Research On Precaution System For Electrical Fire Using An Electronic Nose

Electrification contributes to great progress in productivity and civilization of the world.However it also causes a great number of electrical fires.Since 1990s,the proportion of electrical fires kept on increasing in China,as well as the loss of property and lives.And the same was found abroad.The predominating mechanism of electrical fire is abnormal high temperature caused by overloads,short circuit,poor connections,arcing,leakage,lightning or static electricity,which may cause ignition of the combustible substance or spontaneous combustion of wires.At present the electrical fire detection methods include wire surface temperature detection and electromagnetism principle based detection,which may cause large number of false alarms and maintenance difficulty.Considering that high temperature will cause thermal degradation of electrical insulation materials and consequently specific gas is released,we introduced electronic nose technology to application of early stage electrical fire precaution.It was able to detect the electrical failure in finite time and realize precaution.After theoretical analysis of thermal characteristics of electrical insulation material and thermal degradation process of commonly used PVC wire,we observed the PVC wire being heated and found that before the release of smoke mass organic gas could be detected by GC.The concentration increased as the heating temperature increased,while the components changed a bit.This result is basis of sensor array design.Due to the need of real-time detection,as well as the environmental factor of wire,an active sampling electronic nose was designed.Under the operation of sampling pump,the majority of released gas is collected to the air chamber. Therefore the influence of wind to gas diffusion is reduced greatly and the sampling efficiency is increased too,thus results in fast response of sensor array.The gas chamber provides the sensor array a relatively stable environment,which enhances stability and reliability of the system.The experimental results also indicated that,the active sampling mode has the quicker speed of response than that of passive diffusion mode,which lengthens the precaution time significantly.A solid adsorption based enrichment system is induced,which can also further enhance the performance of the system. Carbon nanotube as a new kind of gas sensitive material has high sensitivity, low detecting limits and can work on normal temperature,which made it the hot spot at present research.Pretreatment and the nano-metal-particle doping of multi-wall carbon nanotube(MWCNT)were studied.Different MWCNT solutions were drop-deposited onto interdigitated Au electrodes on porous Al₂O₃ substrate to make amperometric gas sensors respectively.The performance of the sensors was carefully studied.Theoretical analysis and experiments indicated that the nano-metal-particle doping changed structure and the electricity characteristic of MWCNT,thus they had obvious difference in gas sensitivity and selectivity,which were essential to form a sensor array in an electronic nose.Different response patterns of sensor array were obtained through simulant electrical failure of different breakdown conditions.Time domain threshold rule, statistical principle based algorithm and the artificial neural network were chosen as signal processing methods to work out the electrical fire precaution model.The results indicated all three algorithms could effectively detect hidden trouble of electrical wires,and the precaution time was much lager than that reported in existing literature.Time domain threshold algorithm and statistical principle based algorithm lack of the ability to distinguish between real electrical failure and nuisance,because gas sensors always have poor selectivity and will response to a lot of nuisances as cigarette smoke.And thus results in false alarms.Pattern recognition technology of artificial neural network can not only detect the response of the sensor array,but also can identify whether it is caused by gases released by wires or other disturbance gas according to the characteristics of the sensor response,thus achieves low false alarm rate and enhances reliability and stability of the system.Due to the learning ability of ANN,the system can easily extend to similar applications.