Wireless �Structural Health Monitoring

ChenyangLuCyber-PhysicalSystemsLaboratory

AmericanSocietyforCivilEngineers2017ReportCardforAmerica'sInfrastructureØ  Bridges C+

q  Almost four in 10 are 50 years or older. q  56,007 (9.1%) bridges were structurally deficient.

q  Backlog of bridge rehabilitation needs: $123 billion.q  https://youtu.be/JjN1FwzbJaY

Ø  Dams DØ  Levees DØ  Roads DØ  … …Ø  America's Infrastructure GPA: D+

2!

h+p://www.infrastructurereportcard.org

SmartCivilStructuresDevelop smart structures (with monitoring and control) to prevent…

3!

Freewaya:er1989SanFranciscoEarthquakeMinneapolisBridgeCollapse

StructuralHealthMonitoringCurrentPracCce

Ø  Bridges: inspected manually once every two years.Ø Costly and time consuming.

4

Highway40ClosingforBooneBridgeInspecConMondayAugust10,2009If you'reheading toSt.Charles thisweekend,Highway40 isnotyourbest opNon. Westbound 40 from Long Road in St. Louis County toRoute94inSt.CharlesCountywillbeclosed(weatherpermiSng)whileworkcrewsinspecttheDanielBooneBridgeacrosstheMissouriRiver.The roadwill closeat5:30a.m.onAugust15andwon't reopenunNlsomeNmea:er9p.m.onAugust16.

WirelessStructuralHealthMonitoringØ  Detect and localize damages to structuresØ  Wireless sensor networks monitor at high spatiotemporal granularities

Ø  Key Challengesq  Computationally intensiveq  Resource constraints

q  Long-term monitoring

5

ExisCng(non-CPS)Approach

Ø Centralized: stream all data to base station for processing.q  Too energy-consuming for long-term monitoring

Ø  Example: Golden Gate Bridge project [Kim IPSN'07].q  Nearly 1 day to collect enough data.

q  Lifetime of 10 weeks w/4 x 6V lantern battery.

Ø  Separate designs of sensor networks (cyber) and damage detection (physical).Ø  Sensor networks focus on data transport.Ø  Not concerned with method for damage detection.

6

DistributedArchitecture

Ø Dilemmaq  Too much sensor data to stream to the base station

q  Damage detection algorithms are too complex to run entirely on sensors

➪ Distributed Architectureq  Perform part of computation on sensor nodesq  Send (smaller) intermediate results to base station

q  Complete computation at base station

7!

Raw

DataParNalResults

Cyber-PhysicalCo-design

Ø  Employ damage detection approach amenable for distributed implementation in sensor networks.

Ø Optimally map damage detection algorithm onto distributed architecture.

8

Raw

DataParNalResults

OurSoluCon

q  Physical: Damage Localization Assurance Criterion (DLAC) [Messina96]

q  Identify structure’s natural frequencies based on vibration data.•  “Signature” of structure’s health

q  “Match” natural frequencies to structural models with damages.

q  Cyber: optimally partition data flow between sensors and base station.q  Minimize energy consumption

q  Subject to resource constraints

9

(1)FFT

(2)PowerSpectrum

(3)CurveFiSng

(4)DLAC

DIntegers

HealthyModel DamagedLocaNon

2DFloats

DFloats

PFloats

D:#ofsamplesP:#ofnaturalfreq.(D»P)

DataFlowAnalysis DLACAlgorithm

10

(3a)CoefficientExtracNon

(3b)EquaNonSolving

5*PFloats

DataFlowAnalysis DLACAlgorithm

11

(4)DLAC

(1)FFT

(2)PowerSpectrum

(3)CurveFiSng

HealthyModel DamagedLocaNon

8192bytes

4096bytes

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

(3a)CoefficientExtracNon

(3b)EquaNonSolving

100bytes

20bytes

EffecNvecompressionraNoof204:1

4096bytes

ImplementaCon

Ø  Platform: Imote2 + ITS400 sensor boardq  13 – 416 MHz XScale CPUq  32 MB ROM, 32 MB SDRAM

q  CC2420 802.15.4-compliant radioq  3-axis accelerometer on sensor board

Ø  Data collection and processing application written with TinyOS 1.1q  243 KB ROM, 71 KB RAM

12!

EvaluaCon:Truss

Ø  5.6m steel truss structure at UIUCØ  14 0.4m long bays, on 4 rigid supportsØ  11 Imote2s attached to frontal pane

13!

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

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 = 0.871

DLAC WS #67

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 = 0.873

DLAC WS #28

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 = 0.825

DLAC WS #35

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 = 0.865

DLAC WS #75

Truss Central Bay Position

Damage correctly localized to third bay!

EnergyConsumpCon

0 0.05 0.1 0.15 0.2 0.25

Decentralized

Centralized

EnergyconsumpCon(mAh)

Sampling

ComputaNon

CommunicaNon

EvaluaCon

14!

EnergyConsumpCon

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

RawDataCollecNon

FFT

PowerSpectrum

CoefficientExtracNon

EquaNonSolving

EnergyConsumpCon(mAh)

Sampling

ComputaNon

CommunicaNon

EvaluaCon

15!

MemoryConsumpCon

0 50000 100000 150000 200000 250000

RAM

ROM

Size(bytes)

RawDataCollecNon

FFT

PowerSpectrum

CoefficientExtracNon

EquaNonSolving

EvaluaCon

16!

Whatwehavelearnedsofar

Ø Cyber-physical co-design of a distributed SHM system.q  Reduces energy consumption by 71%

q  Implemented on iMote2 using <1% of its memory

Ø  Effectively localized damage on two physical structures.

Ø Demonstrated the promise of cyber-physical co-design.

17

G.Hackmann,F.Sun,N.Castaneda,C.LuandS.Dyke,AHolisNcApproachtoDecentralizedStructuralDamageLocalizaNonUsingWirelessSensorNetworks,RTSS2008.

HierarchicalDamageLocalizaCon

Ø  The DLAC method employs no collaboration among sensors à limitations in SHM capabilities.q  For example, cannot detect multiple damages.

Ø New hierarchical architecture for collaborative localization.q  Embed processing into a hierarchical architecture

q  Send (smaller!) partial results between layers of hierarchy

q  Multi-level damage localization

Ø Demonstrate the generality of cyber-physical co-design

18!

Flexibility-basedMethods

Ø  Structures flex slightly when a force is applied

Ø  Structural weakening => decreased stiffnessØ  Flexibility acts as a “signature” of the structure’s health

Ø  Two flexibility-based methods of interest for our workq  Beam-like structures: Angles-Between-String-and-Horizon

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

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

19!

θ!

HierarchicalArchitecture1

Ø  Sensors form groups

Ø  Group membersq  collect raw vibration dataq à power spectrum

Ø  Group leadersq  collect and correlate power

spectrum from children q à modal parameters (natural

frequencies + mode shapes)

20!

BaseStaCon

GroupLeader

GroupLeader

GroupMember

GroupMember

GroupMember

GroupMember

GroupMember

HierarchicalArchitecture2Ø  Base station

q  collects modal parameters from group leaders

q  à structural flexibilityq  compared against “baseline” collected when

structure was known to be healthy

Ø  Differences in flexibility à localize damage

21!

DistributedDataFlow

22!

Sensing

FFT

PowerSpectrum

CrossSpectralDensity

SingularValueDecomposiNon

2Dints

Dfloats

Group Leader!

Flexibility

Base Station!

Dmatrices

Group Member!

DfloatsPnaturalfrequencies+

modeshapes

D:#ofsamplesP:#ofnaturalfreq.

(D»P)

EnhancedDistributedDataFlow

23!

Sensing

FFT

PowerSpectrum

2Dints

Dfloats

PeakPicking

Dfloats

CrossSpectralDensity

SingularValueDecomposiNon

Group Leader!

Flexibility

Base Station!

Pmatrices

Pnaturalfrequencies+modeshapesPfloats

Group Member!

D:#ofsamplesP:#ofnaturalfreq.

(D»P)

MulC-LevelDamageLocalizaConØ  Only a handful of sensors are needed to detect damageØ  As more sensors are added, localization gets more preciseØ  Save energy by exploiting localized nature of flexibility-based approach

24!

ImplementaCon

Ø  Hardware: Imote2 + ITS400 sensor boardq  13 – 416 MHz PXA271 XScale CPUq  32 MB ROM, 32 MB SDRAM

q  CC2420 802.15.4-compliant radioq  3-axis accelerometer on sensor board

Ø  Software platformq  TinyOS 1.1 operating systemq  UIUC’s ISHM toolsuite used for sensing,

reliable communication, and time sync

25!

EvaluaCon:Truss

Ø  Simulation of 5.6 m, 14-member steel truss structure at UIUC

Ø  Simulated sensor data generated in MATLAB and injected into live application using “fake” sensor driverq  Intact data set: no damages

q  Damaged data set: three members reduced on left side of truss, four on right side

26!

EvaluaCon:Truss

Ø  Level 1: nine sensors at uniform points along truss’s length

27!

0 2 4 6 80

1

2

3

4x 10

−6

Element Number

Da

ma

ge

In

dic

ato

r

Damage identified on left half

Damage identified on right half

EvaluaCon:Truss

Ø  Level 2: move all nine sensors to respective halves (emulate higher density)

28!

1 2 3 4 5 6 7 8 9 101112130

1

2

3

Element Number

Da

ma

ge

In

dic

ato

r

1 2 3 4 5 6 7 8 9 101112130

1

2

3

Element Number

Da

ma

ge

In

dic

ato

r

Damage localized correctly to all seven members

EvaluaCon:Truss

Ø Codesigned architecture reduces communication latency from estimated 87s to 0.21s

Ø  78.9% of energy attributable to synchronization and sensing

Ø Compare to theoretical energy supply of 20,250 J (3x 1.5 V, 1250 mAh AAA batteries)

29!

GroupMember

SynchronizaNon 12.1JSensing 23.0JComputaNon 9.28J

CommunicaNon 0.08J

GroupLeader

SynchronizaNon 16.2J

Sensing 21.2J

ComputaNon 8.52J

CommunicaNon 0.76J

Full-ScaleTruss

30!

Image source: Zhuoxiong Sun, Purdue University!

TestResults:Full-ScaleTruss

Ø  Two levels of damage localization

Ø  Level 1: localized�damage to bay 9

Ø  Level 2: localized�damage to element 42

31!

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

Summary

Ø Cyber-physical co-design for wireless structural health monitoringq  Distribute flexibility-based damage localization methods in a

hierarchical architectureq  Multi-level search strategy only activates sensors in area of

interest; many sensors remain asleep

Ø  Localize damage to a simulated truss and a real full-size truss with low energy consumption

Ø  Long-term goal: a general cyber-physical co-design approach to integrated sensing and control

32!

G.Hackmann,W.Guo,G.Yan,Z.Sun,C.LuandS.Dyke,Cyber-PhysicalCodesignofDistributedStructuralHealthMonitoringwithWirelessSensorNetworks,IEEETransacNonsonParallelandDistributedSystems,25(1):63-72,January2014.

ReflecCon:TradiConalMethodologyØ Localize damages on structures using wireless sensors.

Ø Traditional: separate network and civil engineeringq Cyber: Wireless network streams all data to a base station

q  Physical: Base station runs damage localization algorithm

Ø Clean separation of concern, but ineffective

q  Streaming raw data consumes too much energy

CPSCo-Design

Ø Get hands dirtyØ Understand the data flow of damage localization

Ø  But still employ clean abstraction and methodologyØ Optimal data flow embedding in a network

Ø Highly effectiveØ  Reduces energy consumption by 71% [RTSS'08]

34!

1. Design a damage localization method suitable for distributed processing. 2. Model the data flow. 3. Optimally embed the data flow in a sensor network.