- Author
-
Rose-Pehrsson, S. L.
|
Hart, S. J.
|
Hammond, M. H.
|
Gottuk, D. T.
|
Wright, M. T.
|
Wong, J. T.
|
Street, T. T.
|
Tatem, P. A.
|
Williams, F. W.
- Title
- Real-Time Probabilistic Neural Network Performance and Optimization for Fire Detection and Nuisance Alarm Rejection: Test Series 2 Results. Memorandum. February 1, 2000-May 31, 2000.
- Coporate
- NOVA Research Inc., Alexandria, VA
Hughes Associates, Inc., Baltimore, MD
- Sponsor
- Office of Naval Research, Arlington, VA
- Report
-
NRL/MR/6110-00-8499,
October 10, 2000,
27 p.
- Keywords
-
fire detection
|
false alarms
|
sensors
|
smoke detectors
|
data analysis
|
warning systems
|
ships
|
neural networks
|
pattern recognition
- Identifiers
- multi-sensor; sensor arrays; multivariate analysis; Probabilistic Neural Network (PNN); sensor data fusion; Early Warning Fire Detection (EWFD) system
- Abstract
- The U.S. Navy program Damage Control-Automation for Reduced Manning (DC-RM), sponsored by the Office of Naval Research, PE0603508N, is focused on enhancing automation of ship fire and damage control systems. A key element to this objective is the improvement in situational awareness by improving the current fire detection systems. As in many applications, it is desirable to increase detection sensitivity, decrease the detection time and increase the reliability of the detection system through improved nuisance alarm immunity. Improved reliability is needed such that fire detection systems can provide quick, remote and automatic fire suppression capability. The use of multi-criteria based detection technology offers the most promising means to achieve both improved sensitivity to real fires and reduced susceptibility to nuisance alarm sources. A multi-year effort to develop an early warning fire detection system is currently underway. The system being developed uses the output from sensors that measure different parameters of a developing fire or from analyzing multiple aspects of a given sensor output (e.g., rate of change as well as absolute value) and a neural network for fire recognition. A series of tests were conducted on the ex-USS SHADWELL from April 25 to May 5, 2000 to evaluate candidate prototypes of the early warning fire detection system (EWFD). Improved fire recognition and low false alarm rates were observed using data from full-scale laboratory tests. Several different sensor combinations were identified for use with a probabilistic neural network (PNN). Full-scale shipboard tests were conducted on the ex-USS SHADWELL to further develop detection algorithms and to expand the fire/nuisance source database. Using these two data sets, two candidate suites of sensors were identified for prototype development. Test Series 1 tested the real-time responses of the prototypes. Two months later, under different environmental conditions, the optimized prototypes were tested with more fire and nuisance sources. The results of Test Series 2 shipboard testing, and the subsequent optimization of the prototypes are described in this report.