Multisensor/Multicriteria Fire Detection: A New Trend Rapidly Becomes State of the Art

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.

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. Indeed, the results show that such systems can be adapted to respond to a substantially wider spectrum of fire phenomena, such as visible/invisible and black/white smoke, aerosols, and temperature, while remaining much less sensitive to deceptive phenomena that result from cigarette smoke, welding, spray aerosols, dust, humidity, and so on. This new technology not only contributes to improved life safety, it also reduces the probability of nuisance alarms.

In this paper, the principle building blocks that lead to the improved detection behavior will be outlined and results from actual installations will be presented. In particular, the effect of application-specific parametrization will be demonstrated. The performance of multisensor/multicriteria fire detection systems will also be compared to that of systems that depend on single sensor inputs only.