SYSTEM AND METHOD PROVIDING EARLY PREDICTION AND FORECASTING OF FALSE ALARMS BY APPLYING STATISTICAL INFERENCE MODELS

• US 20200135009A1
• Filed: 12/20/2019
• Published: 04/30/2020
• Est. Priority Date: 09/01/2015
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Nalukurthy, RajeshBabu, Sethi, Aatish, Prasad Naraharisetti, Kanaka Nagendra, Reddy Singam, Kiran
• Granted Patent: US 11,626,008 B2
• Time in Patent Office: Days
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G08B 13/19682 Graphic User Interface [GUI] presenting system data to the user, e.g. information on a screen helping a user interacting with an alarm system
• 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 21/16 Combustible gas alarms
• CPC Class Codes: G08B 25/008 Alarm setting and unsetting, i.e. arming or disarming of the security system
• CPC Class Codes: G08B 25/009 Signalling of the alarm condition to a substation whose identity is signalled to a central station, e.g. relaying alarm signals in order to extend communication range
• CPC Class Codes: G08B 25/08 using communication transmission lines G08B13/19658, G08B21/0286, G08B25/016 take precedence
• CPC Class Codes: G08B 25/14 Central alarm receiver or annunciator arrangements
• CPC Class Codes: G08B 26/008 central annunciator means of the sensed conditions, e.g. displaying or registering
• CPC Class Codes: G08B 29/185 Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A diagnostics and prediction system including a cloud system that continuously collects operating parameters from each of a number of environmental sensors and provides access to this data by a plurality of processing applications including (1) a predictive modeling system including (a) a health prediction system, (b) a sensor false alarm prediction system, (c) a zone false alarm prediction system and (d) a reporting system, (2) a system that diagnoses and predicts environmental hazardous areas and clusters areas based upon concentrations of CO in the site or building; and (3) a battery prediction system that predicts a battery life for the sensor.

Claims

1. A device, comprising:

a memory; and

a processor coupled to the memory, wherein the processor is configured to execute executable instructions stored in the memory to;

collect operating parameters from a sensor over a time period;

determine a rate of change of the operating parameters;

extrapolate the rate of change of the operating parameters using non-linear regression over the time period in response to the rate of change of the operating parameters being intermittent; and

determine a probability of a false alarm of the sensor based upon the time period.

2. The device of claim 1, wherein the processor is further configured to execute executable instructions stored in the memory to:

extrapolate the rate of change of the operating parameters using linear regression over the time period in response to the rate of change of the operating parameters remaining constant.

3. The device of claim 1, wherein the processor is further configured to execute executable instructions stored in the memory to:

store the operating parameters in the memory.

4. The device of claim 1, wherein the sensor is at least one of:

an environmental sensor or an intrusion sensor.

5. The device of claim 4, wherein the environmental sensor is at least one of:

a smoke detector, a fire detector, or a carbon monoxide detector.

6. The device of claim 4, wherein the intrusion sensor is at least one of:

a switch, a passive infrared sensor, or a closed-circuit television camera.

7. A system, comprising:

a sensor; and

a monitoring device, comprising;

a memory; and

a processor coupled to the memory, wherein the processor is configured to execute executable instructions stored in the memory to;

collect operating parameters from the sensor over a time period;

determine a rate of change of the operating parameters;

extrapolate the rate of change of the operating parameters using linear regression over the time period in response to the rate of change of the operating parameters remaining constant; and

determine a probability of a false alarm of the sensor based upon the time period.

8. The system of claim 7, wherein the processor is configured to execute executable instructions stored in the memory to:

extrapolate the rate of change of the operating parameters using non-linear regression over the time period in response to the rate of change of the operating parameters being intermittent.

9. The system of claim 7, wherein the processor is configured to execute executable instructions stored in the memory to:

combine information from a manufacturer with the operating parameters.

10. The system of claim 7, wherein the processor is configured to execute executable instructions stored in the memory to:

generate a probability report including the determined probability of the false alarm of the sensor based upon the time period.

11. The system of claim 10, wherein the processor is configured to execute executable instructions stored in the memory to:

send the probability report to a user.

12. The system of claim 7, wherein the sensor comprises:

a sensor memory.

13. The system of claim 12, wherein the sensor is configured to:

store at least one of;

the operating parameters, an installation date, or last maintenance date of the sensor in the sensor memory.

14. The system of claim 13, wherein the sensor is configured to:

provide at least one of;

the operating parameters, the installation date, or the last maintenance date of the sensor to the monitoring device.

15. The system of claim 7, wherein the processor is configured to execute executable instructions stored in the memory to:

collect operating parameters from a different sensor.

16. The system of claim 15, wherein the processor is configured to execute executable instructions stored in the memory to:

determine a probability of a false alarm of the different sensor based upon the time period.

17. The system of claim 15, wherein the processor is configured to execute executable instructions stored in the memory to:

cluster the sensor and the different sensor into a false alarm zone.

18. A method, comprising:

collecting operating parameters from an environmental sensor over a time period;

determining a rate of change of the operating parameters;

extrapolating the rate of change of the operating parameters using linear regression over the time period in response to the rate of change of the operating parameters remaining constant;

extrapolating the rate of change of the operating parameters using non-linear regression over the time period in response to the rate of change of the operating parameters being intermittent; and

determining a probability of a false alarm of the environmental sensor based upon the time period.

19. The method of claim 18, wherein extrapolating the rate of change of the operating parameters using non-linear regression includes using a least squares method.

20. The system of claim 18, wherein extrapolating the rate of change of the operating parameters using linear regression includes using Bayesian regression.