Method and system for fire detection

• US 8,294,567 B1
• Filed: 04/12/2011
• Issued: 10/23/2012
• Est. Priority Date: 08/01/2008
• Status: Active Grant

• Current Assignee: WilliamsRDM, Inc.
• Original Assignee: Williams-Pyro Incorporated
• Inventors: Stell, Andrew W
• Primary Examiner(s): Mehmood, Jennifer
• Application Number:
  US13/084,796
• Time in Patent Office: 560 Days
• Field of Search: 340/539.22, 340/539.24, 340/539.26, 340/539.27, 340/517, 340/521, 340/522, 340/577, 340628-630, 340/584, 706/2, 706/4
• US Class Current: 340/522
• CPC Class Codes: G08B 17/10 Actuation by presence of smoke or gases , e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
• CPC Class Codes: G08B 17/117 by using a detection device for specific gases, e.g. combustion products, produced by the fire G08B17/103, G08B17/11 take precedence
• CPC Class Codes: G08B 25/10 using wireless transmission systems G08B25/009 takes precedence
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks

Abstract

A method and system are provided, which provides reliable fire detection. In one implementation, the automated system includes a combination of sensors configured to measure various factors associated with a hazard, such as a fire or gas leakage, and generate sensor readings. Factors measured can include smoke, carbon monoxide and heat. The system further includes a detection device that is configured to determine whether a hazard or fire exists by performing a fuzzy analysis of sensor readings. The fuzzy analysis includes categorizing respective sensor readings into fuzzy sets, and determining whether the hazard exists based on a combination of the categorizations. In addition the size and direction of a fire can be determined from multiple sensors.

Claims

1. A hazard detection system, the system comprising:

a plurality of sensors configured to measure attributes associated with a hazard and generate sensor readings; and

a detection device configured to determine whether the hazard exists or not based on a fuzzy analysis of the sensor readings, wherein;

the sensor readings include temperature readings; and

the fuzzy analysis comprises;

determining a temperature rise rate from the temperature readings;

classifying the temperature rise rate by assigning a classification value in the range 0 to 1;

categorizing the temperature rise rate and the temperature readings into respective fuzzy set categories; and

combining the respective fuzzy set categories into an output that indicates whether the hazardous condition exists or not.

2. The system of claim 1, wherein:

the attributes include one or more of temperature values, temperature rise rates, smoke concentrations and carbon monoxide (CO) concentrations; and

the plurality of sensors include one or more of temperature sensors, smoke sensors, CO sensors and infrared sensors.

3. The system of claim 2, wherein:

the smoke sensor is an ionization smoke sensor configured to measure percentage obscuration due to smoke.

4. The system of claim 2, wherein:

the carbon monoxide (CO) sensor is a tin oxide element based sensor configured to measure CO concentration.

5. The system of claim 1, wherein:

the detection device comprises a fuzzy inference module configured to;

assign fuzzy set categories to the sensor readings; and

combine the fuzzy set categories into an output indicating whether the hazard exists or not.

6. The system of claim 1, wherein:

the detection device comprises a classifier module configured to assign classification values to the sensor readings using a neural network, wherein the classification values lie in a range of 0 to 1.

7. The system of claim 6, wherein:

the detection device further comprises a fuzzy inference module configured to;

assign fuzzy set categories to the sensor readings and the classification values; and

combine the fuzzy set categories into an output indicating whether the hazard exists or not.

8. The system of claim 1, wherein:

the detection device is further configured to raise an alarm if the hazard exists.

9. The system of claim 1, wherein:

the hazard is either a fire or a gas leak.

10. A method for detecting a hazardous condition, the method comprising:

receiving sensor readings from a plurality of sensors for attributes related to the hazardous condition; and

determining whether the hazardous condition exists based on a fuzzy analysis of the sensor readings, wherein;

the sensor readings include temperature readings; and

the fuzzy analysis comprises;

determining a temperature rise rate from the temperature readings;

classifying the temperature rise rate by assigning a classification value in the range 0 to 1;

categorizing the temperature rise rate and the temperature readings into respective fuzzy set categories; and

combining the respective fuzzy set categories into an output that indicates whether the hazardous condition exists or not.

11. The method of claim 10, wherein:

the plurality of sensors includes one or more of temperature sensors, smoke sensors, carbon monoxide sensors and infrared sensors.

12. The method of claim 10, wherein:

the sensor readings include one or more of temperature readings, percentage smoke obscuration readings and carbon monoxide (CO) concentration values.

13. The method of claim 10, wherein the fuzzy analysis comprises:

categorizing the sensor readings into fuzzy set categories; and

combining the fuzzy set categories into an output that indicates whether the hazardous condition exists or not.

14. The method of claim 10, wherein:

the hazardous condition is either a fire or a gas leak.

15. A device for detecting a fire, the device comprising:

a fuzzy inference module configured to receive classification values and sensor readings corresponding to a plurality of sensors that measure one or more fire related attributes, to categorize the classification values and the sensor readings into fuzzy set categories, and, wherein;

the sensor readings include temperature readings; and

wherein, the fuzzy inference module is configured to;

determine a temperature rise rate from the temperature readings;

classify the temperature rise rate by assigning a classification value in the range 0 to 1;

categorize the temperature rise rate and the temperature readings into respective fuzzy set categories; and

combine the respective fuzzy set categories into an output that indicates whether the hazardous condition exists or not.

16. The device according to claim 15 further comprising:

a classifier module configured to assign the classification values using a neural network, wherein the classification values lie in the range of 0 to 1.

17. The device according to claim 15, wherein:

the plurality of sensors includes one or more of temperature sensors, smoke sensors, carbon monoxide sensors and infrared sensors.

18. The device according to claim 15, wherein:

the fuzzy inference module triggers an alarm or another remedy if the fire exists.

19. The device of claim 15, wherein the categorizing comprises:

assigning membership levels for one or more fuzzy set categories to the classification value;

determining a probability of the classification value belonging to the one or more fuzzy set categories; and

categorizing the classification value into a fuzzy set category for which the classification value has the highest probability.