CA2596446A1 - Infrastructure health monitoring and analysis - Google Patents
Infrastructure health monitoring and analysis Download PDFInfo
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- CA2596446A1 CA2596446A1 CA002596446A CA2596446A CA2596446A1 CA 2596446 A1 CA2596446 A1 CA 2596446A1 CA 002596446 A CA002596446 A CA 002596446A CA 2596446 A CA2596446 A CA 2596446A CA 2596446 A1 CA2596446 A1 CA 2596446A1
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- 238000012544 monitoring process Methods 0.000 title 1
- 238000000034 method Methods 0.000 claims abstract 29
- 238000001514 detection method Methods 0.000 claims abstract 16
- 238000013528 artificial neural network Methods 0.000 claims 12
- 230000008407 joint function Effects 0.000 claims 3
- 238000005314 correlation function Methods 0.000 claims 2
Classifications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
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- Testing And Monitoring For Control Systems (AREA)
- Feedback Control In General (AREA)
Abstract
There is described herein a method for detecting anomalies in an infrastructure, the method comprising: providing a computationally-intelligent analysis model to model a behaviour of at least one detection instrument in said infrastructure; inputting control instrument data into said analysis model, said control instrument data being provided by control instruments in said infrastructure; outputting an estimated behaviour for said at least one detection instrument from said analysis model; comparing actual data from said at least one detection instrument to said estimated behaviour and generating a set of residuals representing a difference between said actual data and said estimated behaviour; and identifying anomalies when said residuals exceed a predetermined threshold.
Claims (35)
1. A method for detecting anomalies in an infrastructure, the method comprising:
providing a computationally-intelligent analysis model to model a behaviour of at least one detection instrument in said infrastructure;
inputting control instrument data into said analysis model, said control instrument data being provided by control instruments in said infrastructure;
outputting an estimated behaviour for said at least one detection instrument from said analysis model;
comparing actual data from said at least one detection instrument to said estimated behaviour and generating a set of residuals representing a difference between said actual data and said estimated behaviour; and identifying anomalies when said residuals exceed a predetermined threshold.
providing a computationally-intelligent analysis model to model a behaviour of at least one detection instrument in said infrastructure;
inputting control instrument data into said analysis model, said control instrument data being provided by control instruments in said infrastructure;
outputting an estimated behaviour for said at least one detection instrument from said analysis model;
comparing actual data from said at least one detection instrument to said estimated behaviour and generating a set of residuals representing a difference between said actual data and said estimated behaviour; and identifying anomalies when said residuals exceed a predetermined threshold.
2. A method as claimed in claim 1, wherein said infrastructure is a dam.
3. A method as claimed in claim 1, wherein said inputting control instrument data comprises also inputting detection instrument data into said analysis model.
4. A method as claimed in claim 1, wherein said inputting control instrument data comprises inputting a temperature explicitly into the analysis model.
5. A method as claimed in claim 1, wherein said providing a computationally intelligent analysis model comprises providing a neural-network.
6. A method as claimed in claim 5, wherein said providing a neural-network comprises providing a data-driven parameterized nonlinear model.
7. A method as claimed in claim 6, wherein said providing a neural-network comprises providing a coupled computationally intelligent model, an output of said model being a joint function of all input variables.
8. A method as claimed in claim 6, wherein said providing a neural-network comprises providing a decoupled computationally intelligent model, a contribution of each input to an output being calculated separately and added together.
9. A method as claimed in claim 1, wherein said providing a computationally intelligent analysis model comprises providing a fuzzy network.
10. A method as claimed in claim 1, wherein said providing a computationally intelligent analysis model comprises providing a neuro-fuzzy network.
11. A method as claimed in claim 1, wherein said providing a computationally intelligent analysis model comprises providing a Bayesian network.
12. A method as claimed in claim 1, wherein said inputting control instrument data into said analysis model comprises using lag-time information to delay corresponding input data.
13. A method for modeling a behaviour of at least one detection instrument in an infrastructure, the method comprising:
using a computationally intelligent analysis model to represent said behaviour of at least one detection instrument;
providing a model learning phase using historical data from at least one of detection instruments and control instruments within said infrastructure to teach the analysis model;
saving optimized parameters into said analysis model;
providing a model execution/testing phase to predict and evaluate said behaviour in real-time as data is input therein; and outputting a predicted value for said at least one detection instrument.
using a computationally intelligent analysis model to represent said behaviour of at least one detection instrument;
providing a model learning phase using historical data from at least one of detection instruments and control instruments within said infrastructure to teach the analysis model;
saving optimized parameters into said analysis model;
providing a model execution/testing phase to predict and evaluate said behaviour in real-time as data is input therein; and outputting a predicted value for said at least one detection instrument.
14. A method as claimed in claim 13, wherein said providing a computationally intelligent analysis model comprises providing a neural-network.
15. A method as claimed in claim 14, wherein said providing a neural-network comprises providing a data-driven parameterized nonlinear model.
16. A method as claimed in claim 14, wherein said providing a neural-network comprises providing a coupled computationally intelligent model, an output of said model being a joint function of all input variables.
17. A method as claimed in claim 14, wherein said providing a neural-network comprises providing a decoupled computationally intelligent model, a contribution of each input to an output being calculated separately and added together.
18. A method as claimed in claim 13, wherein said providing a computationally intelligent analysis model comprises providing a fuzzy network.
19. A method as claimed in claim 13, wherein said providing a computationally intelligent analysis model comprises providing a neuro-fuzzy network.
20. A method as claimed in claim 13, wherein said providing a computationally -intelligent analysis model comprises providing a Bayesian network.
21. A method for determining a lag time between a cause and an effect in an infrastructure, the method comprising:
identifying a first variable as said cause and a second variable as said effect;
specifying a desired time period;
assigning a maximum possible lag time between said cause and effect;
calculating a cross-correlation function between said first variable and said second variable over said desired time period; and shifting forward in time said second variable until said maximum lag time is reached while recalculating said cross-correlation function between each shift in time, wherein a total shift needed to reach a maximum absolute cross-correlation corresponds to said lag time.
identifying a first variable as said cause and a second variable as said effect;
specifying a desired time period;
assigning a maximum possible lag time between said cause and effect;
calculating a cross-correlation function between said first variable and said second variable over said desired time period; and shifting forward in time said second variable until said maximum lag time is reached while recalculating said cross-correlation function between each shift in time, wherein a total shift needed to reach a maximum absolute cross-correlation corresponds to said lag time.
22. A method as claimed in claim 21, wherein said infrastructure is a dam.
23. A method as claimed in claim 21, wherein said cause and said effect correspond to sensors in said infrastructure.
24. A method as claimed in claim 21, wherein said maximum absolute cross-correlation corresponds to a measure of dependency between said cause and said effect.
25. A system for detecting anomalies in an infrastructure, the system comprising:
an analysis module comprising a computationally-intelligent model of a behaviour of at least one detection instrument in said infrastructure, said model having control instrument data from said infrastructure as inputs and an estimated behaviour for said at least one detection instrument as an output;
a comparison module adapted to compare actual data from said at least one detection instrument to said estimated behaviour and generate a set of residuals representing a difference between said actual data and said estimated behaviour; and a detection module adapted to received said residuals and identify an anomaly when a predetermined threshold is exceeded.
an analysis module comprising a computationally-intelligent model of a behaviour of at least one detection instrument in said infrastructure, said model having control instrument data from said infrastructure as inputs and an estimated behaviour for said at least one detection instrument as an output;
a comparison module adapted to compare actual data from said at least one detection instrument to said estimated behaviour and generate a set of residuals representing a difference between said actual data and said estimated behaviour; and a detection module adapted to received said residuals and identify an anomaly when a predetermined threshold is exceeded.
26. A system as claimed in claim 25, wherein said infrastructure is a dam.
27. A system as claimed in claim 25, wherein said model also uses detection instrument data as input.
28. A system as claimed in claim 25, wherein said model also uses temperature as input.
29. A system as claimed in claim 25, wherein said model is a neural-network.
30. A system as claimed in claim 29, wherein said neural-network is a data-driven parameterized nonlinear model.
31. A system as claimed in claim 30, wherein said neural-network is a coupled computationally intelligent model, an output of said model being a joint function of all input variables.
32. A system as claimed in claim 30, wherein said neural-network is a decoupled computationally intelligent model, a contribution of each input to an output being calculated separately and added together.
33. A system as claimed in claim 25, wherein said computationally intelligent model is a fuzzy network.
34. A system as claimed in claim 25, wherein said computationally intelligent model is a neuro-fuzzy network.
35. A system as claimed in claim 25, wherein said computationally intelligent model is a Bayesian network.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US84792906P | 2006-09-29 | 2006-09-29 | |
| US60/847,929 | 2006-09-29 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2596446A1 true CA2596446A1 (en) | 2008-03-29 |
| CA2596446C CA2596446C (en) | 2012-07-17 |
Family
ID=39264204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2596446A Expired - Fee Related CA2596446C (en) | 2006-09-29 | 2007-08-08 | Infrastructure health monitoring and analysis |
Country Status (1)
| Country | Link |
|---|---|
| CA (1) | CA2596446C (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642827A (en) * | 2021-06-21 | 2021-11-12 | 南方电网调峰调频发电有限公司 | Dam monitoring data analysis method, device, equipment and storage medium |
| CN115659243A (en) * | 2022-12-22 | 2023-01-31 | 四川九通智路科技有限公司 | Infrastructure risk monitoring method and monitoring system based on MEMS |
| CN117611015A (en) * | 2024-01-22 | 2024-02-27 | 衡水烨通建设工程有限公司 | Real-time monitoring system for quality of building engineering |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112982297B (en) * | 2021-04-19 | 2021-07-13 | 四川省水利科学研究院 | System for realizing slope ecological protection based on convolutional neural network |
-
2007
- 2007-08-08 CA CA2596446A patent/CA2596446C/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113642827A (en) * | 2021-06-21 | 2021-11-12 | 南方电网调峰调频发电有限公司 | Dam monitoring data analysis method, device, equipment and storage medium |
| CN115659243A (en) * | 2022-12-22 | 2023-01-31 | 四川九通智路科技有限公司 | Infrastructure risk monitoring method and monitoring system based on MEMS |
| CN117611015A (en) * | 2024-01-22 | 2024-02-27 | 衡水烨通建设工程有限公司 | Real-time monitoring system for quality of building engineering |
| CN117611015B (en) * | 2024-01-22 | 2024-03-29 | 衡水烨通建设工程有限公司 | Real-time monitoring system for quality of building engineering |
Also Published As
| Publication number | Publication date |
|---|---|
| CA2596446C (en) | 2012-07-17 |
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| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request | ||
| MKLA | Lapsed |
Effective date: 20220808 |