Distributed Sensor Fire Detection.
Distributed Sensor Fire Detection.
(324 K)
Cleary, T. G.; Notarianni, K. A.
NIST SP 965; February 2001.
International Conference on Automatic Fire Detection
"AUBE '01", 12th. Proceedings. National Institute of
Standards and Technology. March 25-28, 2001,
Gaithersburg, MD, Beall, K.; Grosshandler, W. L.; Luck,
H., Editor(s)(s), 139-150 pp, 2001.
Keywords:
fire detection; predictive models; fire detection
systems; sensors; fire models
Abstract:
This paper details a case study that utilized model
simulations to assess the relative performance benefits
of distributed sensing over single-station,
single-sensor smoke detection and co-located
multi-sensor detection. 500 individual CFAST computer
fire model simulations, performed for a separate project
at NIST, were used as the data set to verify the
hypothesis that distributed sensing can improve
detection time over single sensor or co-located
multi-sensor detection. The modeled space configuration
consisted of seven rooms representing a single-floor
apartment residence. The 500 simulations encompass a
range of fire sizes, locations, initial and boundary
conditions deemed important from fire death statistics
and sensitivity analysis of various parameters. Model
outputs included smoke, CO and temperature levels as a
function of time in the upper layer of each room, thus
smoke and CO concentration along with temperature were
chosen as the sensor outputs. It was assumed that the
detector instantaneously sees the computed upper-layer
value of smoke, CO, or temperature. Four sensor
configurations were examined along with four different
rules governing the alarm state. The base configuration
was a smoke detector located in the entrance. Another
configuration had the smoke, CO and temperature sensors
co-located in the entrance, while the other two
configurations had the CO and temperature sensors moved
to other separate rooms. The rules consisted of smoke
concentrations with threshold adjustments if CO or
temperature reached a certain value, and a temperature
threshold criterion. The results suggest that
distributed sensing can improve detection in many cases
over a single multi-sensor detector. While more work
needs to be done to test the distributed sensing
concept, a natural end product would be an adaptive
artificial neural network that is trained by fire model
outputs, adjusts automatically to system changes due to
sensor failure or location changes and incorporates
building environment conditions.
Building and Fire Research Laboratory
National Institute of Standards and Technology
Gaithersburg, MD 20899