Abstract

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.

© 2019 Optical Society of America

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References

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2017 (1)

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

2014 (1)

2012 (1)

2010 (1)

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

2009 (1)

2007 (1)

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

2006 (1)

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

2005 (1)

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

2001 (1)

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

1999 (1)

1995 (1)

J. A. Milke and T. J. McAvoy, “Analysis of signature patterns for discriminating fire detection with multiple sensors,” Fire Technol. 31, 120–136 (1995).
[Crossref]

1992 (1)

G. T. Atkinson and D. D. Drysdale, “Convective heat transfer from fire gases,” Fire Safety J. 19, 217–245 (1992).
[Crossref]

1986 (1)

D. D. Evans and D. W. Stroup, “Methods to calculate the response time of heat and smoke detectors installed below large unobstructed ceilings,” Fire Technol. 22, 54–65 (1986).
[Crossref]

1980 (1)

1976 (1)

1974 (1)

S. L. Lee and J. M. Hellman, “Heat and mass transfer in fire research,” Adv. Heat Transf. 10, 219–284 (1974).
[Crossref]

1971 (1)

1968 (1)

D. I. Lawson, “Laser beam fire detector,” Fire Technol. 4, 257–264 (1968).

1967 (1)

Abboud, M.

Afif, C.

Ajay, K.

R. Knox, K. Ajay, and K. Boettger, “Particle detection,” U.S. patentUS8804119B2 (14August2011).

Almoro, P. F.

Alwahabi, Z. T.

Atkinson, G. T.

G. T. Atkinson and D. D. Drysdale, “Convective heat transfer from fire gases,” Fire Safety J. 19, 217–245 (1992).
[Crossref]

Bachor, H.-A.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Bakar, M. S. A.

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

Boettger, K.

R. Knox, K. Ajay, and K. Boettger, “Particle detection,” U.S. patentUS8804119B2 (14August2011).

Buck, A. L.

Chagger, R.

R. Chagger and D. Smith, “The causes of false fire alarms in buildings,” Report No. (BRE Global Ltd., 2014).

Chen, S.-J.

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

Chiba, T.

Clifford, S. F.

Dally, B. B.

Daniel, R. G.

Delaubert, V.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Dou, Z.

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

Drysdale, D. D.

G. T. Atkinson and D. D. Drysdale, “Convective heat transfer from fire gases,” Fire Safety J. 19, 217–245 (1992).
[Crossref]

Dubovinsky, M.

Evans, D. D.

D. D. Evans and D. W. Stroup, “Methods to calculate the response time of heat and smoke detectors installed below large unobstructed ceilings,” Fire Technol. 22, 54–65 (1986).
[Crossref]

Fabre, C.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Franklin, J.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

Friedman, J.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

Fuchs, P.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Gerschuetz, F.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Goodman, J. W.

Gunn, S. R.

S. R. Gunn, “Support vector machines for classification and regression,” (1998), Vol. 14, pp. 5–16.

Hanson, S. G.

Harb, C. C.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Hastie, T.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

Hellman, J. M.

S. L. Lee and J. M. Hellman, “Heat and mass transfer in fire research,” Adv. Heat Transf. 10, 219–284 (1974).
[Crossref]

Hildebrandt, L.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Hill, R. J.

Hollman, J. P.

J. P. Hollman, Heat Transfer (McGraw-Hill, 1976).

Hovde, D. C.

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

Isterling, W. M.

Knox, R.

R. Knox, K. Ajay, and K. Boettger, “Particle detection,” U.S. patentUS8804119B2 (14August2011).

Koeth, J.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Lam, P. K.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Lancaster, E. D.

Lassen, M.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Lawrence, R. S.

Lawson, D. I.

D. I. Lawson, “Laser beam fire detector,” Fire Technol. 4, 257–264 (1968).

Le Brun, G.

Le Jeune, B.

Lee, S. L.

S. L. Lee and J. M. Hellman, “Heat and mass transfer in fire research,” Adv. Heat Transf. 10, 219–284 (1974).
[Crossref]

Li, J.

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

Maallo, S.

Margarette, A.

Marshall, A. W.

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

McAvoy, T. J.

J. A. Milke and T. J. McAvoy, “Analysis of signature patterns for discriminating fire detection with multiple sensors,” Fire Technol. 31, 120–136 (1995).
[Crossref]

McNesby, K. L.

Milke, J. A.

J. A. Milke and T. J. McAvoy, “Analysis of signature patterns for discriminating fire detection with multiple sensors,” Fire Technol. 31, 120–136 (1995).
[Crossref]

Miziolek, A. W.

Nader, C. A.

Naehle, L.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Nassif, R.

Pellen, F.

Peterson, K. A.

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

Petra, M. I.

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

Silva, L. C. D.

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

Smith, D.

R. Chagger and D. Smith, “The causes of false fire alarms in buildings,” Report No. (BRE Global Ltd., 2014).

Stroup, D. W.

D. D. Evans and D. W. Stroup, “Methods to calculate the response time of heat and smoke detectors installed below large unobstructed ceilings,” Fire Technol. 22, 54–65 (1986).
[Crossref]

Tibshirani, R.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

Treps, N.

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Umar, M. M.

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

Wang, S.

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

Wright, D.

Yang, Z.

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

Zeller, W.

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Adv. Heat Transf. (1)

S. L. Lee and J. M. Hellman, “Heat and mass transfer in fire research,” Adv. Heat Transf. 10, 219–284 (1974).
[Crossref]

Appl. Opt. (6)

Fire Safety J. (2)

S.-J. Chen, D. C. Hovde, K. A. Peterson, and A. W. Marshall, “Fire detection using smoke and gas sensors,” Fire Safety J. 42, 507–515 (2007).
[Crossref]

G. T. Atkinson and D. D. Drysdale, “Convective heat transfer from fire gases,” Fire Safety J. 19, 217–245 (1992).
[Crossref]

Fire Technol. (3)

J. A. Milke and T. J. McAvoy, “Analysis of signature patterns for discriminating fire detection with multiple sensors,” Fire Technol. 31, 120–136 (1995).
[Crossref]

D. D. Evans and D. W. Stroup, “Methods to calculate the response time of heat and smoke detectors installed below large unobstructed ceilings,” Fire Technol. 22, 54–65 (1986).
[Crossref]

D. I. Lawson, “Laser beam fire detector,” Fire Technol. 4, 257–264 (1968).

Int. J. Infrared Millim. Waves (1)

J. Li, S. Wang, Z. Dou, and Z. Yang, “Discrimination of smoke particles using infrared photoelectrical detection,” Int. J. Infrared Millim. Waves 22, 141–151 (2001).
[Crossref]

Int. J. Signal Imaging Syst. (1)

M. M. Umar, L. C. D. Silva, M. S. A. Bakar, and M. I. Petra, “State of the art of smoke and fire detection using image processing,” Int. J. Signal Imaging Syst. 10, 22–30 (2017).
[Crossref]

J. Opt. Soc. Am. (2)

Math. Intelligencer (1)

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The elements of statistical learning: data mining, inference and prediction,” Math. Intelligencer 27, 83–85 (2005).

Phys. Rev. A (1)

V. Delaubert, N. Treps, M. Lassen, C. C. Harb, C. Fabre, P. K. Lam, and H.-A. Bachor, “TEM10 homodyne detection as an optimal small-displacement and tilt-measurement scheme,” Phys. Rev. A 74, 053823 (2006).
[Crossref]

Sensors (1)

W. Zeller, L. Naehle, P. Fuchs, F. Gerschuetz, L. Hildebrandt, and J. Koeth, “DFB lasers between 760  nm and 16  μm for sensing applications,” Sensors 10, 2492–2510 (2010).
[Crossref]

Other (5)

R. Knox, K. Ajay, and K. Boettger, “Particle detection,” U.S. patentUS8804119B2 (14August2011).

R. Chagger and D. Smith, “The causes of false fire alarms in buildings,” Report No. (BRE Global Ltd., 2014).

http://www.energnist.dk/ .

J. P. Hollman, Heat Transfer (McGraw-Hill, 1976).

S. R. Gunn, “Support vector machines for classification and regression,” (1998), Vol. 14, pp. 5–16.

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Figures (5)

Fig. 1.
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.
Fig. 2.
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.
Fig. 3.
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 σ2 and the R2 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.
Fig. 4.
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 R2 of the linear fit of the spectrum are given.
Fig. 5.
Fig. 5. Test results of the nonlinear SVM classifier when trained on only the two most significant principal components derived from a total of eight descriptors. The principal component analysis (PCA) is from a total of 146,306 samples with 221 artificially simulated fires. The two principal components plotted are the two components with the highest variation in the data, i.e., the components are ordered by significance. The samples shown in the plot are a subset of the total acquired data. The complete dataset has been split into a training and a test dataset.

Equations (2)

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nn0+(np)0p+(nT)0T,
(np)0=1.914×109Pa1,(nT)0=9.567×107°C1.

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