Fire detection with low power fet gas sensors

Pohle, Roland; Simon, Elaine; Schneider, R.; Fleischer, Maximilian; Sollacher, Rudolf; Gao, Haichang; Müller, Klaus Robert; Jauch, P.; Loepfe, M.; Frerichs, H.-P.; Wilbertz, Christoph

doi:10.1016/j.snb.2006.03.040

Cited by 31 publications

(12 citation statements)

References 4 publications

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“…The requirements for fire detectors are defined in EN 54 [73] and are based on the timely detection of different types of test fires, i.e., smoldering and open fires, with and without smoke generation [101]. This is actually one of the main obstacles for introducing gas sensor-based fire detectors as the standard has been written with respect to smoke and heat detectors.…”

Section: Fire Detection In Buildingsmentioning

confidence: 99%

“…This seems to exclude many gas sensor principles depending on heated sensors, i.e., MOS-type sensors. Thus, electrochemical cells and gas-sensitive field-effect sensors operating at room temperature are proposed for fire detectors [101,102]. False alarm suppression, however, usually requires multisensor arrays to be used.…”

Section: Fire Detection In Buildingsmentioning

confidence: 99%

“…Hoefer et al have studied multisensor systems combining MOS sensors, GasFET, EC cells for CO and NO 2 , an NDIR cell for CO 2, and temperature and humidity sensors [101] (see Fig. 12).…”

Section: Fire Detection In Buildingsmentioning

confidence: 99%

“…The sensor systems proved stable during field testing over one year even Fig. 12 (a) Multisensor array with various gas sensors, temperature, and humidity sensors for fire detection studies; (b) sensor signals obtained from the multisensor array for a smoldering wood fire (EN 54, test fire 2) in a room with heated ceiling leading to a significantly delayed response of the smoke detector due to a heat layer at the ceiling; (c) modeling of the CO distribution resulting from smoldering cotton shows that the highest concentrations are directly at the ceiling, i.e., the location of the gas sensor system ( [101], reproduced with permission) under harsh environments, for example, in a cafeteria kitchen. At this test sight, an optical smoke detector laced alongside generated more than 600 false alarms plus additional error messages.…”

Section: Fire Detection In Buildingsmentioning

confidence: 99%

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Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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Selectivity will remain one of the main challenges for gas sensors. The majority of single gas sensors have a broad response spectrum and additional effort is required to provide specific information from the recorded raw data. Sensor arrays comprising a number of different sensors, either of the same basic type or combining different sensor principles, are often employed to improve the selectivity. Another approach is the dynamic operation of a single sensor element leading to a virtual multisensor. Depending on the application, both classification, i.e., recognition of a certain gas or gas mixture, and quantification, i.e., determining the concentration of a target gas, are required. For evaluation of the higher-dimensional data vectors obtained from the sensor system, different methods are commonly used, e.g., unsupervised and supervised multivariate statistics or artificial neural networks. For comprehensive classification, a multistep process is employed, typically comprising data preprocessing, feature extraction, dimensionality reduction, and finally classification. This chapter addresses typical methods used for gas sensor data evaluation and also briefly addresses challenges relating to sensor calibration and drift compensation. Four application examples are shown in detail demonstrating the use of different sensor configurations and data processing approaches: robust ozone quantification, sweat odor assessment trained by correlation with a human sensory panel, fire detection in buildings, and finally underground fire detection in coal mines requiring very high selectivity.

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Section: Fire Detection In Buildingsmentioning

confidence: 99%

Section: Fire Detection In Buildingsmentioning

confidence: 99%

“…Hoefer et al have studied multisensor systems combining MOS sensors, GasFET, EC cells for CO and NO 2 , an NDIR cell for CO 2, and temperature and humidity sensors [101] (see Fig. 12).…”

Section: Fire Detection In Buildingsmentioning

confidence: 99%

Section: Fire Detection In Buildingsmentioning

confidence: 99%

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Sensor Arrays, Virtual Multisensors, Data Fusion, and Gas Sensor Data Evaluation

Reimann

Schütze

2013

Springer Series on Chemical Sensors and Biosensors

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“…Nevertheless, the chemical species in fire gases which is generated in concentrations accessible with available sensor technologies are limited [2]. CO as a main target gas for fire detection is evaluated in this work.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Colorimetric Sensor System for Early Fire Detection

et al. 2018

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The use of colorimetric reactions as a sensitive, selective and low-cost method for the detection of fire related gases is evaluated in this work. As the most relevant fire gas carbon monoxide CO is addressed in the first place. A rhodium-based metal-organic complex has been selected, which a colorimetric material sensitive for CO. For the optical readout a compact setup has been developed which provides the option for spectroscopy in transmission and attenuated total reflection in parallel. The detection of CO in concentrations relevant for fire detection has been demonstrated in synthetic gas mixtures as well as in real fire gas.

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