Gas sensor technologies for fire detection

Gutmacher, D.; Hoefer, Ulrich; Wöllenstein, Jürgen

doi:10.1016/j.snb.2011.11.053

Cited by 58 publications

(37 citation statements)

References 6 publications

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“…Hence, fire detectors based on chemical sensing could provide a faster fire alarm than smoke-based detection systems [2]. The challenge, however, is the large number of situations (nuisances) that also generate volatiles and that may lead to false alarms [3][4]. In this way, chemical sensing systems for fire detection rely heavily on machine learning, signal processing and pattern recognition techniques to provide a reliable fire prediction [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Solórzano

Fonollosa

Marco

2017

Proceedings of Eurosensors 2017, Paris, France, 3–6 September 2017

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Chemical sensing may be better suited than conventional smoke-based detectors for the detection of certain type of fires, in particular in fires where smoke appears after gas emissions. However, chemical-based systems also respond to non-fire scenarios that also release volatiles. For this reason, discrimination models need to be trained under different fire and non-fire scenarios. This is usually performed in standard fire rooms, the access to which is very costly. In this work, we present a calibration model combining experiments from standard fire room and small-scale setup. Results show that the use of small-scale setup experiments improve the performance of the system.

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Section: Introductionmentioning

confidence: 99%

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Solórzano

Fonollosa

Marco

2017

Proceedings of Eurosensors 2017, Paris, France, 3–6 September 2017

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“…In many modern applications multiple chemical sensors are used in combination with pattern recognition tools in order to achieve amazing selectivity, which can e.g. allow differentiation of different types of fire and non-fire situations to prevent false alarms in fire detection applications [1,2].…”

Section: Gas Measurement Systemsmentioning

confidence: 99%

Integrated Sensor Systems for Indoor Applications: Ubiquitous Monitoring for Improved Health, Comfort and Safety

Schütze

2015

Procedia Engineering

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People spend an increasing amount of their time indoors. Thus, environmental conditions and air quality but also safety aspects are of specific concern in indoor settings. This contribution gives an overview of chemical sensor technologies used for indoor applications, focusing on explosive gas leak and fire detection as well as indoor air quality as the most relevant applications. These applications are traditionally addressed with a variety of technologies, such as metal-oxide semiconductor gas sensors for gas leak detection or infrared sensors for CO 2 monitoring. Increased requirements led to multisensor developments especially for security applications such as fire detection which demand a high reliability of the sensor system. In recent years, modern signal processing techniques have increasingly been used to overcome typical problems of chemical sensors, especially limited selectivity as well as drift and aging. Some examples for state-of-the-art chemical sensor systems for indoor applications are given showing the growing potential in this area.

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“…On the other hand, fire detection, as applications in safety and security in general, demands high reliability, especially with zero false-negative results and an acceptable suppression of false alarms. Even though gas sensors have been successful in niche applications for many years, more research is required [99,100].…”

Section: Fire Detectionmentioning

confidence: 99%

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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Gas sensor technologies for fire detection

Cited by 58 publications

References 6 publications

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Improving Calibration of Chemical Gas Sensors for Fire Detection Using Small Scale Setups

Integrated Sensor Systems for Indoor Applications: Ubiquitous Monitoring for Improved Health, Comfort and Safety

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

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