signal processing for earthen dam measurements analysis ws 2011-11 pyayt.pdfsignal processing for...

UrbanFloodUrbanFlood

Signal processing for earthen dam

measurements analysis

2011 UrbanFlood Workshop

Amsterdam, The Netherlands, November 3, 2011

Alexander Pyayt alexander.pyayt@siemens.com

Ilya Mokhov ilya.mokhov@siemens.com

Natalia Melnikova N.Melnikova@uva.nl

Valeria Krzhizhanovskaya V.Krzhizhanovskaya@uva.nl

Alexey Kozionov alexey.kozionov.ext@siemens.com

Victoria Kusherbaeva victoria.kusherbaeva@siemens.com

Artem Ozhigin artem.ozhigin@siemens.com

2A. PyaytUrbanFlood

The Netherlands and Flood

3200 km of primary dikes

14 000 km of secondary dikes

3A. PyaytUrbanFlood

The Netherlands and Flood

3200 km of primary dikes

14 000 km of secondary dikes

Overtopping

4A. PyaytUrbanFlood

Dike failures

Wilnis 2003 – drought (peat) & uplift

http://wwwen.uni.lu/research/fstc/research_unit_in_ engineering_science_rues/members/stefan_van_baars/research/dike_engineer ing

• 1735 dike failures in the

Netherlands between 1134 and 2006

• rare visual inspections

• old dikes

Ijkdijk piping

34%30%

20%

10%6%

0%

10%

20%

30%

40%

Ove

rtopp

ing

Foun

datio

n

def

ect

s

Pip

ing

and

seepa

ge

Con

duits

and

valv

es

Oth

er

* Percantage of flood events in the USA

5A. PyaytUrbanFlood

UrbanFlood decision support system workflow

6A. PyaytUrbanFlood

Abnormal Behaviour Detection Approach

Analytical redundancy

Physical redundancy (the same placement)

Physical redundancy (type of sensor)

Committee

Feature extraction

Cl1

Sen

sor

mea

sure

men

ts

Con

fiden

ce v

alue

s

-- NORMAL BEHAVIOUR-- ABNORMAL BEHAVIOUR

-- NORMAL BEHAVIOUR-- ABNORMAL BEHAVIOUR

X1

X2

-5 -4 -3 -2 -1 0 1 2 3 4 5

x 10-3

-4

-2

0

2

4

6x 10

-3

Cl2

Clk

Logical groups

Pre-processing

Feature Extraction

Cl1

Cl2

Clk

Decision support

7A. PyaytUrbanFlood

Knowledge about anomaliesReal measurements

+ Real-world data

+ Low cost of non-destructive experiments

- Absence of measurements related to real dike failures

- High cost of destructive experiments

8A. PyaytUrbanFlood

Knowledge about anomaliesPhysical modellingReal measurements

+ Real-world data

+ Low cost of non-destructive experiments

- Absence of measurements related to real dike failures

- High cost of destructive experiments

+ Cheaper than field experiments

- Model adequacy

9A. PyaytUrbanFlood

Knowledge about anomaliesPhysical modellingReal measurements

+ Real-world data

+ Low cost of non-destructive experiments

- Absence of measurements related to real dike failures

- High cost of destructive experiments

+ Cheaper than field experiments

- Model adequacy

10A. PyaytUrbanFlood

Measurements with anomalies

Normal

Abnormal

Normal

Normal

Abnormal

Failure

Nondestructive experiment,not stable dike

Destructive experiment, real dike failure

Zeeland dijkStammerdijk Ijkdijk

11A. PyaytUrbanFlood

Pre-processing

1) Wavelet denoising

2) Spectrum Singular Analysis (SSA )

3) Hodrick-Prescott filter

4) L1 trend filtering

5) Moving average1

0

1 N

i i ll

x yN

−

−=

= ∑

12 2

1 11 2

min ( ) [( ) ( )] ,T T

i i i i i ii t

y x x x x x smoothing parameterλ λ−

+ −= =

− + − − − −∑ ∑

21

1 2

min ( ) ,T T

i i i ii i

y x x x smoothing parameterλ λ−= =

− + − −∑ ∑

DWT Hard thresholding iDWTy xCoefficients

SSA1

N

nn

y c=

=∑ Take only c which corresponds to maximal eigenvalues

y – measurementsx – estimation of a signal

y x

The main idea : to apply methods, which have minimal number of adjustable parameters and require minimal information about signal

Logical groups

Pre-processing

Feature Extraction

Cl1

Cl2

Clk

Decision support

12A. PyaytUrbanFlood

Data pre-processing

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

20

40

60

80

time

valu

es

1.292 1.294 1.296 1.298 1.3 1.302 1.304 1.306 1.308 1.31 1.312

x 104

12

14

16

18

20

22

24

26

28

Smoothing of 'jumps'

time

valu

es

noisy signalwaveletl1hp filterSSAMAsignal

1.44 1.46 1.48 1.5 1.52 1.54 1.56

x 104

22

24

26

28

30

32

34

36

Smoothing of outliers

time

valu

es

noisy signalwaveletl1hp filterSSAMAsignal

Synthesized data

1.180.500.490.240.23RMSE

MASSAhpl1Wavelet

13A. PyaytUrbanFlood

One-side classification

--ABNORMAL BEHAVIOUR-- NORMAL BEHAVIOUR

Logical groups

Pre-processing

Feature Extraction

Cl1

Cl2

Clk

Decision support1) Hypercube

3) Neural Clouds2) Parzen window

14A. PyaytUrbanFlood

Virtual Dike

ffcstab τφσ −+= tan

fτ

fσ

- cohesion, [Pa]

- friction angle, [grad]

- shear stress, [Pa]

- effective normal stress, [Pa]

φ

Mohr-Coulomb plasticity model:

c

stab - stability factor, [Pa] (<0 in case of plastic deformations )

, where

9 m

0 m

Livedijk, Eemshaven, the Netherlands VVK1

Slide 14

VVK1 LIVEDIKEValeria Krzhizhanovskaya; 3-11-2011

15A. PyaytUrbanFlood

Macro-instability Modelling

Distribution of stability factor in the dike at different loading steps:

Beginning of plastic deformations

critical state, water is at 6.6 m above ref. level

Plastic deformations

16A. PyaytUrbanFlood

Simulated sensor data

17A. PyaytUrbanFlood

Anomaly detection in artificial data

0 500 1000 15000

1

2x 10

-6 First principal strain

0 500 1000 15001.5

2

2.5x 10

-5 X deformation

0 500 1000 15000

0.5

1Confidence value of dike normal behaviour

0 500 1000 15000

0.5

1Confidence value of dike normal behaviour

0 500 1000 1500-1

0

1

2x 10

4Stability factor

Detection of anomaly

“Local failure”

460 1123

18A. PyaytUrbanFlood

Variant of combination of the AI component and the Virtual Dike

Committee

Cl1

X1

X2

-5 -4 -3 -2 -1 0 1 2 3 4 5

x 10-3

-4

-2

0

2

4

6x 10

-3

Cl2

Clk

Decision support

19A. PyaytUrbanFlood

AI component implementation

JMS consumer

Java

Mes

sage

Ser

vice

(JM

S)

AI component

Self monitoring

XML reader

Data analysis

XML writerAnySense

Sensor cabinet

DSS, other components

JMS producer

WebDashBoard

Web browser

Dike measurements Confidence values of normal behaviour AI component state

Logical groups

Pre-processing

Feature Extraction

Cl1

Cl2

Clk

Decision support

20A. PyaytUrbanFlood

AI component as part of the UrbanFlood EWS

JMS consumer

Java

Mes

sage

Ser

vice

(JM

S)

AI component

Self monitoring

XML reader

Data analysis

XML writerAnySense

Sensor cabinet

DSS, other components

JMS producer

WebDashBoard

Web browser

Dike measurements Confidence values of normal behaviour AI component state

Joint UrbanFlood & SSG4Env International Monitoring and FloodSafety Workshop,Amsterdam, the Netherlands, Nov 2010

21A. PyaytUrbanFlood

Summary

� Current results◦ Detection of anomalies� Real dikes

� Stammerdijk (published)

� Zeelandijk (to be published)

� Livedijk-based generated data by the Virtual Dike

� Next steps◦ Ijkdijk modelling and data analysis

◦ AI component� application of parametric methods

22A. PyaytUrbanFlood

Acknowledgements

� This work is supported by the EC FP7 project UrbanFlood, grant N 248767

� Siemens LLC Corporate Technology, Russia for financial support

� Alert Solutions (particularly, Erik Peters), WaterNet (particularly, Rob van Putten) for providing data

23A. PyaytUrbanFlood

Thanksfor your attention!

Alexander Pyayt alexander.pyayt@siemens.com