structural health monitoring: basic approaches and ...€¦ · prof. dr.-ing. c.-p. fritzen...

University of Siegen

Institute of Mechanics and Control Engineering – MechatronicsProf. Dr.-Ing. C.-P. Fritzen

Structural Health Monitoring: Basic Approaches and Applications

Claus-Peter Fritzen

Uppsala, Sweden, 10.11.2010



Introduction

Basic principles of Structural Health Monitoring

Model-based damage localization

Damage detection with Null Space-Based Fault Detection

Method (NSFD)

Load identification

Ultrasonic Waves: Spectral Element Method (SEM)

Damage localization in high frequency domain

Conclusions

Outline



Members of the working group



Researchers:MSc. Maksim KlinkovDipl.-Ing. Philipp KösterDipl.-Ing. Peter KraemerDipl.-Ing. Martin KübbelerDipl.-Ing. Jochen MollDipl.-Ing. Rolf T. SchulteMSc. Kejia XingMSc. Cheng YangDipl.-Ing. Erion Zenuni MSc. Yan NiuMSc. Miguel Torres

Graduate Students:Inka BütheIbraim DzhaferovTorsten FischerPhillipp HilgendorffBernd SchenkelDaniel GinsbergDavid GossenRannam Chaaban

Technical Staff:Gerhard DietrichDipl.-Ing. Wolfgang Richter

Secretary:Gisela Thomas

Director:Prof. Dr.-Ing. C.-P. Fritzen

External PhD Students:MSc. Dongsheng Li, Univ. of Dalian, ChinaDipl.-Ing. Benjamin Eckstein, EADS/AirbusMSc. Carsten Ebert, Fa. Wölfel

Members of the working group



SHM system components

Diagnosis

Sensor signals

Environ./Operat. cond.

Excitation(active/environ.)

Prognosis

Load identificationExtreme events

Material modelsDamage models

Decision,Action

PredictionRUL

Damage type and extent

Damage detection,

localization

Sensor self-diagnosis

Different measurement equipments and systems

System coordination

Choice of sensors• Types• Number• Positions



Mechanical:Static measurement (strain, displacements,...)Vibration measurements using either modal data,

frequency or time responsesStress wave measurements:

Ultrasonic guided waves (Lamb waves)Acoustic emission

Electric, Electro-mechanical, Electro-Magnetic:Impedance based methods with PZTEddy current methodsElectric resistance-based methodsElectro-magnetic

Basic Principles of SHM methods



Global and Local Methods

• Monitoring of structural parts • Dense sensor network• Sensors close to damage• critical location required (hot spots)

• High frequency• Sensitive to small damage

Local:Global:

• Monitoring of whole structure • Rough sensor network• Sensors not necessarily close to damage

• No knowledge about critical location required

• Low frequency• Less sensitive to small damage



Operational/Ambient Excitation

Test Excitation

Environmental & Operational Conditions

Structure(System) Response

Feature Extraction

ReferenceFeatures

Differences, Projections, ...

Residuals(Symptoms)

Residuals evaluation:• Damage Indicators• Statistics,Thresholds

Inverse Modeling:• Localization• Extent of Damage

Decision

Damage identification scheme Measurements Pattern Recognition

Decision



Structural Model

Dynamics of the mechanical system(nonlinear eqn. of motion)

),,(),,,,(),( ededed tt θθfθθxxgxθθM =+ &&& ),,,,( tedd xxθθΓθ && =

),,,,( edt θθxxhy &=

Dynamics of thedamage growth

Measurement Equation



Model-based damage localization



Eigenvalue Problem

( ) 0MK =− ii ϕω 2

Analytical modal data set is obtained from solutionof Eigenvalue Problem using FE-matrices M and K

Assume: Damage causes a local change of stiffness:

∑+=+= jjΔΔ aKKKKK 00

Correction factor approach:



Eigenvalue-/eigenvector residuals and sensitivities

Eigenvalues Eigenvectors

⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

∂∂

∂∂

∂∂

∂∂

=

k

i

S

a

a

aa

1

22

1

1

1

λ

λ

λλ

λ

M

O

K

⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

∂

∂∂∂

∂∂

∂∂

=

k

j,i

,

,,

S

a

a

aa

1

212

11

1

11

ϕ

ϕ

ϕϕ

ϕ

M

O

K

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−

−−

=

i,undami,dam

,undam,dam

,undam,dam

r

λλ

λλλλ

λ M22

11

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

−

−−

=

i,undami,dam

,undam,dam

,undam,dam

r

ϕϕ

ϕϕϕϕ

ϕ M22

11



Inverse Problem: Solution

( ) ( ) rWSaISWS TT =Δ+ γγ 2

Ill-posed problem → regularization (2. term)

Tykhonov-Phillips-Regularization

εraS +=Δ

MinJ aTT →ΔΔ+=Δ aWaWa εε)(

( ) rWSaWSWS Ta

T =Δ+

Use subset selection to find most significant components of Δa



Z24-Bridge: Use of Modal Data from Ambient Vibrations

Three span concrete box girder bridge over the A1 Bern-Zürich, CH60m length, two lanes, one sidewalk

Photos: KU Leuven

EU COST F3 Action: Benchmark example for testing damage diagnosis algorithms



Results of Reference Measurements

Modes 3 and 4Mode 1 Mode 2 Mode 5

Output-Only Modal Data: Stochastic Subspace Identification with MACEC

Provided by: B. Peeters & G. DeRoeck, KU Leuven, Belgium

Meshgrid and Setup



Results of Reference Model Updating

Mode 1 Mode 2 Mode 3 Mode 4 Mode 5Meas. [Hz] 3.9 5.0 9.8 10.3 12.7Model [Hz] 3.9 5.3 9.8 10.4 12.0

Comparison of eigenfrequencies:

ModelMode 1 Mode2 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8

Meas. Mode 1 99.6 0.0 0.0 0.0 0.2 0.0 0.0 0.0Mode 2 0.1 97.0 1.6 1.0 0.0 0.0 0.6 0.2Mode 3 0.2 3.0 96.2 2.9 0.2 0.3 0.5 0.0Mode 4 0.0 2.3 4.9 94.8 0.2 1.1 0.7 0.4Mode 5 0.0 0.2 0.1 0.3 91.4 0.0 0.6 0.2

MAC-values [% ]Table of MAC-values:

Updating using SQP-algorithm (Matlab-function „fmincon“)

( )∑∑==

−+⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

eses n

iiiiMAC

n

i measi

imeasii MACwwJ

modmod

1

2,,

1

2

2,

2mod,

2,

, )(1)(

)( pp

pωωω

λ



Damage Scenario: Pier Settlement

Settlement of the „Koppigen-pier“ with hydraulic jackshere: settlement of about 95mm

Simulation of the dangerous case of undercutting of the pier,this type of damage can cause a collapse of the structure

Damage: Cracks in the concrete box near the connection to the pier



Damage Scenario: Pier Settlement

Settlement of the „Koppigen-pier“ with hydraulic jacks (95mm)Simulation of the dangerous case of undercutting of the pier

Damage: Multiple cracks in the concrete box near the connection to the pier

Mode 1 Mode2 Mode3 Mode4 Mode5Reference [Hz] 3,9 5,0 9,8 10,3 12,7Pier settlement [Hz] 3,7 4,9 9,2 9,7 12,0

Change of Eigenfrequencies

MAC [%] Mode 1 Mode 2 Mode 3 Mode 4 Mode 5Mode 1 99,8 0,2 0,2 0,1 0,8Mode 2 0,0 98,8 1,8 1,6 0,0Mode 3 0,3 5,0 86,4 19,1 0,0Mode 4 0,0 3,8 1,0 88,7 1,8Mode 5 0,1 0,1 2,3 5,0 90,1

MAC Meas. undamaged/damaged



Identification Results

Result:Identification of 7elementswith reduced stiffness

Data: Eigenfrequency and eigenvector residuals and sensitivities (5 modes)

Solution of inverse problem with subset selection (regression analysis)



Software: Model-based damage localization



Steelquake-Structure (ELSA-JRC, Ispra, I)

Sensor positions

Cooperation during the EU Cost Action F3 „Structural Dynamics“



Damage position Results of localization

Steelquake-Structure: Results



measured input yi

Residual Generator

Damage Indicatorζ D

y2

y1

yi

… ( )HKvec T=ζ

Correlation function:

Nullspace-based Fault Detection (NSFD)

Ref.: Basseville et.al. 2000 Fritzen/Mengelkamp 2001

( ) ( )⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−= ∑

−

=

qN

k

Tq kyqky

qNR̂

111

ζζ 1ˆ −Σ= TD



Null-Space based Fault Detection (NSFD)

Important remark: • Constant environmental and operational conditions (EOC)• One Reference• However: changes in EOC can reveal changes of features in same order of magnitude

like damage

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

=

−+

+

1

132

21

βαα

β

β

βα

RR

RRRRRR

H

ˆˆ

ˆˆˆˆˆˆ

,

LL

MOMM

L

L

Hankel matrix (R = auto and cross correlations)

( )i,,i vec βαHKζ ⋅= 0

Residuals (K0 = left kernel space of the reference H0)

iTiiˆD ζζ 1−∑=

Damage indicator (Σ = Covariance matrix)with for intact structure0≅iD

Damage detection



Weighted Hankelmatrix:

∑=

=c

jji

EOCi

jcw1

)(00 )(μ

HH

])[( 0 iEOCii vec HKζ =

Classification with fuzzy-k-means

Training data

Calculate DI with one Reference

Classifikation of DI w.r.t EOC: fuzzy-k-means

New calculation of DIwith more references and threshold setting

Weights: jiw

Calculation of and DIfrom test data

jiw

Damage detection under consideration of changing EOC



SHM of a Complex Composite Structure

ARTEMIS Satellite



ARTEMIS Satellite Antenna

Cooperation with the Univ. of Madrid (Profs. Güemes, Lopez-Diez, Cuerno)Active PZT sensors and actuators for testing



Crack

Simulated Damage Scenarios

Small hole



Experimental Results: NSFD



Simulated data: 168h (no fault) + last 34h (progressive damage).Input: node 24 (force from measured wind speed, here supposed to be unknown).EOC: changing wind speed, wind direction, position of the nacelle, rotational speed of blades. Output: s1-s8 (response included 8 bending modes); Measurement time for one data set: 10 Min.;

Sample rate: 50Hz.

Damage: Young‘s-modulus reduction (1%, 5%, 10%and 25% at element 59)

Example of damage detection



Damage detection with one reference

Training data and results of classification

Damage detection with more referencesand fuzzy-k-means

classification

Example of damage detection



External Forces Identification - An Inverse ProblemImpulse response-matrix (calculated or measured)

U H Y( t ) ( t ) ( t )∗ +Δ = Δ Δ

• Solely ill-posed (Dimension of H)

• Ill-posed and rank deficient (Dimension of H and sensors locations)

• Unstable (Noisy measurements)

0

y H ut

t( t ) ( t ) ( )dτ τ τ= −∫

F

Existing approaches:Modal based – SWAT(Batemen,1992), Input force identification in time domain (Genaro,1998), Optimization dynamic programming (Doyle 2001), Inverse structural filter - ISF (Steltzner & Kammer,2001), Time delay method for input estimation (Nördstrom, 2004) ,Observer based methods MPIO (Söffker & Idriz, 2003, Ha & Trinh, 2004), Neuronal Networks (Ljang 2001, Uhl, 2006), Impact identification of stiffened composite panels (Seydel & Chang 2000), …

Source: Areva-Multibrid GmbH



External Forces Identification - An Inverse ProblemImpulse response-matrix (calculated or measured)

0

y H ut

t( t ) ( t ) ( )dτ τ τ= −∫

F

Simultaneous State and Input EstimatorGiven System

Observer systemDesign of asymptoticallystable Observer withN, L, T and Q matrices

( )ˆ ,ˆN Ly Tf y

Qy

ω ω ξ

ξ ω

= + +

= +

&

( ),,

x E M f yu y H

ξ ξ ξξ

ξ⎡ ⎤ = +

= ⎢ ⎥ =⎣ ⎦

&

0 0T Tˆe , V e Pe, P P , Vξ ξ= − = = > <& & &&

(( ) ), ,x Ax Bu f x u yy Cx Du= + += +

&




Example: Laboratory structure 1- Wind Load



Example: Laboratory structure 1- Impact Load



Measured & Reconstructed Impulse Excitation

Example: Laboratory structure 1- Impact Load



Example: Tripod Laboratory Structure

Wind load

Force sensor

Accelerometer

Strain gauge

Observer

FE-Model

Reconstructed forces



Example: Tripod Laboratory Structure

Measured and Reconstructed Force



M5000-2 Wind Load Reconstruction

Rated power 5.000 kW

Cut-in wind speed 4 m/s

Rated wind speed 12 m/s

Cut-out wind speed 25 m/s

Nacelle height ~97m

Rotor diameter 116 m

Blade weight 16.500 kg

Hub weight 60.100 kg

Nacelle weight 199.300 kg

Design life time 20 years




M5000-2 Wind Load Reconstruction

Observer

F

F̂

__ Acceleration norm. Nacelle__ Acceleration orth. Nacelle

( )

xu

E M f yy H

ξ

ξ ξ ξξ

⎡ ⎤= ⎢ ⎥⎣ ⎦= +=

& ,




M5000-2 Measured operational dataNacelle directionWind direction



M5000-2 Estimated Wind Force

0 500 1000 15000

2

4

6

8

Time [s]

Ang

le[d

eg]

Pitch

212Betz windF c v Aρ=

200 400 600 800 1000 1200 1400

8

10

12

14

Time [S]

Vin

d V

eloc

ity[m

/s]

Wind Velocity



∑ ⋅=i

i tt )()(),( iurrF ϕ

Distributed force with unknown force spatial distribution

: Orthogonal force basisfunctions. (e.g. mode shapes)

)(xiϕ

)(tiu : Time history coefficient.

Distributed Forces: Basic idea



Application: Guangzhou TV Tower

Ambient vibration measurement data (24 hours) is available.

Fig. 2 Guangzhou New TV Tower (610 m). Fig. 3 3D model in ANSYS. Fig. 4 Reduced 3D beam model.

Source: Hongkong Polytechnic University



SHM Using Ultrasonic Waves

Goal:

• improved system development process

• virtual SHM system design

• efficient simulation tools

• many SHM approaches based on propagation of elastic waves

• data-based, model-free• setup: time consuming, costly,

many pre-tests, “optimization” by trial and error



• finite elements with high degree of interpolation polynomial

• carefully chosen nodal base and numerical integration rule

• combines advantages of both methods

global pseudospectral-method

„classical“ FEM

numerically efficient, high accuracy

geometric flexibility

spectral element method, SEM

Ultrasonic Waves: Spectral Element Method



),,,(),,(),,(),,,(~ 0 tzyxwxz

tyxztyxutzyxu y ⋅∂∂

+⋅+= θ

),,,(),,(),,(),,,(~ 0 tzyxwyz

tyxztyxvtzyxv x ⋅∂∂

+⋅−= θ

),,,(),,,(~ tzyxwtzyxw =

Kinematics:

Nodal Base:

)()1( 12 ξξ −⋅− NLo

)()1( 12 ηη −⋅− NLo

Lobatto polynomials of order (N-1)

A Spectral Element for Flat Shells



Approximation of displacements:

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⋅=Ψ=

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

∑∑∑∑+

=

+

=

+

=

+

=

),(ˆ),(ˆ),(ˆ),(ˆ),(ˆ

)()(),(

),(),(),(),(),(

1

1

1

1

1

1

1

1

)(

ji

ji

jiy

jix

ji

j

N

i

N

ji

N

i

N

j

eijijy

x

vu

w

vu

w

ηξηξηξθηξθηξ

ηψξψηξ

ηξηξηξθηξθηξ

q

Selected shape functions:

)(ηψ)(ξψ1D Lagrange inter-polation polynomials

and :

Important property:ijji δξψ =)(




Element stiffness matrix:

( ) [ ] ( ) .det),(),(det),()],([1

1

1

1

)( ∑∑∫∫+

=

+

=Ω

≈Ω=N

i

N

jijij

Tijijjie

Te yxyxwwdyxyx JBDBJBDBK

Element mass matrix:

( ) [ ] ( )∑∑∫∫+

=

+

=Ω

≈Ω=1

1

1

1

)( det),(),(det),()],([N

i

N

jijij

Tijijji

Te yxyxwwdyxyxe JΨHΨJΨHΨM

for symmetrical material lay-up: diagonal mass matrix


( ) [ ] ( )∑∑∫∫+

=

+

=Ω

≈Ω=1

1

1

1

)( det),(),(det),()],([N

i

N

jijijmat

Tijijjimat

Te yxyxwwdyxyxe JΨCΨJΨCΨC

Damping matrix:



UD-GFRP plate:

Comparison of Numerical and Experimental Data



UD-GFRP plate:Excitation:

5 cycle burst, 100kHz hann-windowed,actuator: P5




Comparison of Numerical and Experimental DataCFRP plate with delamination: low-velocity impact

energy: 15J

Material data:

Stacking: [0, 90, -45, 45, 0, 0, 90, -45, 45]sE1 [GPa] E2 [GPa] G12 [GPa] G13 [GPa] G23 [GPa] ν12=ν13=ν23 ρ [kg/m3]

155,0 8,5 4,0 4,0 4,0 0,3 1600



Separated upper and lower elements:

− conforming mesh

− nodes remain in midplane

− offset to midplane

− additional coupling

− mode conversion

− with small gap

Modelling of Delamination



CFRP plate: undamaged stateExcitation:

5 cycle burst, 60kHz hann-windowed,actuator: P5

SEM model:approx. 160,000 dofs




CFRP plate: damaged state Sensor P2; excitation of P5 at 60kHz

Sensor P2; excitation of P5 at 120kHz

vid




3 damage scenarios in a stiffened panelStiffened panel under investigation:

• D1: base-plate delamination• D2: stringer delamination• D3: stringer debonding



3 damage scenarios in a stiffened panelDamage case D1, base-plate delamination:

• excitation: 4 cycle burst, 70kHz

• visibility of damage is isolated by stiffeners

• quantitative results

z-displacement after 100 μs



3 damage scenarios in a stiffened panelDamage case D2, stringer delamination:

x-displacement after 210 μs

• stringer sensors: higher sensitivity

• factor 10 in amplitude



3 damage scenarios in a stiffened panelDamage case D3, stringer debonding :

z-displacement after 150 μs

• debonding: energy-based scheme

vid1

vid2



Damage Localization in Anisotropic Plates

Sum-Travel Time:

Geometric Solution (upper triangle):

1 2TOF TOF TOF= +

Group Velocity:

1,2(f d, , OC, E )Grc f θ τ=

1 2

2 1

1 2

sin( )sin( ) sin( )

( , , ) ( , , )Gr Gr

TOFL L

c fd EOC c fd EOC

π− θ − θ =θ θ

+θ + τ π − θ + τ

L

Properties of anisotropic approach: Damage Location found through intersection of non-elliptic curvesAccurate estimation of time of flight (based on Hinkley-Criterion)Accurate velocity model (Experiments, Simulation, DISPERSE etc.)



Damage Localization in Anisotropic PlatesS0-Mode has been selected for localization (faster than A0-Mode)

Velocity Profile for S0-Mode at 100kHzSolution for two actuator-sensor pairs

Curves are piecewise linear

Mirror damage position



Damage Localization in Anisotropic PlatesAnalysis of all actuator-sensor combinations

Distribution of Curve-IntersectionsStatistical Evaluation by means of probability density function (pdf)

Mirror damage position artifacts



Lamb Waves in Isotropic PlatesWave propagation analysis using Laser-Doppler-Vibrometry



Lamb Waves in Isotropic Plates

Excitation Signal: Frequency: fc=90kHzNumber of cycles: nS=5

Defect Properties:Two Cuts: 40mm x 1mm

Video of wave propagation:



Lamb Waves in Isotropic PlatesInteractive GPU-based visualization of wave field:



Conclusions• A large variety of approaches for monitoring of structuresexists

• Improve safety concepts and reducecosts by continuous inspection and earlydamage detection, change of design concepts

• SHM not yet as far developed as ConditionMonitoring methods for rotating machines

• Highly multidisciplinary task

• SHM has great potential for various applications: civil, aeronautical, mechanical eng., wind energy plants



Thank you for your attention!

structural health monitoring: basic approaches and ...€¦ · prof. dr.-ing. c.-p. fritzen...

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