Smoke detection

• US 9,437,092 B2
• Filed: 09/21/2015
• Issued: 09/06/2016
• Est. Priority Date: 01/23/2014
• Status: Active Grant

• Current Assignee: UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Warmack, Robert J. Bruce, Wolf, Dennis A., Frank, Steven Shane
• Primary Examiner(s): Girma, Fekadeselassie
• Assistant Examiner(s): Burgdorf, Stephen
• Application Number:
  US14/859,631
• Publication Number:
  US 20160012698A1
• Time in Patent Office: 351 Days
• Field of Search: 340/514, 340/577, 340/584, 340/628, 340/632, 700/299
• US Class Current: 1/1
• CPC Class Codes: G08B 17/00 Fire alarms; Alarms responsive to explosion
• 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 29/188 Data fusion; cooperative systems, e.g. voting among different detectors
• CPC Class Codes: G08B 29/20 Calibration, including self-calibrating arrangements
• CPC Class Codes: G08B 3/10 using electric transmission; using electromagnetic transmission
• CPC Class Codes: G08B 5/36 using visible light sources

Abstract

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Claims

1. A smoke detector, comprising:

a computer readable medium including linear discriminant analysis (LDA) training output data generated by;

inputting sensor data from a plurality of tests, the sensor data indicative of environmental conditions during the respective tests;

processing the sensor data to generate derived signal data for the respective tests;

assigning at least one group to the derived signal data for the respective tests, the at least one group selected from a plurality of groups, each group of the plurality of groups associated with a hazardous condition or a non-hazardous condition; and

performing LDA training using the derived signal data and the assigned at least one group for the respective tests as input to the LDA training, the output of the LDA training generating a plurality of transformation coefficients for transforming derived signal data into linear discriminant (LD) coordinates, a mean of group means, and a plurality of centroids in linear discriminant coordinates, wherein the plurality of centroids includes a different centroid for each group of the plurality of groups;

a plurality of sensors configured to observe present environmental conditions, the plurality of sensors comprising an aerosol sensor and a carbon monoxide sensor;

a processor operatively connected to the computer readable memory and the plurality of sensors, the processor configured to;

process data from the plurality of sensors to provide data in a plurality of data channels indicative of the present environmental conditions;

map the data from the plurality of data channels into linear discriminant space using the plurality of transformation coefficients stored in the computer readable medium;

classify the present environmental conditions as belonging to one group of the plurality of groups based on the linear discriminant mapping of the data from the plurality of data channels; and

signal an alarm condition if the present environmental conditions are classified as belonging to a group associated with a hazardous condition; and

an alarm operatively connected to the processor, the alarm generating an audible alert, a visual alert, or a combination thereof in response to the alarm signal.

2. The smoke detector of claim 1, wherein the classification of the present environmental conditions as belonging to one group of the plurality of groups is based on the linear discriminant mapping of the plurality of data channels being outside a threshold in linear discriminant coordinates.

3. The smoke detector of claim 1, wherein processing the sensor data to generate derived signal data for the respective tests comprises applying different scaling factors to sensor data associated with different respective sensors.

4. The smoke detector of claim 1, wherein processing the sensor data to generate derived signal data for the respective tests comprises determining a difference between the sensor data and a moving average of the sensor data.

5. The smoke detector of claim 1, wherein assigning at least one group to the derived signal data for the respective tests comprises excluding extreme or inconclusive sensor data from any group.

6. The smoke detector of claim 1, wherein the inputted sensor data from the plurality of tests comprises data from individual tests broken down into time intervals for the test and wherein assigning at least one group to the derived signal data for the respective tests comprises assigning derived signal data for the time intervals to the groups.

7. A method of training a classifier for a smoke detector tuned for placement in a kitchen, comprising:

inputting sensor data from a plurality of tests into a processor, the sensor data indicative of environmental conditions during the tests;

using the processor to process the sensor data from the tests to generate derived signal data corresponding to the test data for respective tests, wherein processing the sensor data comprises tuning sensor data for detecting fires in the kitchen;

assigning the derived signal data into categories comprising at least one fire group and at least one non-fire group;

performing linear discriminant analysis (LDA) training using the processor and the derived signal data and the assigned categories for the derived signal data as input to the LDA training, the output of the LDA training generating a centroid in linear discriminant coordinates for each of the categories, a plurality of coefficients for transforming derived signal data into linear discriminant (LD) coordinates, and a mean of group means; and

storing the plurality of coefficients, the plurality of centroids, and the mean of group means in a computer readable medium.

8. The method of claim 7, wherein the categories comprise plural fire groups, the fire groups including a flaming fire group and a non-flaming fire group.

9. The method of claim 8, wherein the at least one non-fire group comprises a normal group and a nuisance non-fire indicating group.

10. The method of claim 7, wherein tuning sensor data for detecting fires in the kitchen comprises removing any sensor data from grease-fire tests.

11. The method of claim 7, wherein using the processor to process the sensor data from the tests to generate the derived signal data comprises applying different scaling factors to sensor data associated with different respective sensors.

12. The method of claim 7, wherein using the processor to process the sensor data from the tests to generate the derived signal data comprises determining a difference between the sensor data and a moving average of the sensor data.

13. The method of claim 7, wherein the act of assigning the derived signal data into categories comprises excluding extreme and inconclusive sensor data from any category.

14. A non-transitory computer-readable medium storing computer-executable instructions thereon, the instructions for causing a processor to perform acts for training a classifier for a smoke detector, the acts comprising:

inputting sensor data from a plurality of tests into the processor, the sensor data indicative of environmental conditions during the tests;

using the processor to process the sensor data from the tests to generate derived signal data corresponding to the test data for respective tests;

assigning the derived signal data into categories comprising at least one fire group and at least one non-fire group;

performing linear discriminant analysis (LDA) training using the processor and the derived signal data and the assigned categories for the derived signal data as input to the LDA training, the output of the LDA training generating a centroid in linear discriminant coordinates for each of the categories, a plurality of coefficients for transforming derived signal data into linear discriminant (LD) coordinates, and a mean of group means; and

storing the plurality of coefficients, the plurality of centroids, and the mean of group means.

15. The non-transitory computer-readable medium of claim 14, wherein the act of assigning the derived signal data into categories comprises excluding extreme and inconclusive sensor data from any category.

16. The non-transitory computer-readable medium of claim 14, wherein the inputted sensor data from the plurality of tests comprises data from individual tests broken down into time intervals for the test and the act of assigning comprises assigning derived signal data for the time intervals to the categories.

17. The non-transitory computer-readable medium of claim 14, wherein the sensor data includes data from an aerosol sensor and one or more sensors selected from the group consisting of a temperature sensor, a carbon monoxide sensor, a Taguchi sensor, and a carbon monoxide sensor.

18. The non-transitory computer-readable medium of claim 14, wherein the sensor data from each test is a time-series of sensor data over time periods and wherein the act of processing the sensor data comprises:

generating a first baseline based on a moving average over n previous measurements of the sensor data; and

calculating a difference between a present measurement of the sensor data and the first baseline.

19. The non-transitory computer-readable medium of claim 14, wherein the act of assigning the derived signal data into categories comprises assigning the derived signal data for the respective time periods into the categories.

20. The non-transitory computer-readable medium of claim 14, wherein the act of storing comprises storing the plurality of coefficients, the plurality of centroids, and the mean of group means in a computer readable medium of the smoke detector.