Advanced fire detection system using infrared diagnostics

April 2006 Fire & Safety

Future fire detection systems should have the ability of discriminating signatures between fire and non-fire sources, because nuisance alarm problems have plagued existing smoke detectors.

In high value installations such as semiconductor clean rooms and telephone central offices, it is obvious that reliable fire detection systems are needed, since usually these detection systems are used to activate fixed fire suppression systems and false discharges are certainly undesirable. False alarms can cause unnecessary down time and undermine the operator's confidence in the monitoring systems. In light of these, a new fire detection system using infrared diagnostics (FT-IR spectroscopy) together with advanced signal processing technique (artificial neural networks) has been developed at Advanced Fuel Research ( This new fire detection system promises to provide an early warning of hazardous conditions and has the ability to determine whether the hazardous conditions are from fire or nuisance/environmental sources.


It has been shown that multiparameter fire detection systems are inherently more reliable than any single parameter measurement and can be made robust by the use of artificial intelligence methods. The objective of Advanced Fuel Research's research efforts is to use an advanced Fourier Transform Infrared gas analyser to develop an intelligent fire detection system that can be used in high value facilities. The company has made extensive FT-IR gas measurements of flaming and smouldering fires as well as environmental/nuisance sources. The FT-IR measurements were made in open-path, cross duct, and extractive modes for flaming fires, while measurements of smouldering fires and environmental/nuisance sources were performed in extractive mode, since most of the current fire detection technologies (eg, VESDA and AnaLaser) for cleanrooms and telephone central offices are based on air sampling techniques in which the air samples from multiple locations of the rooms are drawn and delivered through an extensive piping network to a particle analyser. The FT-IR system can be easily incorporated in this type of fire detection system, and comparison can be made with existing technologies.

Numerous materials were tested, including polyurethane (PU), polyvinylchloride (PVC), polymethylmethacrylate (PMMA), polypropylene (PP), polystyrene (PS), Douglas Fir wood (DF), low density polyethylene (LDPE), aqueous ammonia (NH3), tetrafluoromethane (CF4), isopropanol alcohol (IPA), cables, etc. Figure 1 shows part of a spectrum (2700-3100 cm-1) from a smouldering fire of a regular extension cable (with a PVC jacket). The evolution of HCl is evident, although the HCl band is overlapped somewhat with a hydrocarbon band.

Figure 1. FT-IR spectral region indicating HCl evolution from overheated wire cable
Figure 1. FT-IR spectral region indicating HCl evolution from overheated wire cable

Figure 2 shows concentrations of some fuel specific species. N2O and formaldehyde were clearly observed in a smouldering-flaming Douglas Fir fire test shown in the figure. Similar observations can be made for other materials tested.

Figure 2. Gas concentration of Douglas Fir fire test
Figure 2. Gas concentration of Douglas Fir fire test

The species concentrations measured by an FT-IR, together with a neural network and fuzzy logic models, can be used to identify whether there is a fire or nonfire (environmental/nuisance) event and to classify whether it is a flaming or smouldering fire if the event is indeed a fire. A commercially available neural network software package, NeuralWorks Professional II/Plus (), was chosen to build the needed neural network. A so-called Learning Vector Quantisation (LVQ) network has been built and tested (Figure 3). The inputs to the network at this moment are concentrations (18 species from FT-IR measurements) of CO2, CO, H2O, CH4, CH3OH, formaldehyde, HCl, C2H4, N2O, NH3, CF4, NO, methyl methacrylate, isopropanol alcohol, C2H6, C3H6, C6H14, C2H2, C6H6. The outputs of the network are classification of the input data as a flaming fire, smouldering fire, or nuisance/environmental source. The results were very successful, as among the 248 cases tested only 12 cases were misclassified, most due to the difficulties in classifying the modes of combustion during a transition from smouldering to flaming fire.

Figure 3. A learning vector quantisation (LVQ) network to characterise fire and non-fire events
Figure 3. A learning vector quantisation (LVQ) network to characterise fire and non-fire events

Advanced Fuel Research has incorporated the above-trained LVQ network into its data acquisition system that connects with an On-Line 2010 multigas spectrometer. A realtime fire detection system has been constructed. Preliminary tests of this integrated software have been satisfactory using the test data we described above.

However, these tests are in no way rigorous, as only data from a single test arrangement has been used. New tests (other burning materials, geometric arrangement, etc) are needed in order to validate the accuracy and improve the robustness of the new fire detection.


Milke, JA and McAvoy, TJ, Analysis of signature patterns for discriminating fire detection with multiple sensors, Fire Technology, Second Quarter 1995.

Serio, MA, Bonanno, AS, Knight, KS, Wójtowicz, MA and Solomon, PR, Advanced infrared systems for detection of building fires, Final Report to DOC under Contract No. 50-DKNA-4-000-96, February 1995.

Okayama, Y, Ito, T and Sasaki, T, Design of neural net to detect early stage of fire and evaluation by using real sensors' data, Fire Safety Science-Proc. of 4th Int'l Symposium, pp. 751-759, 1993.

Okayama, Y, A primitive study of a fire detection method controlled by artificial neural net, Fire Safety Journal, pp. 535-553, 17, 1991.

Chen, Y, Sathyamoorthy, Y, And Michael A. Serio, An intelligent fire detection system using advanced infrared diagnostics and neural network techniques, The Eastern States Meeting of the Combustion Institute, October, 1997.

NeuralWare, Inc., NeuralWorks Professional II/Plus, Version 5.3, February 1997.

For more information contact Mike Serio, Advanced Fuel Research,,

Share this article:
Share via emailShare via LinkedInPrint this page

Further reading:

SafeQuip introduces lithium fire extinguishers
Fire & Safety Products & Solutions
With the use of Lithium batteries increasing in many types of portable devices and battery storage solutions, SafeQuip, in partnership with AVD Lithex, is introducing a fire extinguisher aimed at suppressing and extinguishing, and also preventing re-ignition of lithium fires.

Modern warehouses come with added fire risk
Fire & Safety Transport (Industry)
Along with increased investments in modern warehousing in Africa, there is also a need to focus on protecting warehouses from crime, however, fire can arguably be a greater danger to business continuity.

How to prevent solar inverter short-circuits and fires
Fire & Safety
With many South Africans installing inverters as part of solar power systems to mitigate the impact of load shedding in homes and at businesses, users need to be aware of the potential fire risks.

Fidelity SecureFire steps into critical fire response space
News & Events Fire & Safety
With the majority of fire stations around the country being crippled by a lack of resources to offer effective responses, Fidelity Fire Solutions has launched its own ‘first responder’ model, Fidelity SecureFire.

Long-distance connectivity with Simplex ES Net Life Safety
Johnson Controls Global Products Fire & Safety Products & Solutions
ES Net Network Bridge allows fire alarm system data to be transmitted across distant buildings via a customer’s existing network infrastructure, improving system-wide monitoring and control in facilities and campuses where life safety networks are often widely dispersed.

Elvey Group and Technoswitch part ways
Elvey Security Technologies Fire & Safety News & Events
The Elvey Group (a division of the Hudaco Group of Companies) is relinquishing its distributorship of the Technoswitch brand, following Hudaco’s acquisition of Brigit Fire.

From one month to 10 minutes
Dahua Technology South Africa Fire & Safety
Dahua has integrated technology with the inspection mode of the photovoltaic power station in order to create a robust system that can monitor the fire situation in the power plant and its surrounding areas 24/7.

Protecting poultry processing plant
Technoswitch Fire Detection & Suppression Fire & Safety
Grain Field Chickens, based in Reitz in the Orange Free State Province, features all the typical fire detection challenges one would expect to encounter in a food processing facility.

Fire prevention for energy systems
Technoswitch Fire Detection & Suppression Fire & Safety
With the significant push towards renewable energy, such as wind and solar, the demand for battery energy storage systems has grown exponentially, as has the need for fire safety solutions for these environments.

Fire risks in solar panel installations
Technoswitch Fire Detection & Suppression Fire & Safety
Installed global solar capacity doubled in three years from 2018, and the expectation is that in the next three years, it will more than double. In South Africa, the year-on-year growth from 2021 to 2022 was 24,90%.