SMOKE DETECTION

• US 20160364971A1
• Filed: 08/04/2016
• Published: 12/15/2016
• 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
• Granted Patent: US 9,792,795 B2
• Time in Patent Office: Days
• 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;

calculating a baseline moving average for sensor data of the respective tests;

generating derived signal data using the sensor data and the baseline moving average for the sensor data of 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;

at least one sensor configured to observe present environmental conditions, the at least one sensor comprising an aerosol sensor;

a processor operatively connected to the computer readable memory and the at least one sensor, the processor configured to;

process data from the at least one sensor 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 generating derived signal data using the sensor data and the baseline moving average for the sensor data of the respective tests comprises determining a difference between the sensor data and the moving average of the sensor data.

3. 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.

4. The smoke detector of claim 1, wherein processing data from the at least one sensor to provide data in a plurality of data channels indicative of the present environmental conditions comprises determining a difference between the sensor data and a moving average of the sensor data.

5. The smoke detector of claim 1, wherein the processor is further configured to be set into a sleep mode between reading cycles of the at least one sensor.

6. The smoke detector of claim 5, wherein an amplifier of the at least one sensor is switched off during the sleep mode.

7. The smoke detector of claim 5, wherein the at least one sensor comprises a carbon monoxide sensor and an amplifier of the carbon monoxide sensor is switched off during the sleep mode.

8. The smoke detector of claim 5, wherein a time period of the sleep mode is between three and ten seconds.

9. A method of training a classifier for a smoke detector, comprising:

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

calculating a baseline moving average for sensor data of the respective tests;

generating derived signal data using the sensor data and the baseline moving average for the sensor data of the 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 in a computer readable medium.

10. The method of claim 9, wherein generating derived signal data using the sensor data and the baseline moving average for the sensor data of the respective tests comprises determining a difference between the sensor data and the moving average of the sensor data.

11. The method of claim 10, wherein generating derived signal data using the sensor data and the baseline moving average for the sensor data of the respective tests comprises multiplying the difference by a scaling factor.

12. The method of claim 9, wherein the categories comprise plural fire groups, the fire groups including a flaming fire group and a non-flaming fire group.

13. The method of claim 12, wherein the at least one non-fire group comprises a normal group and a nuisance non-fire indicating group.

14. The method of claim 9, further comprising:

tuning sensor data for detecting fires in a kitchen by removing any sensor data from grease-fire tests.

15. 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;

at least one sensor configured to observe present environmental conditions, the at least one sensor comprising an aerosol sensor;

a processor operatively connected to the computer readable memory and the at least one sensor, the processor configured to;

process data from the at least one sensor 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;

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

enter a sleep mode between reading cycles of the at least one sensor; 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.

16. The smoke detector of claim 15, wherein the at least one sensor comprises an ionization aerosol sensor and a carbon monoxide sensor.

17. The smoke detector of claim 16, wherein the carbon monoxide sensor comprises an amplifier, and the processor is further configured to switch off the amplifier during the sleep mode.

18. The smoke detector of claim 16, wherein the carbon monoxide sensor comprises an amplifier, and the processor is further configured to switch off the amplifier during the sleep mode and not switch off the ionization aerosol sensor during the sleep mode.

19. The smoke detector of claim 15, wherein a time period of the sleep mode is between three and ten seconds.

20. The smoke detector of claim 15, 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.