科学版),2007,35(12):50-52.
[61]陈晓军,杨立中,邓志华,等.电气线路红外热像检测和诊断[J].红外与毫米波学报,2000,19(6):463-466.
[62]范春利,孙丰瑞,杨立,等.电线电缆破损的定量热像检测与诊断方法研究[J].中国电机工程学报,2005,25(18):162-166.
[63] Furse C, Chung Y.C., Dangol R, et al. Frequency-domain reflectometry for on-board testing ofaging aircraft wiring[J]. IEEE Transactions on Electromagnetic Compatibility.2003,45(2):306-315.
[64]胡建勋,李开成.基于MSP430单片机的低压综合智能保护器的研制[J].仪器仪表学报.2006,27(6):2532-2533.
[65]张冠英,杨晓光,王尧,等.一种智能剩余电流检测装置的开发[J].低压电器.2011,2:28-31.
[66] Kuo H.C., Chang H.K. A real-time shipboard fire-detection system based on grey-fuzzyalgorithms[J]. Fire Safety Journal.2003,38(4):341-363.
[67] Nakanishis. Intelligent Fire Warning System Using Fuzzy Technology[J]. Journal of JapanSociety for Fuzzy Theory and Systems.1993,5(1):95-107.
[68] Nakanishis. Intelligent Fire Warning System Using Fuzzy Technology[C].AUBE’95.1995:203-212.
[69] Muller H.C., Fischer A. A Robust Fire Detection Algorithm for Gas temperature and OpticalSmoke Density using Fuzzy Logic[C]. Proceedings of29thIEEE Conference on SecurityTechnology.1995:197-204.
[70] Zhang Q, Wang S. A fire detection system based on ART-2neuro-fuzzy network[C], Proceedingof ICSP’98。1998:1355-1358.
[71] Rose-Pehrsson S.L.,et al. Multi-criteria fire detection systems using a probabilistic NeuralNetwork[J]. Sensors and Actuators,B.2000,69(3):325-335.
[72] Foo S.Y. A fuzzy logic approach to fire detection in aircraft dry bays and engine compartments[J].IEEE Transactions on Industrial Electronics.2000,47(5):1161-1171.
[73] Li Z.B., Zhou H. Research on the Application of Fuzzy Data Fusion to Cable Fire DetectingSystem[C]. Proceedings of3rdInternational Conference on Machine Learning andCybernetics.2004,08:26-29.
[74]王忠勇.基于模糊控制理论和神经网络的建筑火灾报警系统[J].中国安全科学学报.2004,14(4):109-112.
[75]王利清.基于模糊神经网络的火灾信号探测方法研究[J].仪器仪表学报,2001,22(6):595-598.
[76] LEE W, YUEN K, SE M, et al. Application of Fuzzy ARTMAP to Investigation of FirePhenomena[J]. Journal of Basic Science and Engineering.2004,12:89-94.
[77] Wang L.X., Wang H. R., Kang Q.C. Fire Detection Model Based on Fuzzy RBF NeuralNetwork[C]. Proceedings of International Conference on Safety Science and Technology2008.2008:880-883.
[78]马宪民，张德凤.火灾探测信息融合的一种新型实现方法[J].仪器仪表学报,2006,27(6):2138-2139.
[79]王锡淮,肖健梅,鲍敏中.模糊神经网络和遗传算法结合的船舶火灾探测[J].仪器仪表学报,2001,22(3):312-314.
[80]易继锴,张蔚蔚.模糊神经网络技术及其在火灾探测过程中的应用[J].北京工业大学学报.2001,27(3):337-341.
[81] Ono T, Ishii H, Kawamura K, et al.Application of neural network to analyses of CCD colourTV-camera image for the detection of car fires in expressway tunnels[J]. Fire Safety Journal.2006,41(4):279-284.
[82] Armando M.F, Andrei B. U, Alexander V.L, Development of neural network committee machinesfor automatic forest fire detection using lidar[J]. Pattern Recognition.2004,37(10):2039-2047.
[83] Okayama Y, Ito,Sasaki T. Design of neuralnet to detect early stage of fire and evaluation by usingreal sensors’ data. Fire Safety Science[C]. Proceedings of4thInternational Symposium.1994:751-810.
[84] Fischer A, Performance Studies of Multi-sensor Fire Detection Algorithm by Modeling of FireSignal[J]. EUSAS Newsleter.1994,(5):27-47.
[85] Mike J.A., Monitoring Multiple A spects of Fire Signatures for Discriminating Fire Detection[J].Fire Technology.1999,35(3):195-209.
[86] Shaffer R.E, Rose-Pehrsson S.L, et al. Development of an Early Warning Multi-Criteria FireDetection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network[J].NRL Memorandum Report.2000,2:235-242.
[87] HE Z.H.,PU J.Y., CAI Y.L. Multi-Sensor Fire Detection Algorithm for Ship Fire Alarm Systemusing Neural Fuzzy Network[C]. International Security Science and Technology ProgressConference.2002,1:142-146.
[88] Pei Y. N., Gan F.C., Xu X. Z., et al. Fire Detection Data Fusion System Based on NeuralNetwork Technology[C]. The7thInternational Testing Technology Seminar.2007,1:2358-2361.
[89] Zhu H.W. Study on the tunnel fire detection based on BP Neural Network[C].2009InternationalSymposium on Fire Science and Fire-Protection Engineering.2009,1:420-424.
[90] Chen G.Y.,Sun T. J., Zhong M.Y., et al. Fire Alarming and Controlling System Based onMultisensor Data Fusion Technology[C]. Asia Pacific Symposium on Safety2005.2005,7:1115-1119.
[91] Ma X.M. Application of Data Fusion Theory in Coal Gas Fire Prediction System[C].International Conference on Intelligent Computation Technology and Automation2008.2008,3:572-575.
[92]冉海潮,孙丽华,郭英军.基于BP神经网络的智能火灾探测系统[J].传感技术学报.2000,6:153-155.
[93]汤正华,王殊,陈涛.多传感器/多判据探测器在火灾探测中的应用[J].传感器技术.2001,20(3):33-38.
[94]马宗方,程咏梅,潘泉,等.基于快速支持向量机的图像型火灾探测算法[J].计算机应用研究.2010,27(10):3985-3987.
[95]程彩霞,孙富春,周心权.基于径向基函数神经网络的矿井智能火灾探测方法[J].煤炭科学技术,2011,39(2):65-68.
[96] Chen T.H.,Wu P. H., Chiou Y. C.. An early fire-detectin method based on imageprocessing[C].ICIP’04.2004,3(10):1707-1710.
[97]郭国忠.森林火灾蔓延的三维实时仿真[J].中国图像图形学报Ａ,2003,8(z1):743-747.
[98]张晓凯.电气线路火灾[J].分析科学学报.2000,16(5):406-409.
[99]袁宏永.基于热像技术的电缆火险隐患诊断研究[J].中国科学技术大学学报.2000,30(1):108-112.
[100]吴龙标.一种火灾图像探测系统的研究[J].仪器仪表学报.2001,22(4):422-425.
[101]卢结成,吴龙标,宋卫国.一种火灾图像探测系统的研究[J].仪器仪表学报.2001,22(4):437-440.
[102]陈涛,袁宏永,苏国锋,等.基于CCD图像的火灾空间定位方法[J].光学技术,2003,29(3):292-294.
[103]刘毅,乔妮,姜恩来. KM阻燃剂森林草原防火的实验研究[J].北京林业大学学报,2010,32(2):161-164.
[104]张志祥.35kV电力电缆隧道防火措施[J].高电压技术,2001,27(7):19-21.
[105]王其平.电气电弧理论[M].北京:机械工业出版社,1982.12:1-10.
[106]孟庆海,牟龙华等.基于电流线性衰减模型分析低能电弧放电特性[J].煤炭学报.2001,26(12):120-123.
[107] Wu H.R., Li X.H., Stade D, et al. Arc fault model for Low-voltage AC systems[J]. IEEETransactions On Power Delivery.2005,20(2):1204-1205.
[108] Arunachalam S, Diong B. A Parametric Model Approach to Arc fault Detection for DC and ACPower Systems[C]. Industry Applications Conference.2006,5:2249-2255.
[109] Spyker R, Schweickart D.L, Horwath J.C, et al. An evaluation of diagnostic techniques relevantto arcing fault current interrupters for direct current power systems in future aircraft[C].Electrical Insulation Conference and Electrical Manufacturing Expo.2005,10:146-150.
[110] Restrepo C.E. Arc Fault Detection and Discrimination Methods[C]. Electrical contacts2007. the53thIEEE holm conference.2007,9:115-122.
[111] Sidhu T S, Sachdev M S, Sagoo G S. Detection and Location of Low-level Arcing Faults inMetal-clad electrical apparatus[C].7thInternational Conference on Developments in PowerSystem Protection.2001,2:157-160.
[112] Charytoniuk W, Lee W.J., Chen M.S., et al. Arcing Fault Detection in Underground DistributionNetworks-feasibility Study[J]. IEEE Transactions on Industry Applications.2000,36(6):1756-1761.
[113] Sergio H, Luis S,Luis I. E. Application of the Short-term Discrete Fourier Transform to AC arcFurnace Power Quality Studies[C]. Proceedings of the3rdIASTED International Conference onPower and Energy Systems. ACTA PRESS.2003:114–119.
[114] Lin Y. H., Liu C.W., Chen C.S. A New PMU-Based Fault Detection/Location Technique forTransmission Lines With Consideration of Arcing Fault Discrimination-Part I： Theory andAlgorithms[J]. IEEE Transactions on Power Delivery.2004,19(4):1587–1593.
[115] Lin Y.H., Liu C.W., Chen C.S. A New PMU-Based Fault Detection/Location Technique forTransmission Lines With Consideration of Arcing Fault Discrimination-Part II:PerformanceEvaluation[J]. IEEE Transactions on Power Delivery.2004,19(4):1594–1601.
[116] Li W. J., Li Y. C. Arc Fault Detection Based On Wavelet Packet. Machine Learning andCybernetics[C],2005. Proceedings of2005International Conference,2005,3:1783–1788.
[117] Ortiz V H, Uribe F A, Gutierrez J A R. et al. Arcing Faults Patterns for Based ANN Relays forTransmission Lines[C].2003IEEE Bologna Power Technology Conference.2003,2:7-11.
[118] James A. M, Kumar D M. V. et al. Lab VIEW Based Implementation Action for DC Arcing ofRemedial Faults in a Spacecraft[C]. Power Engineering Society General Meeting.2003,1:491-498.
[119] James A. M, Robbert B. Design and Analysis of Aerospace DC Arcing Faults Using Fast FourierTransformation and Artificial Neural Network[C]. Power Engineering Society General Meeting，2003，2：788–793.
[120] Kim C.H., Kim H.Y., Ko Y.H., et al. A Novel Fault-Detection Technique of High-impedanceArcing Faults in Transmission Lines Using the Wavelet Transform[J]. IEEE Transaction onPower Delivery.2002,17(4):921–929.
[121]陈征,张峰,蔡鹏,等.弧隙间距对故障电弧特性影响的试验研究[J].低压电器,2011,20:1-4,39.
[122]雍静,桂小智,牛亮亮,等.基于自回归参数模型的低压系统串联电弧故障识别[J].电工技术学报,2011,26(8):213-219.
[123]于然,付周兴,王清亮,等.基于MATLAB的电弧建模仿真及故障分析[J].高压电器,2011,47(9):95-99,106.
[124]姜斌峰,王莉.低压交流电线故障电弧模型研究[J].电力系统及其自动化学报.2009,21(4):20-24.
[125]蔡彬,陈德桂,李志鹏,等.中压开关柜中内部电弧故障的仿真与实验[J].电工技术学报,2004,19(3):82-87.
[126]蔡彬,陈德桂.中压开关柜中内部电弧故障的计算方法和防护措施[J].高压电器,2003,39(1):8-11.
[127]林莘,何柏娜,徐建源.超高压输电线路的潜供电弧频谱特性.高电压技术[J],2009,35(8):1891-1895.
[128]陈维江,颜湘莲,贺子鸣.特高压交流输电线路单相接地潜供电弧仿真[J].高电压技术,2010,36(1):1-6.
[129]蓝会立,张认成.基于小波分析的故障电弧伴生弧声特征提取[J].电力系统及其自动化学报,2008,20(4):57-62.
[130]杨建红,张认成,房怀英. Lyapunov指数法在故障电弧早期探测中的应用[J].电工电能新技术.2008,27(2):55-58.
[131]蓝会立,张认成.开关柜内部弧光短路伴生弧声的频谱特性研究[J].高压电器,2008,44(2):107-109.
[132]田广青.电弧光保护及其在中低压开关柜和母线保护中的应用[J].电工技术杂志,2004,1:27-30.
[133]杨茜,荣命哲,吴翊.低压断路器中空气电弧重击穿现象的仿真与实验研究[J].中国电机工程学报,2007,27(6):84-87.
[134]卢其威,王聪,程红,等.电弧故障断路器及故障电弧的辨别[J].电气应用,2009,28(24):48-52.
[135]贾宁，李宏文,魏幼平,等.一种新型电弧故障断路器的研究[J].低压电器,2011,1:18-21.
[136]范越,施围.在单相自动重合闸中检测电弧故障的新方法[J].继电器,1999,27(6):5-7.
[137]薛永端,徐丙垠,冯祖仁,等.小电流接地故障暂态方向保护原理研究[J].中国电机工程学报,2003,23(7):51-56.
[138]王静红,刘教民.基于双目立视技术的开关电弧图像处理[J].电工技术学报,2011,26(1):86-91.
[139]杨建红,张认成,房怀英.关联维数在故障电弧早期探测和保护中的应用[J].低压电器,2008,17:5-8.
[140]杨建红,张认成,房怀英. Duffing振子信号探测在故障电弧短路保护中的应用[J].电力系统自动化,2006,30(24):69-72.
[141]杨建红,张认成,房怀英. Lyapunov指数法在故障电弧早期探测中的应用[J].电工电能新技术,2008,27(2):55-58.
[142]任龙霞,吴为麟.虚拟仪器在低压电弧故障断路器研制中的应用[J].电力系统保护与控制,2011,39(5):134-138,143.
[143]孙鹏,董荣刚,郑志成.基于小波分析信号特征频段能量变比的故障电弧诊断技术研究[J].高压电器,2010,46(7):46-50,56.
[144]魏明果.实用小波分析[M].北京:北京理工大学出版社.2005.9:120-122.
[145]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社.2000:3-5.
[146] Igor V. M, Izidor G. Multi-sensor fusion: an Evolutionary algorithm approach[J]. InformationFusion.2006,7:304-330.
[147]黄浩忠.火灾自动报警系统简明设计手册[M].北京:中国建材工业出版社.2001.10:1-3.
[148]吴龙标,方俊,谢启源.火灾探测与信息处理[M].北京:化学工业出版社,2006:5-28.
[149]中国标准出版社第三编辑室.消防标准汇编﹒消防电子卷(第二版)[M].北京:中国标准出版社.2006.7::29-34.
[150] M A Jackson, I Robins, Gas sensing for fire detection; measurements of CO，CO2，H2，O2，andsmoke density in European standard fire tests[J].Fire safety journal,1994,22:180-205.
[151] DanielT.G, Michelle J.P, Richard J. R, et al. Advanced fire detection using multi-signaturealarm algorithms[J], Fire Safety Journal,2002.6.381-394.
[152] Faouzi Derbel.Performance improvement of fire detectors by means of gas sensors and neuralnetworks[J], Fire Safety Journal,2004(39):383-398.
[153]张毅刚,乔立岩.虚拟仪器软件开发环境Lab Windows/CVI6.0编程指南[M].北京:机械工业出版社,2002.20-50.
[2]公安部消防局指挥中心.6月30日,天津市蓟县城关镇文安街中昌北路莱德商厦发生火灾[EB/OL].(2012-07-01)[2012-07-01]. http://www.119.gov.cn/xiaofang/hzsd/17741.htm.
[3] Liew S.C, Lim A, Kwoh L.K. A stochastic model for active fire detection using the thermalbands of MODIS data[J]. IEEE Geoscience and Remote Sensing Letters.2005,2(3):337-341.
[4] Wang S, Berentsen M, Kaiser T. Signal processing algorithms for fire localization usingtemperature sensor arrays[J]. Fire Safety Journal.2005,40(8):689-697.
[5] Zhang X.F., Cao A.Y., Sun Q.H., et al. Patterned growth of well-alignedcarbon nanotube arrayson quartz substrates by chemical vapor deposition[J]. Journal of Inorganic Materials.2003,18(3):613-618.
[6]张嵩,王剑.光纤光栅传感技术在隧道火灾监控中的应用[J].激光与红外,2010,40(2):179-180.
[7]魏世明,柴敬,许力.煤矿用光纤Bragg光栅火灾探测系统研究[J].工矿自动化,2010,5:40-42.
[8]陆振华.光纤温度传感系统在火灾探测中的应用[J].低压电器,2007,20:27-31.
[9]胡玉祥,王俊山.感温线缆式火灾定位报警控制器的研制[J].工矿自动化,2006,3:42-45.
[10] Jun F, Yuan H.Y. Experimental measurements, integral modeling and smoke detection of earlyfire in thermally stratified environments[J]. Fire Safety Journal,2007,42(1):11-24.
[11] Xie Q.Y., Yuan H.Y., Song L.W., et al. Experimental studies on time-dependent size distributionsof smoke particles of standard test fires[J].Building and Environment.2007,42(2):640-646.
[12] Jun F, Jie J, Yuan H.Y., et al. Early fire smoke movements and detection in high large volumespaces[J]. Building and Environment.2006,41(11):1482-1493.
[13] Li Z.Q., Khananian A, Fraser R.H., et al. Automatic detection of fire smoke using artificial neuralnetworks and threshold approaches applied to AVHRR imagery[J]. IEEE Transactions onGeoscience and Remote Sensing.2001,39(9):1859-1870.
[14] Keller A, Ruegg M, Forster M, et al. Open photoacoustic sensor as smoke detector[J].Sensorsand Actuators B-Chemical.2005,104(1):1-7.
[15] Shu X.M., Yuan H.Y., Su G.F., et al. A new method of laser sheet imaging-based fire smokedetection[J]. Journal of Fire Sciences.2006,24(2):95-104.
[16] Hall J.R. The latest statistics on U.S home smoke detectors[J]. Fire Journal.1989,83(1):39-41.
[17] Pfister G. Multisensor/multicriteria fire detection: A new trend rapidly become state of the art[J].Fire technology,1997,33:115-139.
[18]吴仲城,戈瑜,虞承端,等.嵌入式智能型火灾报警探测器的设计[J].传感器技术,2000,19(2):20-22.
[19]龚伟.复合型智能火灾探测器的设计与研究[J].低压电器,2009,24:25-28.
[20]黄晓玲.紫外型火焰检测器的研制[J].仪器仪表学报,1999,20(5):523-525,550.
[21]陈珏.散射式感烟红外报警探测器机理的探讨[J].激光与红外,2000,30(6):355-357.
[22]张晓华,张认成,龚雪,等.火灾早期过程特征的红外光谱探测和诊断[J].红外与毫米波学报.2006,25(5):382-385.
[23]姚秋霞,李民,莫崇典.红外技术探测原理及其在工业消防领域的应用[J].电气应用,2006,8:40-43,47.
[24]杜建华,张认成.基于傅里叶变换红外光谱探测的火灾早期过程特征信息研究[J].光谱学与光谱分析,2008,28(3):559-563.
[25]庞建莹,施云波,修德斌,等.基于红外传感器的电气火灾预警系统[J].仪表技术与传感器,2010,3:64-66,110.
[26]董政,黄大贵,黄逸平,等.紫外火焰探测器响应时间测试系统的研究[J].电子科技大学学报,2006,35(4):682-685.
[27] Gottuk D.T., Lynch J.A., Rose-Pehrsson S.L., et al. Video image fire detection for shipboarduse[J]. Fire Safety Journal.2006,41(4):321-326.
[28] Marbach G, Loepfe M, Brupbacher T. An image processing technique for fire detection in videoimages[J]. Fire Safety Journal.2006,41(4):285-289.
[29]马宗方,程咏梅,潘泉,等.基于颜色模型和稀疏表示的图像型火焰探测[J].光子学报.2011,40(8):1220-1224.
[30]廖勇,王慧琴.肖立波.基于边界链码幅度谱的图像型火灾探测算法[J].计算机工程,2011,37(24):210-212.
[31]卢结成,吴龙标,宋卫国.一种火灾图像探测系统的研究[J].仪器仪表学报,2001,22(4):437-440
[32]史海山,吕厚余,仲元红,等.基于遗传神经网络的火灾图像识别及应用[J],计算机科学,2006,33(11):233-236.
[33]杨娜娟,王慧琴,马宗方.基于支持向量机的图像型火灾探测算法[J].计算机应用,2010,30(4):1129-1131,1140.
[34]王媛彬,马宪民.基于特征融合的图像型火灾探测方法[J],计算机工程,2011,37(19):166-167,176.
[35]季萍,卢结成.一种总线制火灾图像探测系统的设计与实现[J].计算机工程与应用,2004,26:212-215.
[36] Pohle R, Simon E, Schneider R, et al. Fire detection with low power fet gas sensors[J]. Sensorsand Actuators B-Chemical.2007,120(2):669-672.
[37] Nebiker P.W., Pleisch R.E. Photoacoustic gas detection for fire warning[J]. Fire Safety Journal.2002,37(4):427-429.
[38] Takei Y, Tashiro T, Misawa T, et al. Odor sensor system for early fire detection and its applicationto utility mobile robot[J]. Sensors and Materials.2005,17(7):413-421.
[39]黄湘莹,张认成. CO气体传感器在火灾探测中的应用[J].仪表技术与传感器.2006,6:6-8,11.
[40]纪新明,吴飞蝶,王建业,等.用于火灾探测的非色散红外吸收气体传感器[J].传感技术学报,2006,19(3):602-605,610.
[41]杜建华,张认成,黄湘莹,等. CO和CO2气体红外光谱技术在火灾早期探测中的应用研究[J].光谱学与光谱分析.2007,27(5):899-903.
[42] Chen T, Su G.F., Yuan H.Y. In situ gas filter correlation: photoacoustic CO detection method forfire warning[J]. Sensors and Actuators B-Chemical.2005,109(2):233-237.
[43] Ryan M.A., Zhou H.Y., Buehler M.G., et al. Monitoring space shuttle air quality using the jetpropulsion laboratory electronic nose[J]. IEEE Sensors Journal.2004,4(3):337-347.
[44]陈东,刘文清,张玉钧.可调谐半导体激光光谱火灾气体探测系统[J].中国激光,2006,33(11):1552-1556.
[45]夏慧,刘文清,张玉钧,等.可调谐半导体激光吸收光谱法监测燃烧过程中CO浓度的变化[J].光谱学与光谱分析,2008,28(11):2478-2481.
[46]陈涛,苏国锋,袁宏永.光声和气体滤波技术相结合的CO探测方法[J].传感技术学报,2004,6:292-294,288.
[47]陈涛,苏国锋,申世飞.火灾烟气中CO和烟颗粒的光声复合探测方法[J].传感技术学报.2006,19(3):597-601.
[48]夏滑,董凤忠,涂郭结,等.基于新型长光程多次反射池的CO高灵敏度检测[J].光学学报,2010,30(9):2596-2601.
[49] Austin C.C., Wang D, Ecobichon D.J., et al. Characterization of volatile organic compounds insmoke at experimental fires[J].Journal of Toxicology and Environmental Health-Part A,2001,63(3):191-208.
[50] Austin C.C., Wang D, Ecobichon D.J., et al. Characterization of volatile organic compounds insmoke at municipal structural fires[J].Journal of Toxicology and Environmental Health-Part A.2001,63(6):437-458
[51]吴方堃,王跃思,安俊琳,等.北京奥运时段VOCs浓度变化、臭氧产生潜势及来源分析研究[J].环境科学,2010,31(1):10-16.
[52]慈晓琳,张海涛,瞿贤,等.上海化学工业区污水处理厂废气中挥发性有机物主要成分的测定[J].中国环境监测,2010,26(2):16-20.
[53]刘兴隆,曾立民,陆思华,等.大气中挥发性有机物在线监测系统[J].环境科学学报,2009,29(12):2471-2477.
[54]杜振辉,翟雅琼,李金义,等.空气中挥发性有机物的光谱学在线监测技术[J].光谱学与光谱分析.2009,29(12):3199-3203.
[55]晏乃强,吴祖成,施耀,等.催化剂强化脉冲放电治理有机废气[J].中国环境科学,2000,20(2):136-140.
[56]王海林,张国宁,聂磊,等.我国工业VOCs减排控制与管理对策研究[J].环境科学.2011,32(12):3462-3469.
[57]蒋静学,官洪运,范泳文,等.时频分析方法在燃烧音频信号分析中的应用研究[J].现代电子技术,2011,34(23):67-69.
[58]张雷,陆建华,梁新刚.微波探火及其在低空平台中的系统设计[J].计算机科学,2010,37(3):141-143.
[59]王芳,马幼军,蒋国平.智能化住宅防盗防火报警系统设计[J].传感器技术,2002,21(10):25-27,33.
[60]杨帆,彭宏伟,赵党军,等.基于微波技术的森林防火监控系统[J].华中科技大学学报(自然科学版),2007,35(12):50-52.
[61]陈晓军,杨立中,邓志华,等.电气线路红外热像检测和诊断[J].红外与毫米波学报,2000,19(6):463-466.
[62]范春利,孙丰瑞,杨立,等.电线电缆破损的定量热像检测与诊断方法研究[J].中国电机工程学报,2005,25(18):162-166.
[63] Furse C, Chung Y.C., Dangol R, et al. Frequency-domain reflectometry for on-board testing ofaging aircraft wiring[J]. IEEE Transactions on Electromagnetic Compatibility.2003,45(2):306-315.
[64]胡建勋,李开成.基于MSP430单片机的低压综合智能保护器的研制[J].仪器仪表学报.2006,27(6):2532-2533.
[65]张冠英,杨晓光,王尧,等.一种智能剩余电流检测装置的开发[J].低压电器.2011,2:28-31.
[66] Kuo H.C., Chang H.K. A real-time shipboard fire-detection system based on grey-fuzzyalgorithms[J]. Fire Safety Journal.2003,38(4):341-363.
[67] Nakanishis. Intelligent Fire Warning System Using Fuzzy Technology[J]. Journal of JapanSociety for Fuzzy Theory and Systems.1993,5(1):95-107.
[68] Nakanishis. Intelligent Fire Warning System Using Fuzzy Technology[C].AUBE’95.1995:203-212.
[69] Muller H.C., Fischer A. A Robust Fire Detection Algorithm for Gas temperature and OpticalSmoke Density using Fuzzy Logic[C]. Proceedings of29thIEEE Conference on SecurityTechnology.1995:197-204.
[70] Zhang Q, Wang S. A fire detection system based on ART-2neuro-fuzzy network[C], Proceedingof ICSP’98。1998:1355-1358.
[71] Rose-Pehrsson S.L.,et al. Multi-criteria fire detection systems using a probabilistic NeuralNetwork[J]. Sensors and Actuators,B.2000,69(3):325-335.
[72] Foo S.Y. A fuzzy logic approach to fire detection in aircraft dry bays and engine compartments[J].IEEE Transactions on Industrial Electronics.2000,47(5):1161-1171.
[73] Li Z.B., Zhou H. Research on the Application of Fuzzy Data Fusion to Cable Fire DetectingSystem[C]. Proceedings of3rdInternational Conference on Machine Learning andCybernetics.2004,08:26-29.
[74]王忠勇.基于模糊控制理论和神经网络的建筑火灾报警系统[J].中国安全科学学报.2004,14(4):109-112.
[75]王利清.基于模糊神经网络的火灾信号探测方法研究[J].仪器仪表学报,2001,22(6):595-598.
[76] LEE W, YUEN K, SE M, et al. Application of Fuzzy ARTMAP to Investigation of FirePhenomena[J]. Journal of Basic Science and Engineering.2004,12:89-94.
[77] Wang L.X., Wang H. R., Kang Q.C. Fire Detection Model Based on Fuzzy RBF NeuralNetwork[C]. Proceedings of International Conference on Safety Science and Technology2008.2008:880-883.
[78]马宪民，张德凤.火灾探测信息融合的一种新型实现方法[J].仪器仪表学报,2006,27(6):2138-2139.
[79]王锡淮,肖健梅,鲍敏中.模糊神经网络和遗传算法结合的船舶火灾探测[J].仪器仪表学报,2001,22(3):312-314.
[80]易继锴,张蔚蔚.模糊神经网络技术及其在火灾探测过程中的应用[J].北京工业大学学报.2001,27(3):337-341.
[81] Ono T, Ishii H, Kawamura K, et al.Application of neural network to analyses of CCD colourTV-camera image for the detection of car fires in expressway tunnels[J]. Fire Safety Journal.2006,41(4):279-284.
[82] Armando M.F, Andrei B. U, Alexander V.L, Development of neural network committee machinesfor automatic forest fire detection using lidar[J]. Pattern Recognition.2004,37(10):2039-2047.
[83] Okayama Y, Ito,Sasaki T. Design of neuralnet to detect early stage of fire and evaluation by usingreal sensors’ data. Fire Safety Science[C]. Proceedings of4thInternational Symposium.1994:751-810.
[84] Fischer A, Performance Studies of Multi-sensor Fire Detection Algorithm by Modeling of FireSignal[J]. EUSAS Newsleter.1994,(5):27-47.
[85] Mike J.A., Monitoring Multiple A spects of Fire Signatures for Discriminating Fire Detection[J].Fire Technology.1999,35(3):195-209.
[86] Shaffer R.E, Rose-Pehrsson S.L, et al. Development of an Early Warning Multi-Criteria FireDetection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network[J].NRL Memorandum Report.2000,2:235-242.
[87] HE Z.H.,PU J.Y., CAI Y.L. Multi-Sensor Fire Detection Algorithm for Ship Fire Alarm Systemusing Neural Fuzzy Network[C]. International Security Science and Technology ProgressConference.2002,1:142-146.
[88] Pei Y. N., Gan F.C., Xu X. Z., et al. Fire Detection Data Fusion System Based on NeuralNetwork Technology[C]. The7thInternational Testing Technology Seminar.2007,1:2358-2361.
[89] Zhu H.W. Study on the tunnel fire detection based on BP Neural Network[C].2009InternationalSymposium on Fire Science and Fire-Protection Engineering.2009,1:420-424.
[90] Chen G.Y.,Sun T. J., Zhong M.Y., et al. Fire Alarming and Controlling System Based onMultisensor Data Fusion Technology[C]. Asia Pacific Symposium on Safety2005.2005,7:1115-1119.
[91] Ma X.M. Application of Data Fusion Theory in Coal Gas Fire Prediction System[C].International Conference on Intelligent Computation Technology and Automation2008.2008,3:572-575.
[92]冉海潮,孙丽华,郭英军.基于BP神经网络的智能火灾探测系统[J].传感技术学报.2000,6:153-155.
[93]汤正华,王殊,陈涛.多传感器/多判据探测器在火灾探测中的应用[J].传感器技术.2001,20(3):33-38.
[94]马宗方,程咏梅,潘泉,等.基于快速支持向量机的图像型火灾探测算法[J].计算机应用研究.2010,27(10):3985-3987.
[95]程彩霞,孙富春,周心权.基于径向基函数神经网络的矿井智能火灾探测方法[J].煤炭科学技术,2011,39(2):65-68.
[96] Chen T.H.,Wu P. H., Chiou Y. C.. An early fire-detectin method based on imageprocessing[C].ICIP’04.2004,3(10):1707-1710.
[97]郭国忠.森林火灾蔓延的三维实时仿真[J].中国图像图形学报Ａ,2003,8(z1):743-747.
[98]张晓凯.电气线路火灾[J].分析科学学报.2000,16(5):406-409.
[99]袁宏永.基于热像技术的电缆火险隐患诊断研究[J].中国科学技术大学学报.2000,30(1):108-112.
[100]吴龙标.一种火灾图像探测系统的研究[J].仪器仪表学报.2001,22(4):422-425.
[101]卢结成,吴龙标,宋卫国.一种火灾图像探测系统的研究[J].仪器仪表学报.2001,22(4):437-440.
[102]陈涛,袁宏永,苏国锋,等.基于CCD图像的火灾空间定位方法[J].光学技术,2003,29(3):292-294.
[103]刘毅,乔妮,姜恩来. KM阻燃剂森林草原防火的实验研究[J].北京林业大学学报,2010,32(2):161-164.
[104]张志祥.35kV电力电缆隧道防火措施[J].高电压技术,2001,27(7):19-21.
[105]王其平.电气电弧理论[M].北京:机械工业出版社,1982.12:1-10.
[106]孟庆海,牟龙华等.基于电流线性衰减模型分析低能电弧放电特性[J].煤炭学报.2001,26(12):120-123.
[107] Wu H.R., Li X.H., Stade D, et al. Arc fault model for Low-voltage AC systems[J]. IEEETransactions On Power Delivery.2005,20(2):1204-1205.
[108] Arunachalam S, Diong B. A Parametric Model Approach to Arc fault Detection for DC and ACPower Systems[C]. Industry Applications Conference.2006,5:2249-2255.
[109] Spyker R, Schweickart D.L, Horwath J.C, et al. An evaluation of diagnostic techniques relevantto arcing fault current interrupters for direct current power systems in future aircraft[C].Electrical Insulation Conference and Electrical Manufacturing Expo.2005,10:146-150.
[110] Restrepo C.E. Arc Fault Detection and Discrimination Methods[C]. Electrical contacts2007. the53thIEEE holm conference.2007,9:115-122.
[111] Sidhu T S, Sachdev M S, Sagoo G S. Detection and Location of Low-level Arcing Faults inMetal-clad electrical apparatus[C].7thInternational Conference on Developments in PowerSystem Protection.2001,2:157-160.
[112] Charytoniuk W, Lee W.J., Chen M.S., et al. Arcing Fault Detection in Underground DistributionNetworks-feasibility Study[J]. IEEE Transactions on Industry Applications.2000,36(6):1756-1761.
[113] Sergio H, Luis S,Luis I. E. Application of the Short-term Discrete Fourier Transform to AC arcFurnace Power Quality Studies[C]. Proceedings of the3rdIASTED International Conference onPower and Energy Systems. ACTA PRESS.2003:114–119.
[114] Lin Y. H., Liu C.W., Chen C.S. A New PMU-Based Fault Detection/Location Technique forTransmission Lines With Consideration of Arcing Fault Discrimination-Part I： Theory andAlgorithms[J]. IEEE Transactions on Power Delivery.2004,19(4):1587–1593.
[115] Lin Y.H., Liu C.W., Chen C.S. A New PMU-Based Fault Detection/Location Technique forTransmission Lines With Consideration of Arcing Fault Discrimination-Part II:PerformanceEvaluation[J]. IEEE Transactions on Power Delivery.2004,19(4):1594–1601.
[116] Li W. J., Li Y. C. Arc Fault Detection Based On Wavelet Packet. Machine Learning andCybernetics[C],2005. Proceedings of2005International Conference,2005,3:1783–1788.
[117] Ortiz V H, Uribe F A, Gutierrez J A R. et al. Arcing Faults Patterns for Based ANN Relays forTransmission Lines[C].2003IEEE Bologna Power Technology Conference.2003,2:7-11.
[118] James A. M, Kumar D M. V. et al. Lab VIEW Based Implementation Action for DC Arcing ofRemedial Faults in a Spacecraft[C]. Power Engineering Society General Meeting.2003,1:491-498.
[119] James A. M, Robbert B. Design and Analysis of Aerospace DC Arcing Faults Using Fast FourierTransformation and Artificial Neural Network[C]. Power Engineering Society General Meeting，2003，2：788–793.
[120] Kim C.H., Kim H.Y., Ko Y.H., et al. A Novel Fault-Detection Technique of High-impedanceArcing Faults in Transmission Lines Using the Wavelet Transform[J]. IEEE Transaction onPower Delivery.2002,17(4):921–929.
[121]陈征,张峰,蔡鹏,等.弧隙间距对故障电弧特性影响的试验研究[J].低压电器,2011,20:1-4,39.
[122]雍静,桂小智,牛亮亮,等.基于自回归参数模型的低压系统串联电弧故障识别[J].电工技术学报,2011,26(8):213-219.
[123]于然,付周兴,王清亮,等.基于MATLAB的电弧建模仿真及故障分析[J].高压电器,2011,47(9):95-99,106.
[124]姜斌峰,王莉.低压交流电线故障电弧模型研究[J].电力系统及其自动化学报.2009,21(4):20-24.
[125]蔡彬,陈德桂,李志鹏,等.中压开关柜中内部电弧故障的仿真与实验[J].电工技术学报,2004,19(3):82-87.
[126]蔡彬,陈德桂.中压开关柜中内部电弧故障的计算方法和防护措施[J].高压电器,2003,39(1):8-11.
[127]林莘,何柏娜,徐建源.超高压输电线路的潜供电弧频谱特性.高电压技术[J],2009,35(8):1891-1895.
[128]陈维江,颜湘莲,贺子鸣.特高压交流输电线路单相接地潜供电弧仿真[J].高电压技术,2010,36(1):1-6.
[129]蓝会立,张认成.基于小波分析的故障电弧伴生弧声特征提取[J].电力系统及其自动化学报,2008,20(4):57-62.
[130]杨建红,张认成,房怀英. Lyapunov指数法在故障电弧早期探测中的应用[J].电工电能新技术.2008,27(2):55-58.
[131]蓝会立,张认成.开关柜内部弧光短路伴生弧声的频谱特性研究[J].高压电器,2008,44(2):107-109.
[132]田广青.电弧光保护及其在中低压开关柜和母线保护中的应用[J].电工技术杂志,2004,1:27-30.
[133]杨茜,荣命哲,吴翊.低压断路器中空气电弧重击穿现象的仿真与实验研究[J].中国电机工程学报,2007,27(6):84-87.
[134]卢其威,王聪,程红,等.电弧故障断路器及故障电弧的辨别[J].电气应用,2009,28(24):48-52.
[135]贾宁，李宏文,魏幼平,等.一种新型电弧故障断路器的研究[J].低压电器,2011,1:18-21.
[136]范越,施围.在单相自动重合闸中检测电弧故障的新方法[J].继电器,1999,27(6):5-7.
[137]薛永端,徐丙垠,冯祖仁,等.小电流接地故障暂态方向保护原理研究[J].中国电机工程学报,2003,23(7):51-56.
[138]王静红,刘教民.基于双目立视技术的开关电弧图像处理[J].电工技术学报,2011,26(1):86-91.
[139]杨建红,张认成,房怀英.关联维数在故障电弧早期探测和保护中的应用[J].低压电器,2008,17:5-8.
[140]杨建红,张认成,房怀英. Duffing振子信号探测在故障电弧短路保护中的应用[J].电力系统自动化,2006,30(24):69-72.
[141]杨建红,张认成,房怀英. Lyapunov指数法在故障电弧早期探测中的应用[J].电工电能新技术,2008,27(2):55-58.
[142]任龙霞,吴为麟.虚拟仪器在低压电弧故障断路器研制中的应用[J].电力系统保护与控制,2011,39(5):134-138,143.
[143]孙鹏,董荣刚,郑志成.基于小波分析信号特征频段能量变比的故障电弧诊断技术研究[J].高压电器,2010,46(7):46-50,56.
[144]魏明果.实用小波分析[M].北京:北京理工大学出版社.2005.9:120-122.
[145]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社.2000:3-5.
[146] Igor V. M, Izidor G. Multi-sensor fusion: an Evolutionary algorithm approach[J]. InformationFusion.2006,7:304-330.
[147]黄浩忠.火灾自动报警系统简明设计手册[M].北京:中国建材工业出版社.2001.10:1-3.
[148]吴龙标,方俊,谢启源.火灾探测与信息处理[M].北京:化学工业出版社,2006:5-28.
[149]中国标准出版社第三编辑室.消防标准汇编﹒消防电子卷(第二版)[M].北京:中国标准出版社.2006.7::29-34.
[150] M A Jackson, I Robins, Gas sensing for fire detection; measurements of CO，CO2，H2，O2，andsmoke density in European standard fire tests[J].Fire safety journal,1994,22:180-205.
[151] DanielT.G, Michelle J.P, Richard J. R, et al. Advanced fire detection using multi-signaturealarm algorithms[J], Fire Safety Journal,2002.6.381-394.
[152] Faouzi Derbel.Performance improvement of fire detectors by means of gas sensors and neuralnetworks[J], Fire Safety Journal,2004(39):383-398.
[153]张毅刚,乔立岩.虚拟仪器软件开发环境Lab Windows/CVI6.0编程指南[M].北京:机械工业出版社,2002.20-50.
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