UNCLASSIFIED

UNCLASSIFIED

EXPERIMENTAL METHODS FOR

MULTI-GENRE NETWORKS

Acoustic Emission Analysis of Fuel Pumps

Roman Madoerin

Materials Science and Engineering

University of North Texas

ARL Mentor: Dr. Stephen Berkebile

Directorate / Division: VTD / VICTOR ERP

Project Duration: 06/01/2020 to 08/21/2020

• Unmanned Aerial Systems rely on high pressure fuel source to

operate

• Substandard fuels cause damage and failure

– Scuffing is a major source of damage

– Fuel pumps experience failure through scuffing

– When they fail, the engine doesn’t receive enough fuel

• We will:

– Run different fuels in drone engines

• Substandard fuels (ethanol and dodecane)

• Observe failure mechanisms using Acoustic Emission

• Goal: Demonstrate Acoustic Emission (AE) can distinguish

failure mechanisms

– First, need to understand normal operating signal

Stalker Unmanned

Aerial System (UAS)

Scuffing on a high-pressure

fuel pump piston.

• Material micromechanical events create high

frequency soundwaves

• These waves propagate through the material

• Record ultrasound waves at the surface with the

sensor

• Uses:

– Detect cracks during tensile and compression testing

– Detect changes in sliding mechanical interfaces

Acoustic emission testing of tensile test.

Example AE comparison

• AE signal always present at sliding interfaces

• Signal changes when damage is present

• Easily distinguishable differences between signals

• Three data types

– Raw acoustic emission (AE)

– Tachometer- one tic per rotation

– Tachometer- one tic per degree of rotation

• Establish baseline

– Discover patterns

– Make calculations

– Compare data sets

• Acoustic emission and tachometer data

– Plot raw data

• Observe signal patterns

– Calculations

• Speed

• Root mean square (RMS)

• Bandpass Filter to compare RMS at frequency

ranges

• Correlation of AE peaks to tachometer signal

• No AE data of failing fuel pumps yet

– More data collected in future

• Challenging transposing the tachometer signal

– MATLAB has a lot of useful functions for this

– Abundance of noise and peak splitting

– Difficult to distinguish peaks

• Top graph-2940 RPM

• Bottom graph-1100 RPM

• Slower speed has fewer

peaks

• Also lower base signal

• Tachometer signal

• AE vs. angle and speed vs time

Speed correlated to AE frequency

• Angle between the peaks is analyzed

• When angle distribution is plotted, a pattern arises

• Understanding baseline signal

• AE peak frequency correlated to speed

– Speed determined from the AE data

– More noise at high speeds

• Fourier transform shows frequencies

– Majority of activity at 100-300 kHz

• Angle increment correlation to the AE peaks

– Peaks occur at specific angles

– Two distinct correlation locations

TAKE HOME MESSAGE

Regular signal characteristics exist that

will help locate damage indicators

• We will plot the AE signal and tachometer data over

the whole test

• More angle correlation analysis

• More Fourier transform analysis

• Analyze pressure data

• Get more data to look for failure mechanisms

Background Background

Background Objectives Technical Approach

ChallengesResults and Discussion

Results and Discussion

Results and Discussion Results and Discussion

• Angle between the peaks is analyzed

• When angle distribution is plotted, a pattern arises

Results and Discussion

Results and Discussion Conclusion

Next Steps

AE sensor

Pressure censor

Fuel pump and sensors

AE vs time plot Zoomed view of AE peak

2940 RPM AE signal

1100 RPM AE signal

Speed vs time plot

Image of example tachometer Top dead center tachometer data

Moving average speed vs. timeAE intensity vs. rising angle

AE vs angle plot (2940 RPM) Zoomed view

Angle spacing data

Angle distribution (2940 RPM) Angle distribution 1100RPM)

RMS=0.4389

RMS=0.8504

Bandpass ratios at different frequencies

RMS and speed data

(Top) Stalker XE UAS. (n.d.). Retrieved July 23, 2020, from

https://www.lockheedmartin.com/en-us/products/stalker.html

(Bottom) Source: CCDC ARL.

Prognosis, M. (2018, July 21). Acoustic Emission

Testing Market Globally Grow at a CAGR of 7.5% by

2025: Top Key Players (Olympus, MISTRAS Group,

SGS SA, GE, X-R-I Testing, Applus+, Arcadia

Aerospace, Exova Group, Acuren, COMET, Ashtead

Technology). Retrieved June 29, 2020, from

https://www.openpr.com/news/1138684/acoustic-

emission-testing-market-globally-grow-at-a-cagr-of-7-5-

by-2025-top-key-players-olympus-mistras-group-sgs-sa-

ge-x-r-i-testing-applus-arcadia-aerospace-exova-group-

acuren-comet-ashtead-technology.html

Dykas, B., & Harris, J.

(2017). Acoustic emission

characteristics of a single

cylinder diesel generator at

various loads and with a

failing injector. Mechanical

Systems and Signal

Processing, 93, 397-414.

doi:10.1016/j.ymssp.2017.01.

049

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en-XX/help/372416L-

01/svtconcepts/svspeedfreq/