System and method for securing an infrastructure

• US 20070096896A1
• Filed: 10/28/2005
• Published: 05/03/2007
• Est. Priority Date: 10/28/2005
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Goldfarb, Helena, Bufi, Corey, Thetford, Jeffrey, Zingelewicz, Virginia, Azzaro, Steven
• Application Number:
  US11/260,897
• Publication Number:
  US 20070096896A1
• Time in Patent Office: Days
• US Class Current: 340/522
• CPC Class Codes: G08B 21/12 responsive to undesired emission of substances, e.g. pollution alarms
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A system for detecting potentially adverse conditions includes a plurality of different types of sensors each adapted to monitor a different measured parameter of an infrastructure. The system also includes a model that fuses data from the plurality of sensors and provides an indication of a potentially adverse condition. A method is also provided for detecting potentially adverse conditions.

Claims

1. A monitoring system for a infrastructure, comprising:

a plurality of different types of sensors disposed around a protected zone of the infrastructure, wherein each of the plurality of sensors is configured to detect at least one threat behavior corresponding to an outcome that causes damage to the infrastructure and send a signal representing the threat behavior; and

at least one hybrid fusion model adapted to receive the signals sent by the plurality of sensors, assess the signals to determine a likelihood of an outcome that causes damage and provide a signal indicative of the likelihood of the outcome if the likelihood of the outcome exceeds a threshold.

2. The system of claim 1, wherein the at least one hybrid fusion model comprise at least one of a Markov model, a Bayesian Belief Network, and a spatial model.

3. The system of claim 2, wherein the Markov model is adapted to determine the likelihood of the outcome prior to arrival of a potential threat in the protected zone, at least one of the Bayesian Belief Network and the Spatial model is adapted to determine the likelihood of the outcome after the arrival of the potential threat in the protected zone and the Markov model, the Bayesian Belief Network, and the spatial model together determine whether the likelihood of the outcome exceeds the threshold.

4. The system of claim 1, wherein the plurality of sensors comprises at least one of an accelerometer, a magnetometer, a microphone, a gas sensor, and a range-controlled radar.

5. The system of claim 1, wherein at least one of the plurality of sensors has a range larger than the protected zone.

6. The system of claim 1, wherein the plurality of sensors comprises a network for wirelessly communicating with the at least one hybrid fusion models.

7. The system of claim 1, wherein the at least one threat behavior comprises an intrusion of at least one of a backhoe, a truck, a car, a living being and a natural hazard.

8. The system of claim 1, wherein each of the plurality of sensors is configured to communicate with each other either in a centralized manner or in a decentralized manner.

9. The system of claim 8, further comprising:

an in-field supervisory control center configured to coordinate the communication in centralized or decentralized manner, the supervisory control center comprising at least one hybrid fusion model.

10. The system of claim 9 further comprising an alerting system in communication with the in-field supervisory control center and adapted to provide an alert signal indicative of the likelihood of the outcome if the likelihood of the outcome exceeds a threshold.

11. A system for detecting potentially adverse conditions, comprising:

a plurality of different types of sensors, each adapted to monitor a different measured parameter of an infrastructure; and

at least one model that fuses data from the plurality of sensors and provides an indication of a potentially adverse condition.

12. The system of claim 11, wherein the plurality of sensors comprises at least one of an accelerometer, a gas sensor, a magnetometer, a microphone and a range controlled radar.

13. The system of claim 11, wherein the at least one model comprises at least one of a Markov model, a Bayesian Belief Network, and a spatial model.

14. The system of claim 11, wherein the indication of a potentially adverse condition comprises a threat level.

15. The system of claim 11, wherein the measured parameter corresponds to at least one of information received prior to arrival of a potential threat, after arrival of the potential threat and physical proximity of the potential threat to the infrastructure.

16. A monitoring method for an infrastructure, comprising:

deploying a plurality of different types of sensors around a protected zone of the infrastructure, wherein each of the plurality of sensors is configured to detect at least one threat behavior corresponding to an outcome that causes damage to the infrastructure;

sensing at least one threat behavior corresponding to the outcome that causes the damage to the infrastructure and sending a signal representing the threat behavior;

deploying at least one hybrid fusion model adapted to receive the signals sent by the plurality of sensors, assessing the signals to determine a likelihood of the outcome; and

providing a signal indicative of the likelihood of the outcome if the likelihood of the outcome exceeds a threshold.

17. The method of claim 16, wherein the at least one hybrid fusion model comprises at least one of a Markov model, a Bayesian Belief Network, and a spatial model.

18. The method of claim 17, wherein the Markov model is adapted to determine the likelihood of the outcome prior to arrival of a potential threat in the protected zone, at least one of the Bayesian Belief Network and the Spatial model is adapted to determine the likelihood of the outcome after the arrival of the potential threat in the protected zone and the Markov model, the Bayesian Belief Network, and the spatial model together determine whether the likelihood of the outcome exceeds the threshold.

19. The method of claim 16, wherein the plurality of sensors comprises at least one of an accelerometer, a magnetometer, a microphone, a gas sensor, and a range-controlled radar.

20. The method of claim 16, wherein at least one of the plurality of sensors has a range larger than the protected zone.

21. The method of claim 16 further comprising establishing a network for wirelessly communicating with the at least one hybrid fusion models.

22. The method of claim 16, wherein the at least one threat behavior comprises an intrusion of at least one of a backhoe, a truck, a car, a living being and a natural hazard.

23. The method of claim 16, wherein each of the plurality of sensors is configured to communicate with each other either in a centralized manner or in a decentralized manner.

24. The method of claim 23, further comprising:

disposing an in-field supervisory control center to coordinate the communication in centralized or decentralized manner, the supervisory control center comprising at least one hybrid fusion model.

25. A method of detecting potentially adverse conditions, comprising:

identifying a potentially adverse condition relative to an infrastructure;

selecting a plurality of sensors, each of the plurality of sensors being adapted to monitor a different measured parameter indicative of the potentially adverse condition; and

designing at least one model that fuses data from the plurality of sensors to produce an indication corresponding to a probability that the potentially adverse condition will appear.

26. The method of claim 25, wherein the plurality of sensors comprises at least one of an accelerometer, a gas sensor, a magnetometer, a microphone and a range controlled radar.

27. The method of claim 25, wherein the indication comprises a threat level.

28. The method of claim 25, wherein the measured parameter corresponds to at least one of information received prior to arrival of a potential threat, after arrival of the potential threat and physical proximity of the potential threat to the infrastructure.

29. The method of claim 25, wherein the plurality of sensors provide wireless data.

30. A means for detecting potentially adverse conditions, comprising:

means for identifying a potentially adverse condition relative to an infrastructure;

means for sensing and monitoring different measured parameters indicative of the potentially adverse condition; and

means for fusing data from the sensing and monitoring means to produce an indication corresponding to a probability that the potentially adverse condition will appear.