Use of Acoustic Emission in
Gearbox Condition Monitoring
RAM 7 Workshop
November 4th & 5th
Steven Carter
Roush Industries
Steven.Carter@Roush.com
Condition Based Maintenance
• Condition based maintenance(CBM) is the
practice of providing maintenance when
needed.
– Effective gearbox CBM is reliant on condition
indicators(CIs).
– Current primary gear CIs are vibration,
temperature, and oil debris based.
– Developing research is showing the benefits
of acoustic emission(AE) based CIs.
2
Outline
• Vibration based condition monitoring theory.
• Acoustic emission condition monitoring theory.
• Current research on AEs applicability to gearbox
condition monitoring.
• Theoretical comparison between AE and
vibration.
• Case study of data collected by Roush.
3
Vibration Based Gearbox Condition Monitoring
• Vibration based indicators are typically used to measure data distribution
and energy off of major meshing harmonics.
Typical Condition Indicator Calculation Process
Collection of
vibration data
Development of
time synchronous
averaged signal
Development of
residual/difference
signal
Typical CI “Base” Metrics
– RMS
– Peak Level
– Normalized kurtosis
4
– Crest factor
– Peak-Peak level
– Sideband levels
Condition indicator
calculation
Vibration Based Gearbox Condition Monitoring – Cont.
•
Normalized Kurtosis – Kurtosis is a measure of a data sets peakedness and
is normalized to reduce sensitivity to the data sets standard deviation. This
measure is the basis for FM4 and NA41,2.



dn d 
N n 1


1
_
N
4
2
 1 N 
_ 

  d nd  
 N n 1 




•
Crest Factor – The ratio of a data sets peak value to its RMS level.
d
peak
d rms
5
2
Vibration Based Gearbox Condition Monitoring – Cont.
•
FM0 – The ratio of a data sets peak-to-peak level to the sum of rms levels
of the main gear mesh harmonics1.
d
peak  peak
n

i
f
•
6
i
 primary
d f i 
mesh
harmonics
Sideband Levels – Several condition indicators exist which measure the
amplitude of orders (typically first order sidebands) centered about the
primary gear mesh frequency.
Acoustic Emission Theory - Background
• Acoustic emission is defined as transient elastic waves
caused by the release of localized stress energy.
• Originally developed in the 1960s for static testing.
• Very popular in non-destructive testing:
– Pipeline leak detection.
– Structural health monitoring.
• Sensitive to crack formation in both metals and composites.
• Generally measured in the 100kHz to 1MHz frequency
range.
• Relatively new to gearbox condition monitoring.
7
Acoustic Emission Theory - Introduction
• Acoustic emission can be thought of as an analog to
structural vibration.
8
Phenomena
Source
Path
Receiver
Acoustic
Emission
Mechanical
disturbance causing
the release of local
stress energy.
Wave propagation
through structure.
Mechanical motion
at sensor location.
Vibration
Mechanical
phenomena causing
input force.
Vibration
propagation due to
system dynamic
response.
Mechanical vibration
at sensor location.
Acoustic Emission Theory – Source
• AE events are caused are caused by the release of
localized stress energy.
• Micro-cracks caused by fatigue, thermal loading, etc.
• Crack initiation, crack propagation, plastic deformation,
impact, and friction.
Shifts in the materials micro-structure
are common AE sources
9
Acoustic Emission Theory – Path
• AE waves are transmitted through the structure via Lamb
and Rayleigh waves.
• Reduced sensitivity to mechanical resonance and
outside noise due to frequency band and mode type.
Lamb Waves
Extensional Mode
Rayleigh Wave
Flexural Mode
Rayleigh wave motion process. Graphic. Accessed October 19, 2014.
http://folk.uio.no/valeriem/spice/Frame/surfacew/index.html
Plate Motion for the Two Zero-order Lamb Wave Modes. Graphic, Wikipedia.
Accessed October 19, 2014. http://en.wikipedia.org/wiki/Lamb_waves
10
Acoustic Emission Theory – Receiver
• AE energy is measured through piezo-electric transducers.
• Transducers can have either a wideband or narrowband
frequency response.
• Mounted in a similar manner to accelerometers.
– Silicone grease is typically used as a coupling agent.
• Sensors come in a variety of sizes and weights.
Kistler AE sensor used for testing at Roush
11
Acoustic Emission Theory – Measurement
• AE is measured via several methods.
– Hit count
– RMS level
– Raw time data (typically recorded based off pre-set triggers)
• Sample rates vary up to 40MHz.
Typical AE RMS level vs. time
Measurement
• Measurement locations are defined through sensitivity studies
such as the pencil lead break test7
12
AE Based Gearbox Condition Monitoring – Previous Research
• AE has been shown to identify faults faster then
vibration3, 5.
– In one case it was noted that AE detected pitting at 8% pitted
area compared to vibration detecting pitting at 30% pitted area6.
• It has been shown that AE amplitude and energy
increases with pitting5, 6.
• Torque appears to have a minimal effect on AE rms
levels; however, gearbox speed does appear to have an
effect5,6.
13
AE Based Gearbox Condition Monitoring – Previous Research
• Heterodyne-based frequency reduction technique has
been successfully applied to sample AE at 100kHz3.
• Toutountzakis et al4 were able to measure the gear
meshing AE transient response.
– Unable to measure differences in AE data from seeded pitting.
• In particular, previous work by the Physical Acoustics
Corporation (Mistras) has shown the success of acoustic
emission when applied to a BV-107 helicopter8.
14
Acoustic Emission Vs. Vibration
Vibration
Measurement Source
Phenomena:
Measurement Source
Phenomena:
•
•
Force variation/transmission error.
Impulse from the release of
localized stress energy.
Pros/Cons:
Pros/Cons:
•
•
•
•
•
•
Large body of research.
Ease of data acquisition.
Largely controlled by system
dynamics.
Dependent on total system vibration.
Complex signal processing.
•
•
•
•
•
•
15
Acoustic Emission
Insensitivity to system dynamics and
total system vibration.
Potential to detect faults sooner.
Potentially high signal attenuation in
system.
Relatively small amount of research.
Actual source mechanism is not fully
understood3.
Potentially difficult data acquisition.
Potential simplicity of signal
processing.
Acoustic Emission Vs. Vibration
16
Vibration
Acoustic Emission
Bottom Line: Vibration has
historical prominence in gearbox
condition monitoring but is fraught
with difficulties.
Bottom Line: AE shows significant
potential but much work is still
required to fully understand its
use in gearbox condition
monitoring.
Roush Test Stand
Brake
Gearbox Under Test
Drive Motor
17
Roush Test Stand – Measurement Set-Up
Kistler AE Sensor
Laser Tachometer
for Shaft Speed
Sensor: Kistler Model #8152B111
Response: Wideband – 200-500kHz filtered
Output: RMS – 0.12ms time constant
Measurement Method: 60s RMS vs. time at 15-30min increments
18
Baseline Gearbox – AE Data
Typical AE Data
AE Data Immediately Prior to Inspection
AE Out (Volts)
Consistent, high
amplitude hits
1.38HP, 7.2Nm Load Case
(Estimated)
19
0.78HP, 4.1Nm Load Case
(Estimated)
~16-20hrs of run time
between these
measurements.
Test-stand downtime
Baseline Gearbox – Gear-Set – Post Run
This gear set started from a new condition and was ran intermittently for several months.
Damage was found in a routine inspection after an uptick in AE activity was noticed.
High wear was seen on both the ring and pinion gear faces
20
Baseline Gearbox – Gear-Set – Post Run
21
Natural Wear Gearbox – AE Data
AE Out (Volts)
Consistent,
high amplitude
hits
Test-stand downtime
0.1HP, 0.9Nm Load Case
(Estimated)
22
0.78HP, 4.1Nm Load Case
(Estimated)
Natural Wear Gearbox – Gear-set – Post Run
This gear set started from a new condition and was ran for 5 days when significant wear
was expected based on the AE data.
Wear was seen on both the ring and pinion gear faces
23
Natural Wear Gearbox – Gear-set – Post Run
24
Roush Testing - Findings
• While small compared to AE hits, the RMS level is
sensitive to gearbox load.
• AE does not appear to be sensitive to seeded faults.
• AE does appear to be sensitive to natural wear as
exhibited in the shown data.
– Wear in gear sets.
– Crack in bellows coupling.
– Drive motor failure.
25
Questions?
References
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2.
3.
4.
5.
6.
7.
8.
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Zakrajsek James J. An Investigation of Gear Mesh Failure Prediction Techniques. NASA
Technical Memorandum 102340. 1989.
Antolick Lance J., Branning Jeremy S., Wade Daniel R., Dempsey Paula J. Evaluation of Gear
Condition Indicator Performance on Rotorcraft Fleet. American Helicopter Society 66th Annual
Forum Conference Proceedings. 2010.
Yongzhi Qu, He David, Yoon Jae, Van Hecke Brandon, Bechhoefer Eric, Zhu Junda. Gearbox
Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors – A Comparative
Study. Sensors 14, no 1. 2014.
Tountountzakis Tim, Keong Tan Chee, Mba David. Application of Acoustic Emission to Seeded
Gear Fault Detection, NDT&E International, Volume 38, Issue 1. 2005.
Tountountzakis Tim, Mba David. Observations of Acoustic Emission Activity During Gear Defect
Diagnosis. NDT&E International, Volume 36, Issued 7. 2003.
Mba David. Prognostic Opportunities Offered by Acoustic Emission for Monitoring Bearings and
Gearboxes. Twelfth International Congress on Sound and Vibration. 2005.
ASTM Standard E976, 2010. Standard Guide for Determining the Reproducibility of Acoustic
Emission Sensor Response. ASTM International. West Conshohocken, PA. 2010. DOI:
10.1520/E0976-10. www.astm.org.
Application of Acoustic Emission to Health Monitoring of Helicopter Mechanical System.
Physical Acoustics Corporation. www.pacndt.com.