Standard laser-based fire detection systems are often based on measuring the variation of optical signal amplitude. However, mechanical noise interference and loss from dust and steam can obscure the detection signal, resulting in faulty results or the inability to detect a potential fire. The presented fire detection technology will allow the detection of fire in harsh and dusty areas, which are prone to fires, where current systems show limited performance or are unable to operate. It is not the amount of light or its wavelength that is used for detecting fire, but how the refractive index randomly fluctuates due to heat convection from the fire. In practical terms, this means that light obstruction from ambient dust particles will not be a problem as long as a small fraction of the light is detected and that fires without visible flames can still be detected. The standalone laser system consists of a Linux-based Red Pitaya system, a cheap 650 nm laser diode, and a positive-intrinsic-negative photo-detector. Laser light propagates through the monitored area and reflects off a retroreflector generating a speckle pattern. Every 3 s, time traces and frequency noise spectra are measured, and eight descriptors are deduced to identify a potential fire. Both laboratory and factory acceptance tests have been performed with success.

Figures (5)

Fig. 1. Left image: forward-looking infrared (FLIR) images of the heat flow on a 1 cm thin cardboard plate, for visualization of the heat convection and temperature of the measurement point (marked with the circle). The sensor was placed 10 m away. The temperature gradient is clearly seen and is indicated with the arrow. The measurement point has a temp of approximately 28°C, while 25 cm below the laser spot, the temp is approximately 38°C. The heat flow is generated with a heat gun situated 1 m below the surface. The heat gun has a temperature of approximately 260°C. Right images: measured speckle patterns with a beam profiler with 5 s separation. Download Full Size | PPT Slide | PDF

Fig. 2. Block diagram of the sensor head, and SOLIDWORKS drawings of the prototype sensor head. The standalone Linux-based Red Pitaya system with a Python application is not shown. Download Full Size | PPT Slide | PDF

Fig. 3. Recorded data in laboratory environment. Range is 101 meters from sensor to retroreflector. The heat source (a heat gun) is placed halfway between the laser and retro-reflector, 30 cm under the beam. The figure shows voltage time traces for 10 s measurement time and the associated FFT noise spectrum for (a) with no heat source (heat gun), (b) with a heat gun, and (c) without a heat gun, but with heavy mechanical vibration of the sensor. The red line is a linear fit to the FFFt noise data. The time domain variance σ 2 and the R 2 of the linear fit in the frequency domain are two descriptors that can be indicative of a fire. Corresponding values are shown on the figure. Download Full Size | PPT Slide | PDF

Fig. 4. Recorded data in an outdoor environment when smoke/dust is obscuring the light beam. Range is 10.5 m from the sensor to the retro-reflector. The heat source (a heat gun) is placed halfway between the laser and retro-reflector, 30 cm under the beam. The figure shows voltage time traces for 10 s measurement time and the associated FFFt noise spectrum. The red line is a linear fit to the FFT noise data. Values of the voltage standard deviation and R 2 of the linear fit of the spectrum are given. Download Full Size | PPT Slide | PDF