SHM_Intro

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LA-UR-07-3231 Unclassified

Introduction to Structural Health Monitoring andFeature Extraction

Charles R. Farrar

Presented at

Engineering Institute Workshop

July 25th, 2006

Engineering Institute

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Structural Health Monitoring


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Motivation for Structural Health Monitoring

Move from time-based maintenance to condition-based maintenance

Combat asset readiness

ew us ness mo e s

Manufacturers of large capital investment hardware can charge by theamount of life used instead of a time-based lease.

-with need to integrate diverse technologies makes StructuralHealth Monitoring a Grand Challenge problem for aerospace,


c v an mec an ca eng neers n e cen ury

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Definition of Damage Damage is defined as changes to

the material and/or geometric

adversely affect its performance.

All materials used in engineeringsys ems ave some n ereninitial flaws.

Under environmental and

grow and coalesce to producecomponent level failure.

-level failure.

Must consider the length andtime scales associated with


amage evo ut on.

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Classifying Damage Identification Methods

Damage Identification

Microscopic flaw/damage identification Used to develop material failure models

Incipient, macroscopic, material/component level damage Non-destructive evaluation (local, off-line inspection)

- - ,on-line)

Component damage/failure system level damage Structural health monitoring Condition monitoring (applied to rotating machinery) Health and usage monitoring systems (HUMS, Rotor craft) Statistical process control (monitors system processes where damage

can be one cause of loss of rocess control

Damage Prognosis Adds prediction of remaining life capability to SHM


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Structural Monitoring SHM

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19Roof

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Ch. 11Ch. 5

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16th Floor

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3RD Floor

9th Floor

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Ch. 13Ch. 9

Structural monitoring is not

G

B

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Basement

Ch. 1used to make decisionsregarding system operationand maintenance


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The Structural Health Monitoring Process

1. Operational evaluationDefines the damage to be detected and begins to

issues for a structural health monitoring system.2. Data acquisition

used in the feature extraction process.

3. Feature extraction -

Data Cleansing

Data Normalization

information from measured data.

4. Statistical model development for

Data Fusion

Data Compression

(implemented by

feature discriminationClassifies feature distributions into damaged or

undamaged category.

so ware an orhardware)


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Introduction to Features What is a Feature?

A feature is some characteristic of the measured response that is,

other signal inspection technique Feature extraction transforms data into information It is desirable to have exam les of the features from both

damaged and undamaged structures Primary Characteristics of features

Sensitivity - Feature should ideally be very sensitive to damage

Dimensionality - Want the feature to have the lowest dimensionpossible

Com utational Re uirements - Features should be com utable

with minimal assumptions and CPU cycles Want to use the simplest feature possible that can

distinguish between the damaged and undamaged


sys em

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Feature Extraction

Approaches to identifying damage-sensitive features. as exper ence

Component and system testing Numerical analysis to simulate damaged system response

Features types. Absolute (derived from single data source, e.g. modal

frequency) Relative (derived from multiple data sources, e.g. mode shape)

Damage sensitive features fall into three categories. Waveform or image comparison

Model parameters

Residual errors between measured and predicted response.


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Feature Extraction Want many samples of low-dimension feature vectors. The need for low-dimension feature vectors often

(e.g. compression of accel.-time histories into modalproperties).

multiple and possibly heterogeneous sources

(estimation mode shapes). .

Ideally, the features should change monotonically withdama e level.

Identify and quantify sources of feature variability.

Incorporate feedback from data acquisition and


.

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Data Normalization

1000

500

0

500

1000

Strain

Signal 1

Data Point # = 26980Time Period = 601.17 sec t = 0.02228 sec

0 100 200 300 400 500 600 700

1000

500

0

500

1000

Strain

Signal 2

7.650 100 200 300 400 500 600 7001000

500

0

500

1000

Strain

Signal 3

14.7

20.35

7.45

18.3

7.35

7.50

.

uency(Hz)

Mean Frequency Value

(Top West Outdoor - Top East Outdoor )

Time (Sec)

1.1

-5.1

-10.5

1.9

-0.2 0.6 0

3

7.05

7.20

FirstFreq


9:15 11:30 13:12 15:13 17:52 20:09 21:20 23:29 1:21 3:19 5:19 7:03 9:22

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Features are Used to Answer the Following:1 Is the system damaged?

Group classification problem for supervised learning Identification of outliers for unsu ervised learnin

2 Where is the damage located? Group classification or regression analysis problem for supervised

learning Identification of outliers for unsupervised learning

3 What type of damage is present?

Can only be answered in a supervised learning mode roup c ass ca on

4 What is the extent of damage? Can only be answered in a supervised learning mode

roup c ass ca on or regress on ana ys s5 What is the remaining useful life of the structure? (Prognosis)

Can only be answered in a supervised learning mode


egress on ana ys s

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