Advanced fire detection using multi-signature alarm algorithms *
TL;DR: In this article, the authors assess the feasibility of reducing false alarms while increasing sensitivity through the use of combined conventional smoke detectors with carbon monoxide (CO) sensors through an experimental program using both real (fire) and nuisance alarm sources.
About: This article is published in Fire Safety Journal.The article was published on 2002-06-01. It has received 101 citations till now. The article focuses on the topics: Fire detection & Constant false alarm rate.
Citations
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TL;DR: The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone.
156 citations
TL;DR: The response time of a proposed system for detecting fire hazard in a Bord-and-Pillar coal mine panel (Hustrulid and Bullock, 2001) is presented and results show that the average network delay varies almost linearly with the increasing the number of hops.
136 citations
TL;DR: A review of progress in fire detection technologies over the last decade, including various emerging sensor technologies, signal processing and monitoring technology (e.g., real-time control via Internet) and integrated fire detection systems.
Abstract: Progress in fire detection technologies has been substantial over the last decade due to advances in sensor, microelectronics and information technologies, as well as a greater understanding of fire physics. This paper provides a review of progress in fire detection technologies over the last decade, including various emerging sensor technologies (e.g., computer vision system, distributed fiber optic temperature sensor, and intelligent multiple sensor), signal processing and monitoring technology (e.g., real-time control via Internet) and integrated fire detection systems. Some problems and future research efforts related to current fire detection technologies are discussed.
120 citations
TL;DR: The structure of a network-based fire-detection system via the controller area network (CAN) and the design method of a CAN communication network are described to evaluate the feasibility of using such a home automation protocol in a smart home.
Abstract: This paper presents a network-based fire-detection system via the controller area network (CAN) to evaluate the feasibility of using such a home automation protocol in a smart home In general, a conventional fire-detection system has several shortcomings, such as weakness to noise, because it uses an analog transmission with 4-20 mA current lines Hence, as an alternative to the conventional system, this paper describes the structure of a CAN-based fire-detection system and the design method of a CAN communication network The performance of the proposed system is evaluated through experimental tests The CAN has several advantages, such as low cost and ease of implementation, as compared to the other low layers of the BACNet, such as Ethernet or ARCNET Therefore, if the CAN is selected for the low layer of the BACNet, a home automation system can be implemented more effectively
104 citations
TL;DR: The survey toxic emissions produced in fires and defined standards for fire detection systems are surveyed and the state of the art of chemical sensor systems forFire detection and the associated signal and data processing algorithms are reviewed.
Abstract: Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative.
97 citations
References
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TL;DR: Results from laboratory experiments and computer simulations, field tests, and a rapidly increasing number of real installations clearly demonstrate that systems using multisensor/multicriteria detection technology outperform systems that depend on single sensor inputs, such as ionization or photoelectric smoke detection or temperature sensing.
Abstract: The detection performance of fire alarm systems has recently improved substantially with the development of multisensor/multicriteria detection technology, a new generation of products that derive various alarm and diagnostic criteria from a combination of input signals from sensors responding to different fire phenomena. In an actual case, the signals from a photoelectric smoke sensor and a temperature sensor were combined using modern techniques of signal analysis, such as neural networks and fuzzy logic, which by far exceed commonly used simple logic. The underlying algorithms are parametrized to allow application-specific adaptation of the fire alarm system response behavior.
60 citations
TL;DR: In this paper, the authors investigate the patterns of signatures associated with fire and environmental sources using small-scale experiments and identify trends from which an elementary expert system can be formulated to identify the source of the airborne products.
Abstract: Incorporating intelligence into a fire detector so that it can recognize signature patterns is intended to permit prompt fire detection while allowing the detector to discriminate between signatures from fire and nonfire sources. The primary purpose of this preliminary study is to investigate the patterns of signatures associated with fire and environmental sources using small-scale experiments. We generated products from a wide range of conditions, from flaming or pyrolyzing samples, to heated samples and samples obtained with an atomizer. We also measured gas concentrations, light obscuration, and temperature to characterize the products. By analyzing the data, we identified trends from which an elementary expert system can be formulated to identify the source of the airborne products. Several patterns are evident. The maximum CO2 concentrations achieved during experiments with flaming fires are significantly greater than the maximum CO2 concentrations achieved during experiments with nonflaming fires (pyrolyzing fires, heated liquids, and environmental odors). The nonflaming sources can be identified based on the CO and metal oxide sensor peak measurements. Except for three experiments using pyrolyzing solids, the peak CO concentration is greater—though the Taguchi detector response is less—for nonflaming fires than for environmental sources. Subsequent application of a neural network properly classifies all except one pyrolyzing fire.
58 citations
Patent•
06 Jun 1996
TL;DR: In this paper, a multi-signature fire detection method and apparatus, utilizing first (1) and second (2) detectors for detecting first and second signatures, is presented, where a signal processor (3) is provided for combining the first (A) and the second (B) signals using a number of correlations.
Abstract: A multi-signature fire detection method and apparatus, utilizing first (1) and second (2) detectors for detecting first and second signatures. The first (1) detector outputs a first signal (A) indicative of the first detected fire signature, and the second detector (2) outputs a second signal (B) indicative a second detected fire signature. A signal processor (3) is provided for combining the first (A) and second (B) signals using a number of correlations, wherein outputs of the first (1) and second (2) detector means are coupled to the signal processor (3), and the signal processor (3) compares and combines the first (A) and second (B) signals to a first predetermined reference value (303), and outputs a fire condition signal if a combination of the first (A) and second (B) signals exceeds the predetermined reference value (303).
56 citations
TL;DR: A learned neural net capable of handling pattern-matching of analogue input and output which requires less memory size and time to make decisions and is capable of giving answers to any inputs without fail.
45 citations
TL;DR: In this paper, a system consisting of gas analyzers, Taguchi sensors and thermocouples has been used to determine traits of fire, and these traits have been used in order to determine whether a system of gas sensors, etc. is capable of detecting and discriminating between different types of fires.
29 citations