LIVE DAMAGE DETECTION

Team Members: Eric Snyder, Hewei Ma, Nicole De GiulioTechnical Advisor: Dr. Joel Harley (University of Utah)Faculty Advisor: Dr. Angela Rasmussen (University of Utah)Industry Advisor: Chris Deemer (Orbital ATK)Special Thanks: Jon Davies (University of Utah)

CALIFORNIA METHANE LEAK

1.6 million lbs leaked per day≈ 4.5 million cars¼ of California’s total emissions

(Sources: Time Magazine and CNN)

CALIFORNIA METHANE LEAK

PIPELINE INCIDENTS

Source: http://www.phmsa.dot.gov/ (Pipeline and Hazardous Materials Safety Administration)

FATALITIES

WHO BENEFITS?

Visual Inspection C-Scan Microwave

METHODS FOR DETECTION

Built a prototype to detect and monitor for damage over long periods of time.

GOAL OF PROJECT

Ultrasonic Guided Waves(Lamb Waves)

OUR METHODThin Aluminum Plate

ActiveEric Hewei

PassiveNicole

Implementation

ACTIVE SYSTEMGenerator

Sensor

Damage

COLLECT BASELINE

No damage present

RECEIVED DATA

Received with damage

Use Correlation Coefficient r:

𝑟𝑟 = �𝑛𝑛

𝑧𝑧𝑥𝑥 𝑛𝑛 𝑧𝑧𝑦𝑦 𝑛𝑛

∑ 𝑧𝑧𝑥𝑥2 𝑛𝑛 ∑ 𝑧𝑧𝑦𝑦2 𝑛𝑛

CORRELATION

LOW CORRELATION

HIGH CORRELATION

DAMAGE DETECTED

If r is below threshold, damage assumed

THRESHOLD

Direct Signal

Received Signal = Reflected Signal + Direct Signal + NoiseBaseline Signal = Direct Signal + Noise

BASELINE SUBTRACTION

Direct Signal

Received Signal - Reflected SignalBaseline Signal =

ISOLATE REFLECTED SIGNAL

SUBTRACTED SIGNAL

Damage Locating Technique

DELAY AND SUM

SIMPLE EXAMPLE

1. Delay Generated Signal2. Sum with each Reflection Signal3. Sum all points in resulting signal4. Repeat

DELAY AND SUM STEPS

d1

d1_a

d1_b

d1 = d1_a + d1_b

SIMULATED RESULTS

10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampl

itude

-2

0

2

Baseline

10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampl

itude

-2

0

2

Received

Time (s) 10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampl

itude

-2

0

2

Subtracted

Time (s) 10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampl

itude

-0.5

0

0.5

Subtracted10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampl

itude

-5

0

5

Generated

x direction (meters)0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

y di

rect

ion

(met

ers)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Plot w/ Damage

Plot w/ Damage

Generator Location

Estimated Impact Location

Actual Impact Location

Sensors

Detects damage in simulations using correlation

Locates damage in simulations using Delay and Sum

CONCLUSION

“An ounce of prevention is worth a pound of cure.”

Benjamin Franklin

[1] T. Clarke and P. Cawley, Enhancing the Defect Localization Capability of a Guided Wave SHM System Applied to a Complex Structure, Structural Health Monitoring: Sage, 2010.[3] C. Liu, J.B. Harley, M. Berges, D.W. Greve, W.R. Junker, I.J. Oppenheim, “A robust baseline removal method for guided wave damage localization,” SPIE, vol. 9061, 2014.[4] E. B. Flynn, et al., “Maximum-likelihood Estimation of Damage Location in Guided-Wave Structural Health Monitoring”, Proceedings of the Royal Society, 2011.[5] J. E. Michaels, “Detection, Localization and Characterization of damage in Plates with an In Situ Array of Spatially Distributed Ultrasonic Sensors,” ECE, Georgia Institute of Technology, Atlanta, GA, 2008.[6] J. E. Michaels, A. J Croxford, Paul D Wilcox, “Imaging Algorithms for Locating Damage via in situ Ultrasonic Sensors,” ECE, Georgia Institute of Technology, Atlanta, GA, 2008.[7] J. E. Michaels, J. S. Hall, T. E. Michaels, “Adaptive Imaging of Damage from Changes in Guided Wave Signals Recorded from Spatially Distributed Arrays,” EVE, Georgia Institute of Technology, Atlanta, GA, 2009.

REFERENCES

Eric Snyder(801) 864-2166snyder.eric.r@gmail.com

CONTACT INFORMATION

Questions?THANK YOU

Eric Snyder(801) 864-2166snyder.eric.r@gmail.com

LIVE DAMAGE DETECTION

Team Members: Hewei Ma, Nicole De Giulio, Eric SnyderTechnical Advisor: Dr. Joel Harley (University of Utah)Faculty Advisor: Dr. Angela Rasmussen (University of Utah)Industry Advisor: Chris Deemer (Orbital ATK)Special Thanks: Jon Davies (University of Utah)

ActiveEric Hewei

PassiveNicole

Implementation

PASSIVE SYSTEM• Damage Detection

• Damage Localization

[9] Traditional Damage Detection Method

PASSIVE EXPLANATION

Time (s) 10 -3

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ampli

tude

10 -3

-2

-1

0

1

2

3

4

5

Simulated Lamb Wave w/ Noise

Tippmann’s Algorithm

Threshold Voltage

TRIGGER

NARROWBAND FILTER

10 -4

0 0.5 1 1.5 2 2.5 3

Ampl

itude

0

0.05

0.1

Before Filtering

Time (s) 10 -4

0 0.5 1 1.5 2 2.5 3

Ampl

itude

0

0.05

0.1

After Filtering

ENVELOPE METHOD

ENVELOPE METHOD

MULTILATERATION

∆12 = 𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥 2 + 𝑦𝑦𝑥 − 𝑦𝑦𝑥𝑥 2 − 𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥 2 + 𝑦𝑦𝑥 − 𝑦𝑦𝑥𝑥 2

(x1,y1)

(x2,y2)

(xp,yp)

EQUATIONS

CURVE INTERSECTION

x direction (meters)0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

y di

rect

ion

(met

ers)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Plot w/ Damage

Plot w/ Damage

Estimated Impact Location

Actual Impact Location

Sensors

Actual Location: (0.3, 0.3)

• Damages Detected

• Multilateration Algorithm

• 96% Accuracy in MATLAB

• >90% Accuracy in LabVIEW

CONCLUSION

“With a working passive damage monitoring

system, technicians don’t need to climb 300

feet high wind turbines to find a small impact.

They could do it remotely and easily.” By Hewei Ma

[1] J. D. Tippmann, X. Zhu and F. Lanza di Scalea, ‘‘Application of damage detection methods usingpassive reconstruction of impulse response functions’’, University of California, San Diego CA 92093, USA[2] Marchi L D, Marzani A, Speciale N and Viola E 2011 A passive monitoring technique based on dispersion compensation to locate impacts in plate-like structures Smart Mater. Struct. 20 035021[3] Ciampa F and Meo M 2010 Acoustic emission source localization and velocity determination of the fundamental mode A0 using wavelet analysis and a Newton-based optimization technique Smart Mater. Struct. 19 045027[4] Lee, H. Accuracy limitations of hyperbolic multilateration systems. IEEE Transactions on Aerospace and Electronic Systems, AES-11, 1 (Jan. 1975), 16–29.

REFERENCES

[5] B. Gueye et al., “Constraint-based Geolocation of Internet Hosts,” IEEE/ACM Trans. Net., vol. 14, no. 6, Dec. 2006, pp. 1219–32.[6] B. Xu, L Yu, and V Giurgiutiu, “Advanced Methods for Time-Of-Flight Estimation with Application to Lamb Wave Structural Health Monitoring,” Stanford University, Palo Alto, CA. Rep. 2009.[7]S. M. Ziola, M. R. Gorman, “Source Location in Thin Plates Using Cross correlation”, Naval Postgraduate School, Monterey, CA, Dec, 1991[8] T Clarke and P Cawley, Enhancing the Defect Localization Capability of a Guided Wave SHM System Applied to a Complex Structure, Structural Health Monitoring: Sage, 2010.[9] LM Wind Power, http://www.sunwindenergy.com/wind-energy/high-tech-remedies, online resources, 2016.

[10] V. Kratochvil, “Public Domain Pictures”, http://technology.nasa.gov/t2media/tops/img/LAR-TOPS-128/TOP1_front.jpg, Online resources, 2016

REFERENCES

Questions?THANK YOU

• Hewei Ma• (626) 560-0902• u0765961@Utah.edu

LIVE DAMAGE DETECTION

Team Members: Nicole De Giulio, Hewei Ma, Eric SnyderTechnical Advisor: Dr. Joel Harley (University of Utah)Faculty Advisor: Dr. Angela Rasmussen (University of Utah)Industry Advisor: Chris Deemer (Orbital ATK)Special Thanks: Jon Davies (University of Utah)

ActiveEric Hewei

PassiveNicole

Implementation

• Simulation proved effective• Active ~90% accuracy• Passive ~90% accuracy

• Next stage: experiments

No knowledge prior to this project

LABVIEW

DAQmx

DATA ACQUISITION

Passive SystemTriggering

Active SystemGenerating Signal

DATA ACQUISITION

Receiving Generating

CONSTRUCT PLATE

ACTIVE SYSTEM

Plot

LocateActive

Delay&Sum

Window

Filter

Received

GaussianWave

DataSetup

ACTIVE SYSTEM

GAUSSIAN WAVE

Time (s) 10 -4

4 4.2 4.4 4.6 4.8 5 5.2 5.4 5.6 5.8 6

Ampl

itude

(V)

-1

-0.5

0

0.5

1

Generated Signal

RECEIVED SIGNAL

FILTER

DAMAGE DETECTION

Filtered Received

Filtered Baseline

Correlation Coefficient

𝑟𝑟 = �𝑛𝑛

𝑧𝑧𝑥𝑥 𝑛𝑛 𝑧𝑧𝑦𝑦 𝑛𝑛

∑ 𝑧𝑧𝑥𝑥2 𝑛𝑛 ∑ 𝑧𝑧𝑦𝑦2 𝑛𝑛

WINDOW

Time (s)0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

Ampl

itude

(V)

10 -3

-4

-3

-2

-1

0

1

2

3

Windowed Signal

DELAY AND SUM

ACTIVE RESULTS

PASSIVE SYSTEM

Trigger

Window

Envelope & Filter

Time of Arrival

Multilateration

Plotting

PASSIVE SYSTEM

TRIGGER

Time (s)0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

Ampl

itude

(V)

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Received Signal

Trigger Threshold

Time (s)0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

Ampl

itude

(V)

-0.02

-0.01

0

0.01

0.02

Windowed Signal

WINDOW

ENVELOPE AND FILTER

Time (s)0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

Ampl

itude

(V)

10 -3

-1

-0.5

0

0.5

1

Filtered and Enveloped Signal

TIME OF ARRIVAL

MULTILATERATION

∆12 = 𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥 2 + 𝑦𝑦𝑥 − 𝑦𝑦𝑥𝑥 2 − 𝑥𝑥𝑥 − 𝑥𝑥𝑥𝑥 2 + 𝑦𝑦𝑥 − 𝑦𝑦𝑥𝑥 2

Possible grid pointsX

Y

Signal Estimated Point

PASSIVE RESULTS

LIVE DEMO

• Runs live• Detects damage on the plate• Active and Passive have ~100% detection accuracy

• Passive has ~89% locating accuracy

CONCLUSION

ActiveEric Hewei

PassiveNicole

Implementation

• Why?• Who?

CONCLUSION

• “Turn your wounds into wisdom.”-Oprah Winfrey

• [1] M. Niethammer, L.J. Jacobs, “Time-frequency representation of Lamb waves,” School of Civil & Environmental Engineering, GIT, Atlanta, GA.

• [2] P.D. Wilcox et al., “Mode and transducer selection for long range lamb wave inspection,” Journal of Intelligent Material Systems and Structures, vol. 12, 2001.

• [3] P. Cawley, “Practical long range guided wave inspection-managing complexity,” Department of Mechanical Engineering, Imperial College, London.

• [4] P. Wilcox et al., “The effect of dispersion on long-range inspection using ultrasonic guided waves,” NDET&E International, vol. 34, 1-9, 2001.

• [5] P. Cawley, D. Alleyne, “The use of Lamb waves for the long range inspection of large structures,” Ultrasonics international, vol. 34, pages 287-290, 1996

REFERENCES

Questions?THANK YOU

• Nicole De Giulio• (801) 597-5761• u0765961@Utah.edu