Methods and systems for monitoring environments

• US 20050251339A1
• Filed: 05/05/2004
• Published: 11/10/2005
• Est. Priority Date: 05/05/2004
• Status: Abandoned Application

• Current Assignee: St-Infonox
• Original Assignee: St- Infonox
• Inventors: Banerjee, Ashim, Shah, Safwan, Araki, M. Sam, Bajwa, Mobeen, Coe-Verbica, Peter
• Application Number:
  US10/839,980
• Publication Number:
  US 20050251339A1
• Time in Patent Office: Days
• US Class Current: 702/2
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms

Abstract

Methods and systems are provided for monitoring a state of an environment. Sensors are distributed spatially within the environment, with each sensor measuring one of the measured parameters at its spatial location. A controller receives data collected from each of the sensors. The controller identifies the occurrence of an event at at least one of the sensors. The controller extracts derived parameters from the collected data. The controller determines a cross-correlation of the extracted parameters over the sensors. The controller identifies an abnormality in the environment from the determined cross-correlation.

Claims

1. A system for monitoring a state of an environment, the state being defined by a plurality of measured parameters, the system comprising:

a plurality of sensors distributed spatially within the environment, each such sensor being adapted to measure one of the measured parameters at its spatial location within the environment; and

a controller in communication with the sensors and having programming instructions to;

receive data collected from each of the sensors;

identify the occurrence of an event at at least one of the sensors by identifying a change in an event-defining parameter;

extract a plurality of derived parameters from the collected data;

determine a cross-correlation of the extracted plurality of derived parameters over the plurality of sensors; and

identify an abnormality in the environment from the determined cross-correlation.

2. The system recited in claim 1 wherein the event-defining parameter is the parameter measured at the at least one of the sensors.

3. The system recited in claim 1 wherein the event-defining parameter is derived from the parameter measured at the at least one of the sensors.

4. The system recited in claim 1 wherein:

the plurality of derived parameters each have a time dependence; and

the controller further has programming instructions to apply fuzzy logic to the time dependence of each of the derived parameters prior to determining the cross-correlation of the extracted plurality of derived parameters.

5. The system recited in claim 1 wherein:

the plurality of measured parameters are time-period correlatable; and

the programming instructions to extract the plurality of derived parameters comprise programming instructions to calculate an autocorrelation of each of the plurality of measured parameters.

6. The system recited in claim 1 wherein the plurality of derived parameters comprise a mean and standard deviation over time of the plurality of measured parameters.

7. The system recited in claim 1 wherein the environment comprises a hierarchical branching network with the plurality of sensors distributed throughout the hierarchical branching network.

8. The system recited in claim 7 wherein the environment comprises a fluid-distribution system and the hierarchical branching network comprises a network of branching channels through which fluid flows.

9. The system recited in claim 8 wherein the plurality of measured parameters comprise a quantity selected from the group consisting of a turbidity, a pH level, a conductivity, and a concentration of solids dissolved in the fluid.

10. The system recited in claim 7 wherein the environment comprises a power-distribution system and the hierarchical branching network comprises a network of branching power-distribution lines.

11. The system recited in claim 1 wherein the controller further has programming instructions to determine a severity of the abnormality from the determined cross-correlation.

12. The system recited in claim 11 wherein the controller further has programming instructions to initiate an alarm in accordance with the determined severity of the abnormality.

13. The system recited in claim 1 wherein:

the environment is one of a plurality of environments, each such environment having a state monitored by the system; and

the controller further has programming instructions to correlate abnormalities identified in each of the environments to provide a collective characterization of the plurality of environments.

14. A method for monitoring a state of an environment, the state being defined by a plurality of measured parameters, the method comprising:

receiving data collected from each of a plurality of sensors distributed spatially within the environment, the data providing a measurement of one of the measured parameters at a spatial location of a respective one of the sensors within the environment;

identifying the occurrence of an event at at least one of the sensors by identifying a change in an event-defining parameter;

extracting a plurality of derived parameters from the collected data;

determining a cross-correlation of the extracted plurality of derived parameters over the plurality of sensors; and

identifying an abnormality in the environment from the determined cross-correlation.

15. The method recited in claim 14 wherein the event-defining parameter is the parameter measured at the at least one of the sensors.

16. The method recited in claim 14 wherein the event-defining parameter is derived from the parameter measured at the at least one of the sensors.

17. The method recited in claim 14 wherein the plurality of derived parameters each have a time dependence, the method further comprising applying fuzzy logic to the time dependence of each of the derived parameters prior to determining the cross-correlation of the extracted plurality of derived parameters.

18. The method recited in claim 14 wherein:

the plurality of measured parameters are time-period correlatable; and

extracting the plurality of derived parameters comprises calculating an autocorrelation of each of the plurality of measured parameters.

19. The method recited in claim 14 wherein the plurality of derived parameters comprise a mean and standard deviation over time of the plurality of measured parameters.

20. The method recited in claim 14 wherein the environment comprises a hierarchical branching network with the plurality of sensors distributed throughout the hierarchical branching network.

21. The method recited in claim 20 wherein environment comprises a fluid-distribution system and the hierarchical branching network comprises a network of branching channels through which fluid flows.

22. The method recited in claim 21 wherein the plurality of measured parameters comprise a quantity selected from the group consisting of a turbidity, a pH level, a conductivity, and a concentration of solids dissolved in the fluid.

23. The method recited in claim 20 wherein the environment comprises a power-distribution system and the hierarchical branching network comprises a network a branching power-distribution lines.

24. The method recited in claim 14 further comprising determining a severity of the abnormality from the determined cross-correlation.

25. The method recited in claim 24 further comprising initiating an alarm in accordance with the determined severity of the abnormality.

26. The method recited in claim 14 wherein the environment is one of a plurality of environments, each such environment having a state, the method further comprising correlating abnormalities identified in each of the environments.

27. A system for monitoring a state of a fluid-distribution network having a network of branching channels through which fluid flows, the state being defined by a plurality of measured parameters, the system comprising:

a plurality of sensors distributed spatially throughout the network of branching channels, each such sensor being adapted to measure one of the measured parameters at its spatial location within the network of branching channels; and

a controller in communication with the sensors and having programming instructions to;

receive data collected from each of the sensors;

identify the occurrence of an event at at least one of the sensors by identifying a change in an event-defining parameter;

extract a plurality of derived parameters from the collected data, the plurality of derived parameters each having a time dependence;

apply fuzzy logic to the time dependence of each of the derived parameters;

determine a cross-correlation of the extracted plurality of derived parameters over the plurality of sensors after the fuzzy logic has been applied to the time dependence;

identify an abnormality in the fluid-distribution network from the determined cross-correlation; and

determining a severity of the abnormality from the determined cross-22 correlation.

28. The system recited in claim 27 wherein:

the plurality of measured parameters are time-period correlatable; and

the programming instructions to extract the plurality of derived parameters comprise programming instructions to calculate an autocorrelation of each of the plurality of measured parameters.

29. The system recited in claim 27 wherein the event-defining parameter is the parameter measured at the at least one of the sensors.

30. The system recited in claim 27 wherein the event-defining parameter is derived from the parameter measured at the at least one of the sensors.

31. The system recited in claim 27 wherein the plurality of measured parameters comprise a quantity selected from the group consisting of a turbidity, a pH level, a conductivity, and a concentration of solids dissolved in the fluid.

32. A method for monitoring a state of a fluid-distribution network having a network of branching channels through which fluid flows, the state being defined by a plurality of measured parameters, the method comprising:

receiving data collected from each of a plurality of sensors distributed spatially throughout the network of branching channels, the data providing a measurement of one of the measured parameters at its spatial location of a respective one of the sensors within the network of branching channels;

identifying the occurrence of an event at at least one of the sensors by identifying a change in an event-defining parameter;

extracting a plurality of derived parameters from the collected data, the plurality of derived parameters each having a time dependence;

applying fuzzy logic to the time dependence of each of the derived parameters;

determining a cross-correlation of the extracted plurality of derived parameters over the plurality of sensors after the fuzzy logic has been applied to the time dependence;

identifying an abnormality in the fluid-distribution network from the determined cross-correlation; and

determining a severity of the abnormality from the determined cross-correlation.

33. The method recited in claim 32 wherein:

the plurality of measured parameters are time-period correlatable; and

extracting the plurality of derived parameters comprises calculating an autocorrelation of each of the plurality of measured parameters.

34. The method recited in claim 32 wherein the event-defining parameter is the parameter measured at the at least one of the sensors.

35. The method recited in claim 32 wherein the event-defining parameter is derived from the parameter measured at the at least one of the sensors.

36. The method recited in claim 32 wherein the plurality of measured parameters comprise a quantity selected from the group consisting of a turbidity, a pH level, a conductivity, and a concentration of solids dissolved in the fluid.