Machine-Condition Monitoring using Vibration Analysis The use of ...
VIBRATION BASED CONDITION MONITORING UNDER NON …
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VIBRATION BASED CONDITION MONITORING UNDER NON-STATIONARY CONDITIONS
• Stephan Heyns
• Professor and Director: C-AIM
• C-AIM University of Pretoria
• South Africa
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Introduction
• Existing physical assets aging - operational life
pushed to new limits - financial & environmental.
• Increasingly complex new assets commissioned.
• Reliability Availability Maintenance Safety (RAMS)
requirement increasing.
• Need cost-effective, robust condition based
maintenance (CBM) strategies.
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Wind turbine gearboxes
In recent years
wind turbines
have sparked
great interest
in monitoring
of complex
machinery
during non-
stationary
operations
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Mining gearboxes
A B
E F G
C
H
D
Tooth damage
Dragline Bucket
Drag Cables
Hoist Cables
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Mining gearboxes
• Expensive gearboxes
• Highly fluctuating load and
speed conditions
• Reversals in direction of
rotation
• Load variations tend to
cause amplitude modulation,
rotational speed frequency
modulation
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Why CM under fluctuating conditions?
• Difficult to create comparable operational
conditions unless equipment is unloaded.
• Monitoring under actual loaded conditions more
likely to show defects.
• Conventional CM methodologies often violate
underlying mathematical assumptions and new
approaches are necessary.
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Agenda
1. Fundamental concepts in non-stationary
vibration based condition monitoring.
2. Applications.
3. New developments in the Centre for Asset
Integrity Management.
4. Conclusions.
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1. Fundamental concepts
Fundamental concepts in non-stationary vibration
based condition monitoring:
• Synchronous averaging
• Order tracking
• Load demodulation normalization
• Instantaneous angular speed
• Phase domain averaging
• Discrepancy signals
• Sensorless signal resampling
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Synchronous averaging
• Extracting periodic signals from a composite signal is based on averaging signal sections of the period sought.
• This does require a priori knowledge of the frequency sought.
Encyclopedia of Vibration p 102
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Order tracking
Sampling at constant sampling
frequency (normal frequency analysis)
8 samples per rev
Sampling at fixed number of
times per shaft revolution
Shock and Vibration Handbook by C.M. Harris
Spectral peak
smeared over
various lines
Spectrum one line
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Load demodulation normalization
• Load demodulation normalization procedure account for modulation caused by fluctuating loads.
• Divide by load modulating envelope existing assumptions
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Load demodulation normalization
• Envelope estimated as low pass filtered signal
maxima.
• Filter frequency optimized to ensure best
conformance between statistical properties of
signals measured under different load
conditions.
• Rotation domain averaging technique combines
ability of COT and time domain averaging to
suppress spectral smearing caused by speed
fluctuations and suppress amplitude of non-
synchronous vibrations.
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Instantaneous angular speed
• IAS sensitive indicator of gear
condition
• IAS less susceptible to phase
distortions introduced by
transmission path compared to
gearbox casing vibration
measurements.
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Phase domain averaging
• Reduce transmission path phase
distortion effects. Employ phase
domain averaging.
• Synchronous averaging with
regard to phase of reference
frequency.
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Discrepancy analysis
Recently focus on low-cost condition monitoring
using empirical models such as:
• probability density functions and regression
functions
• to model complex machine response signals.
Special attention was given to discrepancy analysis
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Discrepancy analysis
• Slide Models not data samples
Autoregressive filters
(linear steady state)
• Parallel adaptive AR filters
• SANC technique based on
LMS adaptive filter
• Schur filter
• Non-linear principal
components
• Likelihood measures based
on Gaussian mixture models
Neural network residual
envelopeTheo Heyns
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Discrepancy transform
• Compare novel signal to reference signal(s) in piecewise manner
• Generate discrepancy transform which indicates where novel signal deviates from reference signal(s)
• Reference signal(s) representative of vibration response from healthy machine as subjected to different/fluctuating operating conditions
• Discrepancy signal expected to be smoother and simpler and subsequently less sensitive to frequency, amplitude and phase modulation.
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Sensorless signal resampling
• Order tracking popular method to account for frequency modulation
• Due to physical and financial constraints not always possible to install an angular position reference sensor (e.g. tachometer)
• Different methodologies have been investigated where shaft speed/ angular position is directly estimated from signal itself
• Due to signal noise/ stochastic behaviour and cross frequency interference the angular position/speed estimates offer limited accuracy
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2. Applications
Accelerated gearbox failure
ACC Fan gearbox
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Accelerated gearbox failure
Accelerated test 17 h
Local damage shows after 8 h
Pinion fails not gear
Much earlier than frequency domain
Gear Damage: Gear machined down.
Regression model weighted average of
ensemble of AR models (range of
operating conditions – healthy state)
updated on statistical model selection
framework.
Discrepancy: Difference between one step
ahead prediction by non-linear AR filter
and observed response waveform
Structure (magnitude and periodicity) of
discrepancy signal contains diagnostic
information.
Kroch & Heyns
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Methodology
• Transform complex (containing lost of information) vibration signal into smoothed discrepancy signal
• Estimate approximate shaft speed based on analysis of original vibration signal
• Resample discrepancy signal to account for most significant frequency modulation
• Analyse resampled discrepancy signal by means of synchronous averaging or spectrum.
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Application: ACC fan gearbox
• ACC fans widely used to
facilitate indirect dry cooling
– 48 fans required per unit
– 288 fans for 6 unit power station
• Recurrent failures of multi-
stage gearboxes at specific
power station
– Same gearboxes intended for
use in 2 new power stations
– 2-year vibration monitoring
programme
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Methodology
• Current power station– 9m diameter fan – Hot air recirculation– Gearbox with helical gears– Pinion gear failures– Gear pump failures
• Vibration monitoring of gearbox over lifespan– Started a couple of months
after installation– November 2011 – November
2013
24t68t
18t
Dri
ve
moto
r
Brg 5/6
79tBrg 5/6
Gear pump
Brg 3/4
Brg 3/4
Brg 1
Brg 2
To fan
Shaft 1
Shaft 2
Shaft 3
1500 RPM
120.6 RPM
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Methodology
• Three adjacent gearboxes instrumented– Single axis accelerometer (radial)– Tachometer– Thermocouple
• Data acquisition with eDAQ lite data logger– 2500 Hz sample frequency
• Recurrent pinion gear failures
• Initially bearing diagnostics were ignored
Boil
er s
ide
HV
yar
d s
ide
Gea
rbox
#1
Gea
rbox
#2
Gea
rbox
#3
Accelerometer position &
orientation
Tachometer
Thermocouple
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Inner shaft lower taper roller bearing
• Two defects on outer race
• Locations correspond to NLL peak positions
• Movement of bearing outer ring in gearbox
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3. New developments in C-AIM
Fundamental concepts in non-stationary vibration
based condition monitoring:
• Synchronous averaging
• Order tracking
• Load demodulation normalization
• Instantaneous angular speed
• Phase domain averaging
• Discrepancy signals
• Sensorless signal resampling
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Optical flow measurement
Localised phase information of the
image convolved with a complex filter.
Very small vibrations.
450 x smaller than single pixel from
video sequence.
20 Hz
225 FPS
450 mm standoff distance
167 micron/pixel image
resolution
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Optical flow measurement
• Up to a resolution/amplitude ratio of 50, the optical flow method is better than 1% accurate
• Optical flow worst performance of 5% error
• Much more accurate than conventional DIC
• Viable for cost-effective structural vibration measurement with the advantage of full field vision!
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Incremental shaft encoders
• Measure shaft angular velocity.
• Normally has several pulses per revolution, as
opposed to one pulse.
• Has potential to measure torsional vibration
(e.g. gearboxes) for diagnostics etc.
• Instantaneous Angular Speed can potentially
be more sensitive to gear faults than
acceleration measurements.
• But IAS usually difficult to measure. Good
quality encoders difficult to install and
expensive.
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Low cost zebra tape encoder
Stationary Piezoelectric
probe
Shaft
Zebra tape
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Geometric effect illustration
Stationary probe
Butt joint
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Method assumptions
• For a shaft encoder with N encoder increments and M recorded revolutions.
• Assume that the angular velocity is linearly varying throughout each increment.
• Assume that the shaft velocity is continuous throughout all shaft increments.
• There are 2MN unknowns and MN equations in system. System is underdetermined and has infinitelymany solutions.
• Bayesian linear regression can be used to addprior information about the system parameters.
• Very promising results for varying speed conditions.
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Blade tip timing for detecting
turbomachine blade problems
Bayesian statistics
Pressure measurementssensitive microphone
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Blade tip timing for detecting
turbomachine blade problems
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Blade tip timing
• The mill is the
Strain gauge – TelemetryBlade Tip TimingScanning LaserHigh Speed photography
Gwashavanhu & Diamond
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Mill monitoring
Poorly performing milling can cause a range of secondary issues:
• Unburnt carbon in ash
• High NOx emissions
• Uneven steam temperatures
• High exit gas temperatures
Salzwedel
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Fault detection and diagnosis
• Use machine learning to extract features from
data to predict and diagnose coal mill failures.
• Three main sources of data:
• Analogue process signals
• Binary event signals
• Mill notifications
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Mill notifications
• Natural language descriptions of faults, events
logged by operators, maintenance personnel
and engineers.
• Dirty, but informative. There are many different
ways to describe the same fault.
• Often include spelling errors, abbreviations:
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Semantic labelling of operator logs
• Refine mill
notification clustering
• Correlate notification
clusters to process
data
• Train a deep neural
network to classify
failures based on
process signals.
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4. Conclusions
• Explained need for CM under non-stationary
conditions
• Introduced a number of very important concepts
in condition monitoring under non-stationary
conditions
• Shown a number of applications
• Introduced proposing new developments in the
C-AIM
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Acknowledgements
I am pleased to acknowledge the critical research
contributions of the following C-AIM post-graduate students
and researchers:
• Dr Corné Stander
• Berndt Eggers
• Dr Abrie Oberholster
• Dawie Diamond
• Benji Gwashavanhu
• Robert Salzwedel
and the Eskom Power Plant Industry Programme (EPPEI)