Identification of fire signatures for shipboard multi-criteria fire detection systems

• US 7,034,701 B1
• Filed: 06/16/2000
• Issued: 04/25/2006
• Est. Priority Date: 06/16/2000
• Status: Expired due to Fees

• Current Assignee: the united states of america as represented by the secretary of the navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Hammond, Mark H., Rose-Pehrsson, Susan, Gottuk, Daniel T., Hart, Sean J., Shaffer, Ronald E.
• Primary Examiner(s): Wu, Daniel J.
• Assistant Examiner(s): TANG, SON M
• Application Number:
  US09/885,255
• Time in Patent Office: 2,139 Days
• Field of Search: 340/628, 340/629, 340/630, 340/577, 340/578, 340/521, 340/522, 340/523, 340/511, 340/506, 340/588, 340/589
• US Class Current: 340/628
• CPC Class Codes: G08B 17/00 Fire alarms; Alarms responsive to explosion
• CPC Class Codes: G08B 29/183 Single detectors using dual technologies G08B13/1672, G08B13/2448, G08B13/2494 take precedence
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A multi-criteria fire detection system, comprising a plurality of sensors, wherein each sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating the same. A processor for receiving each output of the plurality of sensors is also employed. The processor includes a probabilistic neural network for processing the sensor outputs. The probabilistic neural network comprises a nonlinear, nor-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter. The plurality of data sets includes a baseline, non-fire, fist data set; a second, fire data set, and a third, nuisance data set. The algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discrimination between said data sets, and whether the outputs suffice to substantially indicate the presence of a fire, as opposed to a non-fire or nuisance situation.

Claims

1. A multi-criteria fire detection system, comprising:

a plurality of sensors, wherein each said sensor is capable of detecting a signature characteristic of a presence of a fire and providing an output indicating same;

a processor for receiving each of said outputs of said plurality of sensors, said processor including a probabilistic neural network for processing said outputs, and wherein said probabilistic neural network comprises a nonlinear, non-parametric pattern recognition algorithm that operates by defining a probability density function for a plurality of data sets that are each based on a training set data and an optimized kernel width parameter, and wherein said plurality of data sets includes;

a baseline, non-fire, first data set;

a second, fire data set; and

a third, nuisance data set;

whereby said algorithm provides a decisional output indicative of the presence of a fire based on recognizing and discriminating between said data sets and whether said outputs suffice to substantially indicate the presence of a fire as opposed to a non-fire or nuisance situation.

2. A system as in claim 1, wherein said algorithm comprises just one such optimized kernel width parameter that along with one of said training set data defines said probability density function for each said data set.

3. A system as in claim 2, wherein said algorithm further comprises a cross-validation protocol.

4. A method for detecting the presence of a fire, comprising the steps of:

establishing a plurality of data sets, said data sets including;

a baseline, non-fire, first data set;

a second, fire data set; and

a third, nuisance data set;

training each of said data sets to respond to an input and provide a representative output;

sensing a plurality of signatures of a fire;

encoding each of said plurality of signatures in a numerical output representative of a point or location in a multidimensional space;

inputting each said numerical output to a probabilistic neural network that operates by defining a probability density function for each said data set based on said training set data and an optimized kernel width parameter; and

correlating said numerical outputs to a location in said multidimensional space to determine the presence or absence of a fire at said location.