structural(health(monitoringlu/cse520s/slides/shm.pdfclayton, e.h. (2006), “ frequency...

Structural Health Monitoring

CSE 520S Fall 2011

Structural Health Monitoring (SHM)   Problem: detect and localize damage to a structure   Wireless sensor networks (WSNs) monitor at unprecedented temporal

and spaCal granulariCes

  Key Challenges:   Long-‐term monitoring   Rapid on-‐demand analysis   Resource and energy constraints

2

Centralized Designs   Wisden [Xu, SenSys 2004]

  Services for reliable transmission of raw data

  Golden Gate Bridge [Kim, IPSN 2007]   46-‐hop network deployed along Golden Gate Bridge

  BriMon [Chebrolu, MobiSys 2008]   Trains as data mules

  Torre Aquila [CerioZ, IPSN 2009]   Heterogeneous sensors, most with low data rate

  Primarily focus on data transport issues

3

Decentralized Design Principles   Raw sensor data is too large to stream back to the

base staCon   Damage detecCon is too complex to run enCrely

onboard the sensors

  SoluCon: decentralized codesign   Select an algorithm which can be run parCally on the

motes   Send back (smaller!) parCal results to the base staCon to

complete computaCon

4

Raw Data

Decentralized System Evolution   Damage LocalizaCon Assurance Criterion (DLAC)

  No collaboraCon needed among nodes => lightweight network architecture

  Some limitaCons in damage detecCon

  Flexibility-‐Based Methods   CollaboraCon among nodes => more complex architecture, but

more robust damage localizaCon   Even more energy savings through sensor selecCon

5

Damage Localization Assurance Criterion (DLAC)   Collect vibraCon data and use to idenCfy structure’s

natural frequencies [Messina, J. Sound and Vibra:on, 1998]   “Signature” of structure’s health

  Several traits useful for a decentralized system   No data exchanged among nodes   IniCal stages are computaConally inexpensive   Later stages have much smaller inputs (typically <1% of iniCal data

set)

6

Vibration Data Input Damage LocalizaCon Algorithm

7

0 1 2 3 4 5 6 7 8!2000

!1500

!1000

!500

0

500

1000

1500Time History WS2

Time(s)

Am

plit

ud

e

0 10 20 30 40 50!20

0

20

40

60

80

100

120

140Power Spectrum WS2

Frequency (Hz)

Am

plit

ud

e(d

B)

FFT + Power Spectrum Analysis Damage LocalizaCon Algorithm

8

Curve Fitting Damage LocalizaCon Algorithm

9

! "! #! $! %! &!!#!

!

#!

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6-,73,819.:;<=

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.

.

)*+,-./0,12-34

C3-D,.6@22@8E

DLAC   A mathemaCcal model of the

structure is created offline   Used to predict effect of structural

damage on natural frequencies

  Natural frequency data input:   Healthy structure   Healthy model

  Model damaged at different

discrete locaCons   (Possibly) damaged structure

10

DLAC Output Damage LocalizaCon Algorithm

11

0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

DLAC WS2

Element Position

Highest correlaCon to damage at LocaCon 5

D: # of samples P: # of natural freq. (D » P)

Data Flow Analysis Damage LocalizaCon Algorithm

12

(1) FFT

(2) Power Spectrum

(3) Curve FiZng

(4) DLAC

D Integers

Healthy Model Damaged LocaCon

D Floats

D/2 Floats

P Floats

Data Flow Analysis Damage LocalizaCon Algorithm

13

(1) FFT

(2) Power Spectrum

(3) Curve FiZng

(4) DLAC Healthy Model Damaged LocaCon

8192 Bytes

4096 Bytes

D: 2048 P: 5 Integer: 2 bytes Float: 4 bytes

4096 Bytes

20 Bytes

EffecCve compression raCo of 204:1

Implementation   Hardware plaoorm: Intel/Crossbow Imote2 + ITS400

sensor board   13 – 416 MHz XScale CPU   32 MB ROM, 32 MB SDRAM   CC2420 802.15.4-‐compliant radio   3-‐axis accelerometer on sensor board

  Sosware plaoorm: TinyOS 1.1   243 KB ROM, 71 KB RAM

14

Evaluation: Truss   5.6 m steel truss structure at UIUC

  Fourteen 0.4 m long bays, siZng on four rigid supports

  11 Imote2s atached to frontal pane

15

Wireless SensorTruss Frontal Panel

Fig 12. DLAC results for truss bay # 3

6.0 CONCLUSIONS

In this study a successful demonstration for an in-situ experimental validation of a

correlation-based decentralized damage detection technique using a wireless sensor network has

been performed. Structural damage was detected with sufficiently high correlation percentage in

two experimental structures independently of the damage hypothesis used in the sensitivity

matrix. On-board processing iMote2 capacities were exploited to reduce communication load

and make the application scalable within a wireless sensor network.

7.0 ACKNOWLEDGMENT S

Funding for this research is provided in part by the National Science Foundation; grant NSF

NeTS-NOSS Grant CNS-0627126, by Washington University in St. Louis. Additionally, the

authors would like to thank Prof. Bill Spencer and Shin-Ae Jang for the use of and assistance

with the experimental truss.

8.0 REFERENCES

Clayton, E.H. (2002), “Development of an Experimental Model for the Study of Infrastructure

Preservation”, Proceedings of the National Conference on Undergraduate Research,

Whitewater, Wisconsin.

Clayton, E.H., Koh, B.H., Xing, G., Fok, C.L., Dyke, S.J. and Lu, C. (2005), “Damage

Detection and Correlation-based Localization Using Wireless Mote Sensors”, Proceedings

of ’05 The 13Th

Mediterranean Conference on Control and Automation, Limassol, Cyprus.

Clayton, E.H. (2006), “ Frequency Correlation-based Structural Health Monitoring with Smart

Wireless Sensors”, Master of Science Thesis, Washington University in St. Louis.

1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.868

DLAC WS #32

Truss Central Bay Position

1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.864

DLAC WS #45


1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.871

DLAC WS #67


1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.873

DLAC WS #28


1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.825

DLAC WS #35


1234567891011120

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 3Y = 0.865

DLAC WS #75


Damage correctly localized to third bay

Latency

0 2000 4000 6000 8000 10000 12000 14000

Decentralized

Centralized

Latency (ms)

Sampling ComputaCon CommunicaCon

EvaluaCon

16

Energy Consumption

0 1 2 3 4 5 6

Decentralized

Centralized

Energy consump3on (J)

Sampling ComputaCon CommunicaCon

EvaluaCon

17

DLAC: Findings   Onboard processing reduces latency by 66% and energy

consumpCon by 71%   EffecCvely localized damage to discrete locaCons on two

structures

  Results indicate the power of holisCc energy management

18

G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke, “A HolisCc Approach to Decentralized Structural Damage LocalizaCon Using Wireless Sensor Networks”, RTSS, 2008.

Flexibility-‐Based Methods   Structures flex slightly when a force is applied   Structural weakening => decreased sCffness   Flexibility acts as a “signature” of the structure’s health

  Two flexibility-‐based methods of interest   Beam-‐like structures: Angles-‐Between-‐String-‐and-‐Horizon

flexibility method (ASHFM) [Duan, J. Structural Engineering and Mechanics 09]

  Truss-‐like structures: Axial Strain flexibility method (ASFM) [Yan, J. Smart Structures and Systems 09]

19

θ

Network Architecture   Sensors form physically-‐

colocated groups   Group members collect raw

vibraCon data and process into power spectrum data

  Group leaders collect corresponding power spectrum data from children, correlaCng into modal parameters (natural frequencies + mode shapes)

20

Base Sta3on

Group Leader

Group Leader

Group Member

Group Member

Group Member

Group Member

Group Member

Network Architecture   Base sta3on collects modal

parameters from group leaders, completes processing into structural flexibility

  Output is compared against “baseline” output from healthy structure

  Differences in flexibility can be used to detect and localize damage

21

Base Sta3on

Group Leader

Group Leader

Group Member

Group Member

Group Member

Group Member

Group Member

Standard Data Flow

22

Sensing

FFT

Power Spectrum

Cross Spectral Density

Singular Value DecomposiCon

2 x D ints

D floats

Group Leader

Flexibility

Base StaCon

D matrices

Group Member

D floats P natural frequencies +

mode shapes


Enhanced Distributed Data Flow

23

Sensing

FFT

Power Spectrum

2 x D ints

D floats

Peak Picking

D floats

Cross Spectral Density

Singular Value DecomposiCon

Group Leader

Flexibility

Base StaCon

P matrices

P natural frequencies + mode shapes

P floats

Group Member


Multi-‐Resolution Damage Localization   Under ASHFM and ASFM, only a handful of sensors are

needed to detect damage   As more sensors are added, localizaCon gets more fine-‐

grained   Significant energy savings by exploiCng localized nature of

flexibility-‐based approach

24

Evaluation: Simulated Truss   SimulaCon of UIUC truss structure

  Simulated sensor data generated in MATLAB and injected into live applicaCon using “fake” sensor driver   Intact data set: no damages   Damaged data set: three members reduced on les side of truss,

four on right side

  Result: Level 1 idenCfied damage on both halves of truss; Level 2 localized damage correctly to all seven members

25

Evaluation: Simulated Truss   Codesigned architecture reduces

communicaCon latency from esCmated 87 s to 0.21 s

  78.9% of energy atributable to synchronizaCon and sensing

  Compare to theoreCcal supply of 20,250 J from 3x AAA bateries

26

Group Member

SynchronizaCon 12.1 J

Sensing 23.0 J

ComputaCon 9.28 J

CommunicaCon 0.08 J

Group Leader

SynchronizaCon 16.2 J

Sensing 21.2 J

ComputaCon 8.52 J

CommunicaCon 0.76 J

G. Hackmann, W. Guo, G. Yan, C. Lu, and S. Dyke, “Cyber-‐Physical Codesign of Distributed Structural Health Monitoring With Wireless Sensor Networks”, ICCPS, 2010.

Preliminary Test Full-‐Scale Truss

27

Image source: Zhuoxiong Sun, Purdue University

Test Results: Full-‐Scale Truss

  Two levels of damage localizaCon

  Level 1: localized damage to bay 9

  Level 2: localized damage to element 42

28

2 3 4 5 6 7 8 910 20 3132 420

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 10−6

Truss Element Number

AS F

lexi

bilit

y D

amag

e In

dica

tor

Conclusion   Codesign approach integrates two SHM methods with

efficient distributed compuCng architectures   Mul:-‐level search strategy only acCvates sensors in area of

interest; many sensors remain asleep   Shown to localize damage to real beam and truss

structures

  Long-‐term goal: a general codesign framework for integrated sensing and control

29

Papers   G. Hackmann, F. Sun, N. Castaneda, C. Lu, and S. Dyke,

A HolisCc Approach to Decentralized Structural Damage LocalizaCon Using Wireless Sensor Networks, IEEE Real-‐Time Systems Symposium (RTSS'08), December 2008.

  G. Hackmann, W. Guo, G. Yan, C. Lu and S. Dyke, Cyber-‐Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks, ACM/IEEE InternaConal Conference on Cyber-‐Physical Systems (ICCPS'10), April 2010.

30

Evaluation: Cantilever Beam   2.75 m x 7.6 cm x 0.6 cm steel beam in

Structural Control and Earthquake Engineering Lab

  Damage simulated at three locaCons by ataching a steel bar

  7 Imote2s atached at equidistant locaCons

32 0.6

6 m

1.3

5 m

1.9

m

2.7

5 m

Wireless Sensor

Damage Location

damage case by applying a hammer strike along the weaker bending axis. Results reported using

the entire network are depicted in Figs. 6, 7 and 8 where corresponding identified natural

frequencies and DLAC measurements are introduced for each damage scenario. DLAC values

determined at sensors along the length of the beam are provided. Values close to unity indicate

damage location. The entire network report successful damage detection results for all damage

scenarios with correlation measurements greater than 90% at the damaged positions. Recall

experimental damage positions D1, D2 and D3 are associated with elements 5, 10 and 14,

respectively. Despite consistency in the results, some of the sensors report correlation

measurements greater than 50% for some of the element positions. As explained previously,

results of correlation-based methods may not be unique. Frequency change vectors associated

with one damage location could be potentially the same as those obtained with several

combinations of damage location when reduced numbers of modes are used. Therefore, the

inclusion of more modes is expected to clarify the results by concentrating the correlation

measurements around one damage location. Note that these results are obtained with a damage

hypothesis of only 67% of the actual damage. Two additional damage hypotheses are

implemented to test the DLAC performance off-line using different damage assumptions and

acceleration records previously obtained for debugging purposes. New sensitivities matrices and

corresponding frequency change vectors were developed with a prescribed analytical damages

equivalent to 200% and 33% of the actual damage. Results showed the same tendencies and

consistency, and were also successful for all damage scenarios with high correlation

measurements.

Fig 6. DLAC results for element position # 5

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.94

DLAC WS1

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.971

DLAC WS2

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.972

DLAC WS3

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.955

DLAC WS4

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.964

DLAC WS5

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.965

DLAC WS6

Element Position

0 10 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

X = 5

Y = 0.97

DLAC WS7

Element Position

Damage correctly localized in all three trials

structural(health(monitoringlu/cse520s/slides/shm.pdfclayton, e.h. (2006), “ frequency...

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