Manual UT

vs

PIMS

(Permanently installed monitoring sensors)

F. B. Cegla

Non-Destructive Evaluation Group, Department of Mechanical Engineering

Imperial College London , SW7 2AZ,UK

2/36 Outline

• Motivation/Background

• Corrosion example + Surface Roughness

• The effect of roughness on scattering

• Simulation method (DPSM)

• Results PIMS/ C-Scan

• Conclusions

• Future work

3/36 Motivation/Background

• Corrosion costs several billion $/annum

• Inspection very important to avoid failures

• Main tool Manual UT Inspection

Source:www.ge-mcs.com

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

Measured Thickness (normalised)

Actu

al T

hic

kness (

norm

alis

ed)

After: van Roodselar et al., 2009 Inspectors Summit 2009, Galveston Texas

4/36 Motivation/Background

• Human factors are potential source of large spread

• Mechanized scanning Inspection (C-Scan)

• Permanently installed sensors (PIMS)

Source:www.permasense.com

• What is the likely size of measurement errors?

• What is the likely influence of roughness on the ultrasonic

signal?

Source:www.sliverwingme.com

5/36

Wall thickness (T)

-0.02 -0.015 -0.01 -0.005 0 0.005 0.01 0.015 0.02

0

0.005

0.01

0.015

0.02

0.025

0.03

Transmitter Receiver

Not to scale

Surface skimming wave

Backwall reflection

22

122

1ppttT

Pitch (p)

Wave speed (v)

t1

t2 t2 = ?

Ultrasonic thickness measurement principle

Time

Am

plitu

de

6/36 What is the effect of roughness on UT

Ingredients considered:

• Length scales that have an effect on UT signal

• Typical surface roughness in the field

• Transducer geometry

• Fast simulation technique for statistics

• Signal processing techniques

7/36

• Gaussian distributed: Uses normally distributed random numbers

to generate different surfaces with similar statistics.

Correlation length (λ0)

RMS height (σ)

Correlation length (λ0) RMS height (σ)

Rough surface definition

8/36

Scale of roughness that affects UT signal

• Rayleigh parameter: transition to ‘high’ surface RMS

• RMS value ~ 0.2mm in steel for S waves (~2.5 MHz) or P

waves (~5 MHz)

• But what about horizontal extent? What horizontal scales

must be present for scattering to influence the signal?

4cos

k

k = wavenumber σ = RMS height θ = incident and reflected angle

J. A. Ogilvy. Theory of Wave Scattering from Random Rough Surfaces. IOP publishing Ltd. 1991

9/36

x-axis (mm)

y-a

xis

(m

m)

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10

8

6

4

2

0-2

-1

0

1

2

x-axis (mm)

y-a

xis

(m

m)

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10

8

6

4

2

0-2

-1

0

1

2

x-axis (mm)

y-a

xis

(m

m)

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10

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6

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0-2

-1

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1

2

x-axis (mm)

y-a

xis

(m

m)

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10

8

6

4

2

0-2

-1

0

1

2

am

plit

ud

e (

arb

)

Scale of roughness that influences UT signal

am

plit

ud

e (

arb

)

λs=0.5mm (0.3λ) λs=2mm (1.2λ)

λs=8mm (5λ) λs=50mm (31λ)

10/36

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0

2

4

6

8

10

12

x-axis (mm)

y-a

xis

(m

m)

sinusoidal surface wavelength = 0.2mm

backwall

transmitter

receiver

Scale of roughness that influences UT Signal

• 2D Problem, Tx, Rx (0.6 width) 2MHz, 5 cycles, = 1.6mm:

0 2 4 6 8 10-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

time (s)

am

plitu

de

(a

rb)

flat backwall

sinusoidal backwall

hilbert envelope

NOTE: trough in surface always occurs directly between transmitter and receiver

11/36

0 2 4 6 8 10-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

time (s)

am

plitu

de

(a

rb)

flat backwall

sinusoidal backwall

hilbert envelope

-6 -4 -2 0 2 4 6

0

2

4

6

8

10

12

x-axis (mm)

y-a

xis

(m

m)

sinusoidal surface wavelength = 2.4mm

backwall

transmitter

receiver

Scale of roughness that influences UT Signal

• 2D Problem, Tx, Rx (0.6 width) 2MHz, 5 cycles, = 1.6mm:

NOTE: trough in surface always occurs directly between transmitter and receiver

12/36

0 2 4 6 8 10-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

time (s)

am

plitu

de

(a

rb)

flat backwall

sinusoidal backwall

hilbert envelope

-6 -4 -2 0 2 4 6

0

2

4

6

8

10

12

x-axis (mm)

y-a

xis

(m

m)

sinusoidal surface wavelength = 4mm

backwall

transmitter

receiver

Scale of roughness that influences UT Signal

• 2D Problem, Tx, Rx (0.6 width) 2MHz, 5 cycles, = 1.6mm:

NOTE: trough in surface always occurs directly between transmitter and receiver

13/36

0 2 4 6 8 10-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

time (s)

am

plitu

de

(a

rb)

flat backwall

sinusoidal backwall

hilbert envelope

-6 -4 -2 0 2 4 6

0

2

4

6

8

10

12

x-axis (mm)

y-a

xis

(m

m)

sinusoidal surface wavelength = 40mm

backwall

transmitter

receiver

Scale of roughness that influences UT Signal

• 2D Problem, Tx, Rx (0.6 width) 2MHz, 5 cycles, = 1.6mm:

NOTE: trough in surface always occurs directly between transmitter and receiver

14/36

Scale of roughness that influences UT signal

• Look at max. amplitude of backwall reflection as surface

wavelength increases compared to max. amplitude of flat

backwall reflection:

0 5 10 15 20 25-6

-4

-2

0

2

4

6

sinusoidal surface wavelength (s/)

am

plit

ud

e c

ha

ng

e (

dB

)

15/36

Scale of roughness that influences UT signal

The simulations show:

For rough surfaces with horizontal length scales (L)

roughness distorts the signal if:

o vertical RMS height > 0.1-0.125 λ

o horizontal FFT of surface contains 0.8 λ < L < 5-10 λ

16/36 Sulphidation corrosion example

o RMS height = 0.1-0.5mm

o FFT of surface contains scales of L between 1-10mm

Picture from: Taylor-Hobson

17/36 Typical Transducer parameters

Parameter C-SCAN PIMS

Transducer Area 6mm diameter 1x12mm

rectangular

Operational

frequency

5 MHz 2 MHz

Operational

wavelength

(steel)

~1 mm ~1.5mm

Beam footprint (at

10mm depth)

~6mm diameter ~8-12mm

Corrosion RMS height = 0.1-0.5mm ~ 0.1-0.3λ

Corroded surface FFT scales = 1-10mm

or 0.8λ < L < 10λ

18/36

Operation in region where roughness influences

signal

Statistical Simulations

19/36 DPSM: basic principle

• Fundamentally based on Huygens’ principle

• Propagating wave front can be discretised into contributions from

many point sources.

• Field at a single target point is then the summation of contributions

from all point sources

Propagating wave front

Point Sources

Target point

Free Space Greens function

2D:

3D:

m

nfn

m

nm rkHArP)2(

0

m

nfm

n

nm

nm rikr

ArP exp

Placko, D. and Kundu, T. DPSM for Modeling Engineering Problems. (2007)

20/36 DPSM: Matrix formulation

• Equations cast into set of linear equations

• All contributions calculated in a single step

STST AQP

N source points

M ta

rget p

oin

ts

A1

A2

A3

AN

r1m

r2m

r3m

rN m

Pm

P1

P2

PM

PT AS QTS

M

N

M

Nf

M

M

f

M

M

f

N

Nfff

N

Nfff

TS

r

rik

r

rik

r

rik

r

rik

r

rik

r

rik

r

rik

r

rik

r

rik

expexpexp

expexpexp

expexpexp

Q

2

2

1

1

2

2

2

2

2

2

2

1

2

1

1

1

1

2

1

2

1

1

1

1

21/36

22/36

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plit

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)

FEM

DPSM

Kirchoff

σ = 3λc/16

2D simulation case study different models

Method Nodes Time taken (s)

FEM 464 594 236

DPSM 772 13

Kirchhoff 772 11

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xis

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x-axis (mm)

y-a

xis

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m)

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y-a

xis

(m

m)

x-axis (mm)

0 Single acoustic wave transceiver point source (λ=1.6mm)

Chosen for its very high speed and ability to simulate multiple scattering and shadowing

Backwall

23/36

• Transducer field

Permanently installed sensor simulations Avera

ge

surfa

ce

lf lf lf lf lf

x y

z

0.5X10mm contact

2MHz SH wave

24/36

• Transducer field

Permanently installed sensor simulations

x y

z

0.5X10mm contact

2MHz SH wave

Inner surface

Footprint width

25/36 Extracting a thickness from the simulated signal

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time (s)

am

plit

ud

e (

arb

)

TOF

Envelope Peak (EP)

• Wall Thickness Example

– Envelope peak algorithm is used to evaluate the range of wall

thicknesses that would be measured from many surfaces with

the same roughness/surface statistics.

26/36

• 1000 surface realisations at each RMS for correlation length 0.8mm

• Peak to peak timing algorithm

Effect of roughness on thickness measurement?

1: Jarvis, A.J.C. and Cegla, F.B., Application of the Distributed Point Source Method to Rough Surface Scattering and Ultrasonic Wall Thickness Measurement, JASA (2012). 2: Jarvis, A.J.C and Cegla, F.B., (2013) Scattering of SH Waves by Sinusoidal and Rough Surfaces in 3D: Comparison to the Scalar Wave Approximation, manuscript in peer review process 2013

27/36 C-scan transducer field

10mm

8mm 8mm

6mm

28/36 C-scan transducer results, 1000 surfaces

Me

an

of th

ickn

ess e

stim

ate

s (

mm

)

29/36 Sampling due to the footprint

•Transducer only

probes a small area of

the surface

•How much of this

variation is due to this

sampling effect? 10mm

9.8mm

Transducer

30/36 C-scan transducer results, 1000 surfaces

Sta

nd

ard

de

v. o

f th

ickn

ess e

stim

ate

s (

mm

)

31/36

32/36

• Vertical RMS >~1/10 incident wavelength

UT signal can be distorted

• Horizontal CL~0.8-10 incident wavelength

UT signal can be distorted

• For simulations with RMS >1/10

UT measurement std > surface RMS

spread due to interaction of signal processing with the scattered signal

• Awareness of this important:

measurement spread and uncertainties not due to UT setup and equipment but due to structure property itself.

Conclusion

33/36

• All UT measurements influenced by the physics:

manual, automatic scanning and permanently installed

Conclusion

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

Measured Thickness (normalised)

Actu

al T

hic

kness (

norm

alis

ed)

After: van Roodselar et al., 2009 Inspectors Summit 2009, Galveston Texas

34/36

Future Work

• Link more temporal and spatial information

• Link to underlying corrosion mechanisms (general vs pitting corrosion)

35/36

For contribution:

• Dr Andrew Jarvis

• Mr Attila Gajdacsi

• Mr Daniel Benstock

Sponsors:

Acknowledgements

36/36 QUESTIONS

QUESTIONS?