vibration analysis for reciprocating compressors
DESCRIPTION
Vibration Analysis for Reciprocating CompressorsTRANSCRIPT
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orbit VOL. 32 | NO. 2 | APR. 2012 A Technical Publication for Advancing the Practice of Operating Asset Condition Monitoring, Diagnostics, and Performance Optimization
ORBIT
VOLU
ME 32 N
UM
BER 2 APRIL 2
012
AN
OM
ALERT* UN
DER TH
E HO
OD
MOTOR CONDITION MONITORING & DIAGNOSTICS
AnomAlert* Motor Anomaly Detector Under the Hood PG10Vibration Data Identifies Hot Spot on Motor Rotor PG26
NEW DEPARTMENT ITEMS
Application Notes PG25 System 1* Software Tips & Tricks PG42
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EDITORS NOTE
We are also continuing with our series of ADRE* Tips, and
for the first time in a while, have included a Recip Tips
article as well. For the first time ever, we are including
a System 1* Software Tips & Tricks article, and an
Application Note summary. I anticipate sharing many
more of these useful software tips and application
references as we continue updating the content of Orbit.
Speaking of sharing, we will soon be posting Orbit articles
at a convenient public blogsite, so that our readers can ask
questions and post comments about individual articles
and related concepts. I look forward to learning from these
conversations, and getting some ideas for additional
follow-on articles to address the points that are raised.
Finally, I couldnt help but notice that the little stop sign
icon in our reader service card looked a bit odd for some
reason. It was introduced in 2004, and apparently was
not questioned until now. Gina and I fixed it for this
issue. Can you spot the difference? I suppose these
older back-issues will now become valuable collectors
items like double-struck coins, or
postage stamps that are printed
upside down. If you are lucky enough
to have one of these rarities, hang
onto it as a treasured family heirloom!
Editors Notepad
Cheers!
Gary
Gary Swift
Editor
Orbit Magazine
Greetings, and welcome to
Orbit! This issues cover is
based on the graphic that
accompanies our Feature
article. I anticipate that
our technical readers will
appreciate the humorous
analogy of a tiny V-8 engine
symbolizing the power of
the monitor. In keeping with
the theme of motor condition
monitoring, we also have
a classic case history that
describes how vibration
analysis detected a problem
with a motor that had a
load-related thermal bow.
-
IN THIS ISSUE
In this Issue
orbitVolume 32 | Number 2 | April 2012
A Technical Publication for Advancing the Practice of Operating Asset Condition Monitoring, Diagnostics, and Performance Optimization
Publisher: GE EnergyEditor: Gary B. SwiftDesign Coordination: Eileen OConnellDesign: Gina AlteriEuropean Circulation: Estelle SjournNorth American Circulation: Karen SchanhalsPrinter: RR Donnelley
CONTRIBUTORSGE EnergyRoengchai ChumaiCharles HatchJohn KinghamStuart RochonGaia RossiRob Winter Adrian Cobb Nate Littrell
ArtesisCaner Kuzkaya
CREDITSGE Measurement and ControlGlobal CommunicationsNik Noel
Questions, suggestions, and letters to the editor may be addressed to: ORBIT Magazine 1631 Bently Parkway South Minden, Nevada USA 89423 Phone: 775.782.3611 Fax: 775.215.2855 e-mail: [email protected]
Printed quarterly in the USA. More than 35,000 hard copies of each issue distributed worldwide
* Denotes a trademark of Bently Nevada, Inc., a wholly owned subsidiary of General Electric Company.
Copyright 2012 General Electric Company. All rights reserved.
10 FEATURES
AnomAlert* Motor Anomaly Detector Under the Hood
NEWS
04 Advanced Machinery Dynamics Course Invitation
06 Celebrating Our Experience
DEPARTMENTS
ADRE* Tips 18 How to Display Filtered and Unfiltered Orbits Together
Application Note 25 Resources for Managing Electrical Runout
Case Histories 26 Vibration Data Identifies Hot Spot on Motor Rotor
Recip Tips34 Vibration Analysis for Reciprocating Compressors Part 1
System 1* Software Tips & Tricks42 How to Create a Machine Reference Dataset
-
Advanced Machinery Dynamics Course
Measurement & Control, a GE Oil & Gas business invites you to extend your knowledge of machinery diagnostic techniques and rotor dynamics as applicable to rotating machinery in a 5-days Advanced Machinery Dynamics Course, from June 11th through 15th in Florence, Italy.
This high-level course was last
conducted in 2009 and is now fully
updated in response to previous
participants feedback and to meet
the most demanding machinery
diagnostics challenges.
In our hands-on workshops you will
use standard vibration diagnostic
tools on machine simulating rotor kits.
With us you will for sure put
theory into practice.
Case histories highlighting vibration
documentation, analysis, and
machine malfunction corrective
techniques will be presented
throughout the course.
Who should attend?
Engineers desiring to advance
their machinery vibration
diagnostics skills
Engineers involved in the design,
acceptance testing, and main-
tenance of rotating machinery
Post-graduate engineers
Academic researchers and profes-
sors involved in rotor dynamics
Prerequisites
Prior to this course, participants
should have completed the Machinery
Diagnostics course or be
ISO category 3 certified.
The Machinery Diagnostics course will
be offered the week before for those
who do not yet meet the prerequisite.
If you wish to take part in this
course even though you dont
fulfill the prerequisites, your
enrolment will be accepted but
in this case you may not get the
expected return on investment.
PresentersRon Bosmans
Global Director
Machinery Diagnostics Services
19952006 (Retired)
Nicolas Peton
MDS Technical Leader for South
and West Europe
GE Measurement & Control
Rob Winter
Senior Specialist
Learning and Development
GE Measurement & Control
Arun Menon
Global Director
Machinery Diagnostics Services
GE Measurement & Control
REG
ISTE
RTO
DAY
!
4 ORBIT Vol .32 No.2 Apr.2012
NEWS
-
Advanced Machinery Dynamics Course1115 June 2012 | Florence, Italy | GE Learning Center
For registration and logistics details please contact
Marta Petruzzelli at [email protected] or call +39 0396561420
EUR 3,500.00 (including one joint dinner)
Apr.2012 No.2 Vol .32 ORBIT 5
-
The Bently Nevada team had a saying back in 1990:
Duplicating our products is challenging. Duplicating our people is impossible.
Although a lot has changed over the past 22 years,
it is still our people who create the high-quality
products and provide the excellent care that our
customers depend on. In keeping with tradition,
the employees at the home of our product line
in Nevada, USA, pause once a year to recognize
the dedicated work of our coworkers who have
reached significant service milestones. The people
listed here are only a small fraction of our total
team, yet they represent more than 1800 years
of combined experience! Our multinational team
extends around the world, where similar commit-
ment can be found in every global region.
Celebrating Our Experience
35Y E A R S
BACK ROW, LEFT TO RIGHT: Ron Sanchez, Jack Howard, Al Davis. FRONT ROW: Candy Baldwin, Pam Caughron.
Photos by Adrian Cobb
NEWS
-
BACK ROW, LEFT TO RIGHT: Tim Walmsley, Randy Willis, Paul Blair. MIDDLE ROW: Alan Thomson, Gerry ONeill, Mike Evans, Jana Ferguson. FRONT ROW: Debbie Hartzell, Jill Evans. NOT SHOWN: Denise Clendenen, John Grant, Andrew Grimm, Doug Hoover.
BACK ROW, LEFT TO RIGHT: Rob Rose, Dave McNeilly. MIDDLE ROW: Brenda Allmett, Jerry Pritchard, Jean Van Den Berg. FRONT ROW: Dave Whitefield, Robert Nikkels. NOT SHOWN: Sherrie Ashurst, Stan McPartland, Tim Sheets, Dave Van Den Berg.
30Y E A R S
25Y E A R S
Apr.2012 No.2 Vol .32 ORBIT
NEWS
-
BACK ROW, LEFT TO RIGHT: Thane Tahti, Mike Holcomb, Tammy Rhead. FRONT ROW: Ronnie Swan, Francie Welsh, Diana Thomas. NOT SHOWN: Rudy Capa, Ken Ceglia, Ken Forbes, Steve Kichler, Dave McElroy, Barbara Uemura.
BACK ROW, LEFT TO RIGHT: Carol Brennaman, Kyle Hoffman, Tim Gross, Landon Boyer. NEXT ROW FORWARD: Pamela Greek, Deana Cormier, Paul Lindsay, Ben Willis. SECOND ROW FROM FRONT: Leslie Yered, Beth Ferrara, Ray Jensen, Scott Williams. FRONT ROW: Enrique Corcostegui, Larry Mcdonald, Steve Schmid. NOT SHOWN: Daniel Abawi, Matt Anderson, Alex Beitel, Dale Bradley, Chien Cheng, Mitch Cohen, Doran Cushing, Mike Hanifan, Mike Rokusek, Bryan Shadel.
20Y E A R S
15Y E A R S
8 ORBIT Vol .32 No.2 Apr.2012
NEWS
-
BACK ROW, LEFT TO RIGHT: Ron Robbins, Daniel Jenkins, Todd Balcon, Paul Carrion. NEXT ROW FORWARD: Stephen Lau, Kris Wickstead, Becky Cawthorne, Donna Barber. NEXT ROW FORWARD: Jay Brown, Bev McMahon, Lisa Akins, Kelly Kondo, Sandi Bachstein, Tina Ku, Christina Caldwell. SECOND ROW FROM FRONT: Ray Murphy, Brian Steinkraus, Richard Fraser, Laura Love, Ruby Ecobisag, Lynne Towle. FRONT ROW: Manuel Lara, Violeta Della Pella, Jack Riley, Joe Jenks. NOT SHOWN: Jennifer Carlson, Ken Crosby, Michael Gaynor, Paul Gonzi, Dustin Hess, Brad Kelly, Rick Lohroff, Lelana Moralez, Paul Parisien, Jean Untereiner.
10Y E A R S
Apr.2012 No.2 Vol .32 ORBIT
NEWS
-
Anom Alert Charles T. Hatch
Principal Engineer
Caner Kuzkaya
Vice President, Artesis A.S.
The AnomAlert Motor Anomaly Detector is a
system of software and networked hardware that
continuously identifies faults on electric motors
and their driven equipment. AnomAlert utilizes
an intelligent, model-based approach to provide
anomaly detection by measuring the current
and voltage signals from the electrical supply to
the motor. It is permanently mounted, generally
in the motor control center and is applicable
to 3-phase AC, induction or synchronous, fixed
or variable speed motors. AnomAlert models
are also available for monitoring generators.
FEATURES
10 ORBIT Vol .32 No.2 Apr.2012
-
Anom Alert under the hood
Apr.2012 No.2 Vol .32 ORBIT 11
FEATURES
-
12 ORBIT Vol .32 No.2 Apr.2012
FEATURES
The AnomAlert diagnostic solution can be used together with
a vibration monitoring system as a complementary tool for
detecting electrical faults. Alternatively, it can be used where
dedicated vibration monitoring is not practical, economical, or
comprehensive enough. It can detect changes in the load the
motor is experiencing due to anomalies in the driven equipment or
process such as cavitation or plugged filters and screens. Since it
doesnt require any sensor installation on the motor itself or on the
associated load, AnomAlert is especially attractive for inaccessible
driven equipment and is applicable to most types of pumps,
compressors, and similar loads. It is also well suited to the moni-
toring of submersible, borehole, downhole, and canned pumps.
The AnomAlert monitor uses a
combination of voltage and current
dynamic waveforms, together
with learned models, to detect
motor or driven equipment faults.
Active learning is backed up by
an additional fleet model in case
the monitor has been installed on
an already defective motor. The
monitor detects differences between
observed current characteristics
and learned characteristics and
relates these differences to faults.
Motor fault detection is based on a
learned, physics-based motor model,
where constants in the model are cal-
culated from real-time data and com-
pared to previously learned values.
Mechanical fault detection is based
on power spectral density amplitudes
in particular frequency bands, in
relation to learned values. This infor-
mation is combined automatically
with expert diagnostic knowledge.
Because of this spectral band
approach, mechanical fault detection
is not precise, but provides guidance
toward a class of possible faults. The
sensitivity to some faults (for example
rolling-element bearing faults) will
decrease with distance from the
fault. On the other hand, faults that
increase motor load are independent
of the distance from the motor.
The spectrum-based mechanical
fault detection in the AnomAlert
monitor seems similar to Motor
Current Signature Analysis (MCSA),
but several important differences
set it apart from typical MCSA:
The AnomAlert monitor uses cause-
effect (voltage-current) relation-
ships, while MCSA uses the current
only. Changes in input voltage will
cause changes in the current that
could lead to false alarms in MCSA.
The cause-effect relationship in
the AnomAlert processing helps
protect against these false alarms.
The AnomAlert monitor uses a
stable reference data set that is
obtained from ten days of motor
operation, and it calculates
alarm threshold levels specific
to the equipment itself.
Detected anomalies are subjected
to a sophisticated change
persistence algorithm to guard
against false alarms, making the
AnomAlert monitor less sensitive to
random fluctuations in the signals.
We will now delve more deeply
into the operating principles of the
AnomAlert monitor. We will not dis-
cuss current and voltage transformer
selection or installation, or operating
modes and programming; these
aspects are covered elsewhere.1
Data AcquisitionVoltage and current signals from
all three phases (6 total signals) are
sent to the monitor where they are
digitized for further signal processing.
Voltages less than 480 V can be
input directly, while higher voltages
require a potential transformer.
Depending on the application,
current transformers or Hall-effect
current sensors are used to sense
and step down the motor currents.
AnomAlert processing operates on
a 90 second iteration cycle. At the
beginning of every 90 second itera-
tion, the monitor samples voltage and
current waveforms. The remainder of
the period is used for post processing
analysis and front panel update.
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Apr.2012 No.2 Vol .32 ORBIT 13
FEATURES
All six waveforms can be exported to
a text file for further post process-
ing. The text file has no headers
and six columns, corresponding
to paired voltage and current
waveforms V1, I1, V2, I2, and V3, I3.
Modeling And Fault DetectionThe AnomAlert monitor uses four
different approaches to fault detec-
tion. One is based on internal motor
characteristics; another is based on
frequency analysis of the residual cur-
rent spectrum; a third analyzes actual
line voltages and currents to check for
certain types of line and current faults;
finally, the fourth uses fleet data from
similar motors to provide an inde-
pendent diagnostic reference. We will
discuss how all of these work in turn.
The Internal Motor ModelFor an ideal motor, voltage and cur-
rent waveforms are sinusoidal at line
frequency. The changing line voltage
creates magnetic forces that cause
the rotor to turn, and the amplitude
and phase of the motor currents are
related to the input voltages through
the internal mechanical and electrical
workings of the motor. We can think
of the line voltage waveforms as
inputs to the motor, and the current
waveforms as outputs. The motor
electrical and mechanical internals
can be thought of as a transfer func-
tion that converts the input voltage
waveform into the output current
waveform (Figure 1). This is the key
to understanding the internal motor
model in the AnomAlert monitor.
The monitor uses a linear model
for the electrical and mechanical
internals of the motor. This physics-
based model is derived from a set
of differential equations, and it can
be expressed as a transfer function.
During the learning process, the
monitor determines the coefficients
of this model. For a normal motor,
the model transfer function is a
close approximation to the real
physical transfer function of the
motor. We will discuss later the
special case of what happens when
the AnomAlert monitor models a
motor that already has a defect.
While monitoring, the AnomAlert
monitor takes the input voltage
waveform and passes it through the
model transfer function to obtain
a theoretical current waveform.
Meanwhile, the real motor transfer
function converts the input volt-
age waveform into the observed
(measured) current waveform. The
theoretical current waveform is sub-
tracted from the measured current
waveform to produce a residual cur-
rent waveform (Figure 2). The residual
waveform contains the errors
between theory and reality, and the
monitor uses this residual waveform
for mechanical fault analysis.
FIGURE 1: The motor as a transfer function. A voltage waveform is converted to a current waveform by the motor.
Input Voltage
OutputCurrent
Motor
FIGURE 2: A source voltage waveform passes through the real motor transfer function, producing a current waveform with harmonic distortion, IMotor. The same voltage waveform is passed through the learned model transfer function, producing a theoretical current waveform, IModel. The two waveforms are subtracted, producing a residual current waveform. The residual waveform represents the error between theory and reality.
VResidualCurrent
Motor
Learned Model
-1
IMotor
IModel
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Motor Electrical Fault DetectionChanges in the internal character-
istics of the motor (for example, a
shorted winding) will cause the real
motor transfer function to change.
While monitoring, the AnomAlert
unit takes the measured voltage and
current waveforms and calculates
a new set of observed coefficients
for the internal motor model. The
original model coefficients are
subtracted from the observed
coefficients to yield residuals.
These residuals are used to detect
internal electrical motor problems.
Mechanical Fault Detection
In an ideal motor, the rotor would
be perfectly centered in the stator
clearance, turn smoothly, and have no
unbalance. In real motors, the rotor is
never perfectly centered in the stator,
bearings and driven equipment create
disturbances, and the rotor always
has some unbalance.
Mechanical faults disturb the rotor
position and create disturbances and
distortions in the current waveforms.
As faults develop in the machine train,
they will cause the output current to
deviate further from the theoretical.
For example, an unbalanced rotor
will move in a 1X orbit that causes a
rotating rotor/stator gap change. This
change causes amplitude modulation
of the current signals and causes
sidebands to appear around the line
frequency in the spectrum. In another
example, a race fault in a rolling
element bearing will cause a periodic
disturbance in the rotor position;
this disturbance in rotor position will
create a corresponding disturbance
in rotor/stator gap and amplitude
modulation of the motor current.
The modulation produces sidebands
around the line frequency in the
residual current spectrum, and the
distance of the sidebands from the
line frequency will correspond to the
bearing defect frequency. Other kinds
of faults can produce a wide variety
of additional frequency content in
the current waveforms. AnomAlert
processing (and in general, MCSA)
looks for this additional frequency
content and uses it to diagnose differ-
ent classes of mechanical problems.
AnomAlert analysis is different
from MCSA. MCSA involves spectral
analysis of the observed current
waveform (sometimes demodulated),
while AnomAlert processing
produces a Power Spectral Density
(PSD) plot from the residual current
waveform (the difference between
the theoretical current waveform and
the measured current waveform).
The AnomAlert residual current
waveform is based on a learned
model, so the PSD is a spectrum of the
difference between theory and reality.
Thus, AnomAlert methodology first
detects change in the motor current,
and then classifies the spectral
characteristics of that change into
fault classes. The monitor classifies
PSD energy into 12 typical spectral
frequency ranges that are associated
with particular fault classes.
Line and Current Faults
During the learning period, the
monitor learns typical behavior for
that motor. Deviations of voltage or
current from normal behavior can
signal a problem. The monitor checks
for significant changes in power
factor, voltage, and current imbal-
ance. Because an increase in driven
load will cause an increase in motor
current, AnomAlert methodology uses
abnormal current as an indicator
of a load problem. For example,
decreasing flow through a fan or
blower would cause a decrease in
fan load and motor current, and this
could signal an obstruction in flow.
The Fleet Model
What happens if the monitor is
installed on a motor that has an
existing fault? Will it learn the fault
and fail to detect that something is
wrong? No. This is where the fleet
model comes in. The monitor has a
database of residual waveform signal
characteristics that are representa-
tive of a large fleet of similar motors.
This is used as a backup to guard
against missed alarms in case the
AnomAlert monitor has learned
a bad motor. When a measured
value exceeds the High value in
the database for that frequency
range (Figure 3), the monitor will
alarm assuming that the alarm
level has passed the persistence
test. We will discuss this test later.
14 ORBIT Vol .32 No.2 Apr.2012
FEATURES
-
Learning
When first installed, the monitor
learns the behavior of the motor it is
hooked up to. It spends some time
learning before starting to monitor the
motor. Some motors drive equipment
that operates at a constant speed and
load. This is the simplest operating
mode to learn and monitor because
any change in operating character-
istics is probably indicative of a fault.
Many other machine trains operate
at variable speed or variable load. In
this case, what is normal for one load
range may be abnormal for another.
In this situation, the monitor learns
and creates a separate internal motor
model for each operating mode.
Then, later, as conditions change, it
will shift from one model to the next.
The AnomAlert learning period takes
about 10 days (Figure 4), whether
the motor is fixed or variable
speed. During learning, the monitor
iterates by collecting waveforms,
performing analysis, then repeating
the process. During each 90 second
iteration, it simultaneously collects
voltage and current waveforms
for each phase, and then performs
numerical analysis of the data. During
the initial, 3 day Learn phase, the
AnomAlert unit will not monitor. It is
busy building a preliminary internal
motor model and spectral statistics.
After the initial Learn phase is
complete, the AnomAlert unit will
begin to monitor the motor. While it
does this, it will continue to improve
the model for another 7 days (the
Improve phase). For variable speed
motors, these iterations are spread
over as many operating modes as
necessary. During the Learn and
Improve phases, if motor operation
shifts from one operating mode to
another, the monitor will save the
previous data and start learning
the new operating mode. When
the motor returns to a partially
completed mode, the monitor will
continue learning from the last point.
Once the entire learning process has
been completed, the monitor stops
model refinement and continuously
monitors the motor using the
completed internal motor model
and PSD spectral characteristics.
If, after model completion, the motor
enters a new operating mode that
hasnt been seen before, the monitor
may go into alarm if the current
waveforms are significantly different
from what has been modeled. At
that time, the user can manually
direct the AnomAlert unit to learn
the new mode using the Update
command. It will then learn the new
operating mode. It will not monitor
the new mode until the update
learning process is completed.
During all learning, if either motor
power or AnomAlert power is
interrupted, the monitor will
automatically recover and continue
learning from the last point.
FIGURE 3: Residual current PSD plot showing the motor spectrum (blue) and the fleet High curve (red). If a motor frequency persistently exceeds a fleet High value, the monitor will alarm.
FIGURE 4: The AnomAlert learning period. After installation, AnomAlert spends about 10 days learning the motor behavior. It will start to monitor after the initial 3 day Learn period is complete.
Learning Period (10 days)
ImproveLearn
7 days3 days
Monitor
Apr.2012 No.2 Vol .32 ORBIT 15
FEATURES
-
Change Detection, Persistence, and AlarmingBecause of noise and small changes
in operating characteristics, there
is always some variation between
successively observed model and
spectrum parameters. During the
learning phase, the AnomAlert
monitor builds statistics that describe
the variation that occurs. When
learning is complete, the monitor has
a set of statistics for every model
coefficient (electrical faults) and
spectral band2 (mechanical faults).
The AnomAlert unit operates by
detecting differences between
observed and previously learned
parameters; either internal model
coefficients or spectral band
amplitudes. These differences must
pass a statistical test before being
considered significantly different.
These tests define minimum alarm
thresholds. Check Line alarms are
generated based on voltage imbal-
ance variations and voltage fluctua-
tions from the range encountered
during the Learn phase. A similar
alarm method is used for power
factor, total harmonic distortion,
voltage and current rms values, and
voltage and current imbalance values.
Even large deviations could be
expected to occur in a normal
machine once in a while. To guard
against false alarms, AnomAlert
processing requires that the detected
change be persistent over time.
The monitor uses a sophisticated
algorithm that compares the amount
by which a parameter exceeds the
threshold value and the number of
times this has occurred in a window
of time. This sliding window varies
depending on the amount the
measured parameter exceeds the
statistical threshold. Large threshold
exceedance will require only a short
time window, while mild exceedance
will require a long window. The moni-
tor will alarm only when the persis-
tence requirement has been satisfied.
DiagnosticsFor the most part, the AnomAlert
monitor does not provide precise
diagnoses of particular faults. Instead,
it reports categories of faults that
act as indications and point to areas
that should be further investigated.
It uses four independent fault
detection methods that cover two
categories, electrical and mechanical.
Electrical faults are associated with
either motor internal problems or
external power supply issues. The
AnomAlert unit monitors both using
two independent methods. Internal
motor faults are detected using the
learned internal motor model as a
reference. During each monitoring
iteration, the monitor calculates a set
of 8 internal motor model parameters
based on the observed voltage and
current. These observed parameters
are compared against the param-
eters that were obtained during the
learning phase, and significant and
persistent changes are detected and
reported as electrical faults. These
faults include the following examples:
Loose windings
Stator problem
Short circuit
External supply is directly checked
for voltage or current imbalance,
voltage range, maximum current,
and low voltage or current.
Mechanical fault categories are
detected and diagnosed using the
PSD of the residual current waveform.
The residual current represents the
difference between the observed
current and the theoretical current
produced by the internal motor
model using the same observed
voltage. The PSD is divided into 12
frequency ranges that are typically
associated with certain mechanical
problems (listed below). Analysis of
these frequency ranges produces
fault classes for further investigation.
Loose Foundation/Components
Unbalance/Misalignment/
Coupling/Bearing
Belt/Transmission Element/
Driven Equipment
Bearing
Rotor
Note that the Check Load alarm,
caused by abnormally high or low
current, is usually caused by a
change in the driven machines load;
machine load can change for two
reasons, fault or process change. If
the machine is running in a different
condition which is not seen during
the learn period, the user has to set
16 ORBIT Vol .32 No.2 Apr.2012
FEATURES
-
the AnomAlert unit to update mode
to learn this new condition. If the
load is changed due to a fault, the
problem should be investigated,
and the user needs to make sure
the alarm is cleared in the monitor.
The Fleet Model provides an
independent analysis in the event
that the AnomAlert unit has learned
a faulty system. The Fleet Model
consists of Normal and High values
for each of the 12 PSD ranges based
on experience with a large number
of similar motors. If a residual
current PSD range value exceeds
the fleet High value, then, after
persistence checking, the monitor
will warn that something is wrong.
LimitationsThe AnomAlert Motor Anomaly
Detector is a powerful motor
monitoring system. However,
there are some limitations on
its use and interpretation.
It cannot be used for DC or
single-phase motors.
For variable frequency drives,
the inverter chopping frequency
should be higher than 2 kHz.
Mechanical diagnostics are based
on energy in 12 spectral frequency
ranges. This is, by nature, an approxi-
mate analysis, and diagnostic indica-
tions usually only represent broad
classes of problems. The customer will
have to follow up using other methods
to determine the actual fault. The PSD
spectrum produced by the AnomAlert
unit can be helpful, but may not be
sufficient for problem identification.
The AnomAlert unit cannot be
used on motors that have rapidly
varying voltage or power. Voltage,
frequency and current amplitude
must not change by more than 15%
in six seconds. This is not a serious
restriction for most applications, but
some applications, like crushers, will
not fit this requirement. Note that
if a sudden change of load occurs,
the monitor will reject that sample;
however, the same machine could run
steadily at some load, and this would
allow the unit to monitor the machine.
The AnomAlert unit will work very
well on applications where the
motor is located some distance
from the current or potential
transformers. However, the line at
the current measurement point
must be dedicated to a single motor;
multiple motors downstream from
a single CT cannot be monitored. On
the other hand, one set of PTs can be
used for all motors that are supplied
from the same voltage source. The
current measurement restriction is a
consideration for subsea applications
where power may be delivered to the
sea floor only to branch off to multiple
motors. In this case, an AnomAlert
unit could not be used on the main
delivery power line. However, it could
be used if CTs could be installed on
each branch (CT burden limits apply3).
Summary
The AnomAlert Motor Anomaly
Detector is a powerful motor monitor-
ing system. Its power comes from
both sophisticated signal processing
and analysis algorithms and from
built-in redundancy. Its ability to learn
makes it sensitive and flexible, and
a fleet reference database protects
against missed alarms caused by
learning an already defective motor.
Alarming is clever and uses statistical
analysis combined with an adaptive
persistence test. These features
produce a product that is a significant
improvement over conventional Motor
Current Signature Analysis, and it has
a proven track record documented
by many case histories.
* Denotes a trademark of Bently Nevada, Inc., a wholly owned subsidiary of General Electric Company.
Copyright 2012 General Electric Company. All rights reserved.
1 For sensor selection and installation, see Bently Nevada Guide 286752, Selection of CTs, CSs, and PTs for AnomAlert. For general ordering information, see 286754-01, Specifications and Ordering Information.
2 Note that the AnomAlert monitor identifies the largest amplitude spectral line in a particular frequency range and uses that lines amplitude for the value in that range. It does not add up all the spectral energy in a range.
3 The burden of a current transformer is the maximum resistance that the secondary of the CT (the part hooked up to the AnomAlert monitor) can drive and meet specification. Long wires from the CT will have more resistance that will limit the allowable distance from the CT to the monitor. See Bently Nevada Guide 286752, Selection of CTs, CSs, and PTs for AnomAlert.
Apr.2012 No.2 Vol .32 ORBIT 1
FEATURES
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John W. Kingham
Field Application Engineer
How to Display Direct and Filtered Orbits Together, Synchronized by Sample
When I used to be a Machinery Diagnostic Services (MDS) engineer, and travelled the world to diagnose machinery problems,
I had several plot formats that I used all of the time. One of
my favorites was the Orbit plot. Ive always described the
orbit plot as what the shaft would draw if there were a
pencil lead at its centerline, and you held a piece of paper
up to it . Seeing what the shaft is doing graphically allows
you to interpret what it is doing mechanically, and from
there a diagnosis may be made.
Typically, most people look at orbit plots for the unfiltered,
or direct data and the 1X filtered data. I particularly like to
look at these two orbits together on the same page. A quick
glance at the unfiltered orbit can show problems such as
glitch (electrical & mechanical runout noise), unbalance,
misalignment, oil whirl (instabilities), looseness and rubs.
The 1X filtered plot gives you some insight into these
malfunctions as well, and is especially good for observing
shaft precession.
The plots in Figure 1 are of a steam turbine exciting its first
natural rotor vibration frequency due to a rub. The plots
have been scaled using the Auto, All Plots function, which
allows you to see at a quick glance that there is significant
NOT 1X activity.
18 ORBIT Vol .32 No.2 Apr.2012
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FIGURE 1: Orbit and timebase waveform plots for vibration of a steam turbine rotor within the clearances of its fluid film bearings. The upper plots show Direct (unfiltered) data, while the lower plots show 1X-filtered data.
While we are on the subject, it is easy to determine that
the frequency is locked in to X by looking closely at
the signal from the X (horizontal) probe. First, notice that
there is only 1 positive peak seen for every 2 Keyphasor*
marks. Second, to determine it is exactly 1/2X, note that
you can draw two straight lines through the Keyphasor
marks as shown by the red dashed lines (Figure 2).
FIGURE 2: Magnified view of the unfiltered timebase waveform
signal from the X (horizontal) probe.
Overview
For those who are fairly experienced in configuring
ADRE plot sessions, this brief overview summarizes the
process. A more complete description with step by
step instructions is included after this summary.
Create your Orbit/Timebase plot group.
Make sure that you are using the
Synchronous waveform.
Copy and paste the new plot group below the original,
and reconfigure it for 1X (or 2X or nX). While you are
configuring, make sure that the Use Static Samples
for Filtered Waveforms check box is cleared.
Drag the 1X plots up into the original plot group.
Do this at the New Orbit/Timebase Plot level,
not at the variable (waveform) level.
Once the plots are organized correctly, delete
the 1X plot group it is no longer needed.
If you are using a 2x1 plot layout with paging by
sample, the direct orbit will be on top with the filtered
orbit on the bottom. If you have a 2x2 plot layout,
the filtered orbits will be on the right hand side. This
can be changed by changing the order (dragging
and dropping) of the plots at the plot group level.
For steady-state machine conditions, it is recommended
that you set scaling to auto all plots. This way, it is
easy to see which bearing is giving you a problem. If
you are observing a startup or shutdown transient,
setting manual scaling may be better, as the auto all
plots will scale for the amplitude extremes (such as
when the machine passes through a resonance).
Detailed Description
1. CREATE A BASIC ORBIT TIMEBASE PLOT
Select the channel(s) that you want to show orbit
plots for from the Configuration hierarchy on the
left side of the screen. It is only necessary to select
one channel from each channel pair (Figure 3):
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FIGURE 3: In this example, two channels have been selected for
display. We will be viewing data for bearings 1 and 2.
Right-click on the selected channel(s) and select the
Orbit/Timebase plot from the shortcut menu (Figure 4):
FIGURE 4: Selecting Orbit/Timebase plot option.
When you do this, the Orbit/Timebase plot
will open. Close the plot for now, since we
will not be looking at it for a while.
Direct Orbit/Timebase plots are typically made from
the Synchronous waveform sample. The 1X filtered
and Slow Roll Compensated data is always taken
from the Synchronous waveform sample. Therefore,
we need to make sure that the Orbit/Timebase plot is
configured to use the Synchronous waveform data.
Right-click on the new plot group in the plot hierarchy
and select Configure: from the menu (Figure 5). This
will open the plot group configuration dialog.
FIGURE 5: Opening the plot group configuration dialog.
In the Orbit Timebase plot group configuration dialog,
first verify that the Sync Waveform variables are
selected for each channel. If they are not, select the
correct variables from the drop-down boxes (Figure 6):
FIGURE 6: Checking which variables are selected.
Click the arrow button to expand the drop-down
list. Select Sync Waveform for display (Figure 7).
FIGURE 7: Ensure the Sync Waveform is selected.
Repeat the process for the channel pair variable (Figure 8):
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FIGURE 8: Selecting Sync Waveform for the paired variables to be displayed.
2. DUPLICATE THE PLOT
In the Plot Session hierarchy (right hand tree), make
a copy of the plot group that you just created, and
place it just below the original plot group. There are
several ways to accomplish this. The easiest way is to
right-click on the plot group and select Copy (Figure
9), then Paste it into the plot session (Figure 10).
FIGURE 9: Copying the new plot group.
FIGURE 10: Pasting the copied plot group into the plot session.
The result should look something like the example in
Figure 11.
FIGURE 11: Observe that the selected variables are now shown underneath the associated Orbit/Timebase plot groups.
3. CONFIGURE THE FILTERED ORBIT
For clarity, I renamed the bottom Orbit plot group 1X
Orbit/Timebase Plot Group. You can too, but it isnt neces-
sary (Right click on the plot group and select Rename).
When we are done, we will delete this plot group, and
rename the reconfigured plot group Direct & 1X Orbit/
Timebase Plot Group again, this will be optional, but it
is a nice thing to do if you are going to use this plot group
as a template. I do this as a matter of bookkeeping. If I
didnt delete the plot group, at the end of the day, Id have
two plot groups the one that I want, with both direct and
1X data and also a plot group that only has 1X plots.
To establish the plots to be 1X filtered, right-click on the
appropriate plot group and select Configure. In the top
half of the configuration grid (Figure 12), select 1X from
the drop down menu under the Filtering column. Select
this for all channels that you want filtered orbits for.
FIGURE 12: Selecting 1X filtering option from plot General properties.
At this same time, make sure that the check boxes
under Use Static Samples for Filtered Waveforms
are cleared (Figure 13). If this isnt done, the filtered
and unfiltered samples will be indexed differently and
become unsynchronized, which would be confusing.
Apr.2012 No.2 Vol .32 ORBIT 21
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FIGURE 13: Clear the Use Static Samples check boxes.
Now expand the 1X Orbit/Timebase Plot Group by
right clicking and choosing Expand All (Figure 14).
FIGURE 14: Example of expanded plot groups.
Drag the first Orbit/Timebase Plot from the 1X plot group
(Figure 15) up into the top plot group. Do this by clicking
on the highlighted plot, and dragging it with your mouse
up to just below the first Orbit/Timebase plot in the top
group of the plot session. As you are completing this, your
result will look something like the example in Figure 16.
FIGURE 15: Selecting the first Orbit/Timebase Plot for dragging.
Observe that as you drag the plot up, the pointer
will change from a circle with a slash through it to a
horizontal straight line. When the insertion point it is
where you want it to be in the hierarchy, drop it in.
FIGURE 16: The first Orbit/Timebase plot will be dropped at the horizontal line insertion point.
After dropping the plot at the insertion point, your
result should be similar to the example in Figure 17:
FIGURE 17: Example of Plot Session Manager Hierarchy after the plot has been dragged to the insertion point.
Repeat this procedure with the remaining channels,
to drag their associated plots up into the appropriate
plot group. My results are shown in Figure 18.
FIGURE 18: Example of Plot Session Manager Hierarchy after all required plots have been dragged up into the appropriate plot group.
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Just to confirm you have put the plots where you want
them, use the Expand All command on the top Orbit/
Timebase plot group in the hierarchy (Figure 19):
FIGURE 19: Example showing that all four needed plot sessions (outlined in red) have been dragged into the top plot group in the Plot Session Hierarchy.
Since this looks good, we can go ahead and delete
the unneeded 1X Orbit/Timebase Plot Group (Right
click on the group and select Delete (Figure 20).
Note: The reason we dragged these new plots into
the new plots is to enact plot overlays in ADRE. We
started with two distinctly separate plot groups one
is for Direct data, and one is for 1X data. By drag-
ging and dropping the 1X plot into the Direct plot,
we can see both sets of data in the same plot.
FIGURE 20: Selecting the unneeded 1X Orbit/Timebase Plot Group for deletion.
This step is optional, but helps to keep things clearly
labeled: Rename the New Orbit/Timebase Plot Group to
Direct and 1X Orbit/Timebase Plot Group (Figure 21).
FIGURE 21: In this example, we renamed the new plot group (in red outline box) with a descriptive name.
IVE ALWAYS DESCRIBED THE ORBIT PLOT AS WHAT THE SHAFT WOULD
DRAW IF THERE WERE A PENCIL LEAD AT ITS CENTERLINE, AND YOU
HELD A PIECE OF PAPER UP TO IT. SEEING WHAT THE SHAFT IS DOING
GRAPHICALLY ALLOWS YOU TO INTERPRET WHAT IT IS DOING
MECHANICALLY, AND FROM THERE A DIAGNOSIS MAY BE MADE.
Apr.2012 No.2 Vol .32 ORBIT 23
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4. HOUSEKEEPING, OR MAKING IT LOOK GOOD
In the plot group configuration (bottom part of
configuration window), there are a few things that we
can do to make the plots tidy, and simpler to navigate:
Change the Paging Mode to By Sample (Figure 22).
If you leave it set for paging mode By Channel, your
plots will only show data from one channel you will
scroll through all of the data for that channel, and
then you will scroll through all of the data for the next
channel, and so forth. Typically, I want to see what is
happening on all of the channels at the same instant
in time. This is much more meaningful to me.
FIGURE 22: Selecting By Sample from the drop-down list.
Change the plot layout to display the plots
appropriately. Either show 2 plots per page
(Figure 23) or 4 plots per page (Figure 24):
FIGURE 23: The 2 x 1 option will show two plots per page.
FIGURE 24: The 2 x 2 option will show four plots per page.
Click OK on the plot configuration to close it when you
are finished.
5. OPEN THE PLOT
If you used a plot layout of two plots per page, the 1X
Filtered plot will be located below the unfiltered (direct)
plot (Figure 25):
FIGURE 25: Example with two plots per page.
If you used a layout with four plots per page mode, they
are displayed in a two-by-two arrangement (Figure 26).
FIGURE 26: Example with four plots per page.
Re-ordering the plots in the plot tree will change
the arrangement of the four plots on the page:
For instance, by changing the order, you could
make the top plots show unfiltered data, with the
bottom two plots showing 1X filtered data.
For analyzing data that was collected from steady state
(constant speed) conditions, it is recommended that you
set scaling to auto all plots. This way, it is easy to see
which bearing is giving you a problem. If you are observing
a startup or shutdown, setting manual scaling may be
better, as the auto all plots will scale for the extremes
(such as when the machine passes through a resonance).
Hopefully, this tip will make you more productive and help
you diagnose machinery problems a little more easily.
See you the next time the Keyphasor* comes around!
* denotes a trademark of Bently Nevada, Inc., a wholly owned subsidiary of General Electric Company. Copyright 2012 General Electric Company. All rights reserved.
24 ORBIT Vol .32 No.2 Apr.2012
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[This is the first installment in a continuing series of Application Notes on important topics regarding the effective use of Bently Nevada* products. Watch for additional Application Notes topics in future issuesEditor]
Resources for Managing Electrical Runout
Electrical Runout is the term used to describe the unwanted signal from an eddy current probe due to variations in material properties. Many machines are required to meet specifications limiting the baseline vibration. Electrical runout can cause problems with acceptance of new
machinery, or with diagnostics of machinery with low levels of vibration.
The first step in obtaining an accurate measurement is ensuring that the eddy current probe is installed
optimally. Once there is confidence in the probe installation, we are ready to evaluate the causes and take appropriate corrective actions for electrical runout problems.
The Orbit article at the following link was written by Nate Littrell. It includes useful guidance for probe installation, as well as for evaluating the causes of electrical runout, and planning effective corrective actions:
http://www.ge-mcs.com/download/orbit-archives/2001-2005/3q2005_runout.pdf
* Denotes a trademark of Bently Nevada, Inc., a wholly owned subsidiary of General Electric Company. Copyright 2012 General Electric Company. All rights reserved.
DEPARTMENTS
APPLICATION NOTE
Apr.2012 No.2 Vol .32 ORBIT 25
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Vibration Data Identifies Hot Spot on Motor Rotor
Roengchai Chumai
Technical Leader
Bently Nevada Machinery
Diagnostics Services
Executive Summary
This case history describes how vibration analysis
identified a thermally-sensitive rotor in an induction
motor driving a condensate pump at a newly com-
missioned power plant in Thailand. Induction motors
have characteristic vibration behavior based on the
electrical, magnetic and mechanical effects that they
experience. In order to capture the significant data for
analysis, it is important to perform test procedures
and data collection very carefully. In some situations,
problems with machine casings, mounting foundations
and associated structures and even the configuration
of the driven machine can influence motor vibration
behavior. All of these factors should be taken in account.
In this particular case, the analysis was concerned
with increasing vibration amplitude that occurred
when the pump was running at loaded conditions.
The phase angle of 1X filtered vibration kept changing
over the running period and a thermal vector was
identified. It was suspected that a hot spot was
causing the rotor to bow, so a repeatability test was
performed to check for consistency of its location.
The motor was run uncoupled from its pump to
reduce load and therefore operating temperature of
the rotor. Testing verified that the rotor did indeed
straighten out when it was run solo, which indicated
that the thermally-induced bow had gone away, and
with it, the previously-observed high 1X vibration.
Shop inspection showed clear evidence of burnt rotor
insulation resin on the rotor surface, which verified the
location of the hot spot that caused the thermal bow.
Background and Sequence of EventsOne block of the newly-commissioned combined-cycle
power plant includes three vertical motor-driven
condensate pumps. The drive motors are of 4-pole
induction design, with synchronous speed of 1500
Hz (50 Hz power supply). The drive motors operate at
constant speed, and condensate flow is controlled
26 ORBIT Vol .32 No.2 Apr.2012
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by throttle valves. The multistage centrifugal
water pumps are of typical canned design.
Motor Nameplate Data Power: 250 hp (186 kW)
Speed: 1485 rpm
Power Supply: 380 vac, 3 phase, 50 Hz
Current: 347 amp
Service Factor 1.15
Time Rating: Continuous
Power Factor: 0.855
During initial plant startup activities, all three
condensate pumps exhibited high vibration at the
motor Non-Drive End (NDE). Since the steam plant
could not be started without all three of these pumps
running, the project construction contractor called
Bently Nevada* Machinery Diagnostic Services
(MDS) to assist with vibration testing and analysis
to determine the root cause and to provide on-site
advisory for resolution of the high vibration conditions.
The MDS Engineer arrived at the plant site and
discussed the history of the problem with the customer.
He set up portable data acquisition instruments
and temporarily-installed vibration transducers for
individual testing of all three condensate pumps.
InstrumentationAn orthogonal (perpendicular) pair of radial velocity
transducers was installed at both the Non-Drive End
(NDE) and the Drive End (DE) of each motor while it
was being tested. The temporary installation also
included an optical Keyphasor* sensor for providing
reference phase angle and supplementary machine
speed measurement (Figure 1). All vibration signals
were sent to an ADRE* Data Acquisition Interface
Unit (DAIU) using coaxial cables. A laptop computer
running ADRE for Windows software was connected
to the DAIU for capturing, digitizing and presenting
vibration data in a variety of plot formats for analysis.
BANGKOK SPARKLES LIKE A JEWEL AT NIGHT, SYMBOLIZING THE IMPORTANCE OF ELECTRICAL POWER TO THAILANDS ECONOMY.
Apr.2012 No.2 Vol .32 ORBIT 2
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FIGURE 1: An orthogonal pair of velocity sensors is temporarily installed at the DE and NDE of one of the condensate pump drive motors. An optical sensor (not visible in this photo) was also installed for direct observation of a one event per turn feature on the rotating shaft which was provided by a strip of reflective tape.
FIGURE 2: Trim balance correction weight installed at motor rotor NDE location. The motor dust cover has been removed to reveal the weight plane. Observe that one bolt of appropriate mass has been threaded into the required location to provide trim balance.
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Initial Investigation & Actions
Based on available vibration data, it was discovered
that all three of the pumps (units A, B & C) were
experiencing higher than acceptable vibration due
to unbalance. The MDS Engineer trim balanced the
pumps to acceptable vibration levels based on ISO
10816-3 Standard, by adding appropriate correction
weights at the NDE of each motor rotor (Figure 2).
Additional Pump A TestingAfter trim balancing, Pumps B & C behaved normally.
However Pump A exhibited an unusual change in the
vibration amplitude and phase over the observed
running period (Figures 3 and 4). These trend plots show
the change of vibration amplitude over a time period
of just over 4 hours at constant speed and load.
This kind of behavior often indicates a thermally-sensitive
rotor that bows during operation due to a hot spot caused
by a local fault in the rotor. With such a fault, the amount
of heating and thermal bow typically depends on motor
load, and the associated current flow in the rotor iron.
With the Pump A motor coupled with its pump, plant
personnel performed alignment checks and then started
the pump and ran it at a steady state operating condition.
Vibration data was captured throughout the running
period. Motor soft foot and pump baseplate rocking
effects were checked using phase angle relationships
of the vibration timebase waveforms. These evaluations
verified there was no sign of these possible problems.
FIGURE 3: First test run: Four hour trend plot of vibration phase (upper plot) and amplitude (lower plot) from the 1XV sensor (oriented to the 0 degree North reference) at the NDE location of the Pump A drive motor. Sensor names in these vibration plots correspond to labeling in the photo of Figure 1.
FIGURE 4: First test run: Four hour trend of vibration phase and amplitude from the 1YV sensor (oriented 90 degrees to the right of the North reference when viewed from the driver toward the driven load.
Apr.2012 No.2 Vol .32 ORBIT 2
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FIGURE 5: Second test run: Four hour trend of vibration phase and amplitude from the 1XV sensor. Results are consistent with the first test run.
FIGURE 6: Second test run: Four hour trend of vibration phase and amplitude from the 1YV sensor. Results are consistent with the first test run.
FIGURE 7: First test run shows a 1X vibration vector that changed significantly in both amplitude and phase lag angle over the period of the test.
FIGURE 9: Vibration data from the 1XV sensor for a 2-hour solo run. As the rotor cooled, the Direct and 1X vibration levels dropped significantly.
FIGURE 8: Second test run shows almost exactly the same results as the first run.
FIGURE 10: Vibration data from the 1YV sensor for a 2-hour solo run. As the rotor cooled, the Direct and 1X vibration levels dropped significantly.
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Testing Under Load
In order to check for repeatability of the temperature
dependent vibration effects, a second test was run
the following day, after shutting down the motor and
allowing it to cool. Similar results were obtained (Figures
5 and 6). This validation meant that the location of the
suspected rotor hot spot was indeed at a fixed position.
The vibration data for these two test runs was also
plotted in a polar format. Again, the results of the
two runs were consistent, and the effects of the
thermal vector are quite apparent (Figures 7 & 8).
No-Load Solo Testing
Immediately after the pump was shut down after the
second series of loaded testing, the motor was uncoupled
and run solo. Since the rotor was still hot and thermally
bowed, the vibration amplitude was relatively high at
the beginning of the test. However, as shown in Figures
9 and 10, vibration amplitude dropped as the rotor
cooled and straightened out during the uncoupled run.
Note: The observed cyclic variation in vibration amplitude can be a classic symptom of broken or cracked rotor bars, or high-resistance joints between the bars and the rotor end rings. In electrical diagnostics
FIGURE 11: Polar plots show the 1X vibration vector for the motor NDE and DE during its solo run. This data shows that rotor was bowed when hot, as it had high 1X vibration amplitudes with the same phase angle at both ends of the machine. As the amplitude dropped, the data points can be seen moving closer to the center (zero amplitude) of the plot. Finally, the classic phase shift can be seen as the motor is tripped and coasts down (as indicated by rpm labels).
(not performed in this particular case), the motor current often shows corresponding fluctuations, that correspond to the pole-pass frequency of the motor. In fact, this slow modulation of motor vibration often produces an audible low-frequency beating sound. We will look more closely at this symptom near the end of this article.
Polar plots of 1X vibration amplitude and phase (Figure 11)
verifies that the rotor was straightening out as it cooled,
causing reductions in amplitude and changes in phase.
RecommendationsBased on review of the vibration data, the fol-
lowing recommendations were made:
The rotor iron should be inspected for any evidence
of lamination smear region, which would indicate
local heating and generate a hot spot on the rotor.
Check for proper function of the rotor cool-
ing air system. It is possible that non-uniform
airflow or a plugged flow path within the motor
can contribute to abnormal rotor heating.
Closely monitor the vibration amplitude of the subject
unit to ensure that it does not increase with time. The
existing rotor should be replaced for a permanent repair.
Inspection ResultsThe rotor inspection showed clear evidence of local
overheating, as indicated by a small discolored spot
where rotor insulating resin had seeped to the surface
of the iron laminations and charred (Figure 12).
THE ENTIRE PROJECT COST WAS
APPROXIMATELY 250K US DOLLARS,
SO THE NEW ONLINE SYSTEM
MORE THAN PAID FOR ITSELF
IMMEDIATELY AFTER INSTALLATION."
Apr.2012 No.2 Vol .32 ORBIT 31
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A hot spot in the laminated core of the
Pump A induction motor rotor caused
uneven thermal expansion along the
rotor (more expansion on the side of
the rotor with the hot spot, and less
expansion on the undamaged side of
the rotor). This thermal bow resulted in
increasing synchronous (1X) vibration
amplitude under loaded conditions,
with in-phase vibration measure-
ments at both ends of the rotor. Since
thermal bowing is related to rotor
current, the motor was also tested
under unloaded solo conditions to
check whether the bow would relax as
the rotor cooled from hot conditions,
and straightened out. This effect was
observed, validating the diagnosis.
Pump A Corrective Actions
The customer ordered a new rotor
to replace the damaged rotor.
However, the lead time for a new
rotor was approximately 3 months.
So in the interim, the existing rotor
was rebuilt for temporary use during
the plant commissioning period.
After rebuilding, it was balanced at
the repair shop and then returned to
the generating site for installation.
Post-Repair Symptoms
The Bently Nevada MDS Engineer
was requested to visit the site again
when the Pump A motor (now with
rebuilt rotor installed) was coupled
to its condensate pump. Vibration
testing was performed following
FIGURE 12: The local high temperature at the hot spot showed up as a heat-damaged area on the surface of the rotor.
CONCLUSIONS
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BACK-TO-BASICS SIDEBAR
Motor Synchronous Speed120f = np, where
f is the frequency of the power supply
n is the speed of the machine in rpm (or cpm)
p is the number of poles
n = 120f/p = 120 (50)/4 = 1500 rpm (or cpm)
So with a power supply frequency of 50 Hz, a 4-pole
induction motor has a synchronous (no-slip) running
speed of exactly 1500 rpm.
Synchronous Vibration Frequency1500 cpm / (60 Hz/cpm) = 25 Hz.
So with no slip, synchronous vibration frequency at
1500 rpm is 25 Hz.
Slip FrequencySince the motor was actually running at 1494 rpm,
the slip frequency was:
1500 cpm 1494 cpm = 6 cpm.
Converting to Hz6 cpm / (60 cpm/Hz) gives a slip frequency of 0.1 Hz.
Pole Pass Frequency (slip frequency x number of poles):
(0.1 Hz)(4 poles) = 0.4 Hz.
Actual 1X Frequency for Running SpeedWith the motor running at 1494 rpm, actual 1X
frequency is found by converting to Hz:
1494 cpm /(60 Hz/cpm) = 24.9 Hz.
SummaryThe vibration spectrum example in Figure 13 shows
a 1X peak at 24.9 Hz, with small sideband peaks at
0.4 Hz.
the same procedures that were used earlier. This time,
no signs of thermal sensitivity were observed.
However, high synchronous vibration was observed
at the motor NDE, with a small amount of amplitude
modulation at pole passing frequency (slip frequency
times number of poles). This indicated that there was
significant mechanical unbalance, with a small amount of
modulation caused by rotor bars that were still cracked
or broken or high resistance joints that still existed
between rotor bars and shorting rings at the rotor ends.
The vibration spectrum in Figure 13 shows a peak at 24.9
Hz center frequency, which corresponds to synchronous
(1X) vibration for the running speed of 1494 rpm.
Sidebands are seen at about 0.4 Hz, which corresponded
to the pole pass frequency (see sidebar for calculations).
Since vibration amplitudes at the sideband frequency
components were relatively low compared with
synchronous vibration amplitude, it was determined
that no immediate electrical work was required on
the rebuilt rotor. The unit was trim balanced at solo
run (uncoupled) to reduce synchronous vibration
amplitude down to acceptable levels, then the motor
was re-coupled to the pump. The unit was returned to
service for normal operation until the new rotor could
be delivered to site for permanent replacement.
* denotes a trademark of Bently Nevada, Inc., a wholly-owned subsidiary of General Electric Company. Copyright 2012 General Electric Company. All rights reserved.
FIGURE 13: Vibration spectrum measured at motor NDE of the rebuilt rotor showing predominant frequency at synchronous (1X) with sideband components at motor pole pass frequency.
Apr.2012 No.2 Vol .32 ORBIT 33
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[This is the first installment in a mini-series of Recip Tip articles that is planned by our experienced Italian Field Application Engineer (FAE), Gaia Rossi. Editor]
Vibration Analysis for Reciprocating Compressors (Part 1)
Gaia Rossi
Bently Nevada Field
Application Engineer
Vibration analysis of reciprocating machines creates some unique challenges. This article explains the reasons and gives clarity on recommended monitoring and analysis practices and tools. Years of field experience have demonstrated that techniques which may be well understood for measuring and analyzing the vibration of purely rotating machinery can produce confusing results when applied to reciprocating machinery.
Vibration associated with rotational speed is the dominant motion for most industrial rotating machines. This synchronous (1X) behavior allows the direct application of traditional vibration analysis concepts towards addressing common machinery malfunctions such as rotor unbalance. The typical frequencies
observed with those common rotor-related malfunctions generally occur between a quarter of running speed and twice running speed and correlate excellently with machine mechanical conditions. Consequently, principles and diagnostic methodologies for these machines are broadly accepted and harmonized within the machinery diagnostic community.
This is not quite true for reciprocating compressors. Vibration analysis of these machines creates some unique challenges; many forcing functions produce a complex vibration signature that makes any attempt of using standard analysis techniques used for rotating equipment ineffective.
34 ORBIT Vol .32 No.2 Apr.2012
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FIGURE 1: This drawing shows typical vibration monitoring locations for a reciprocating compressor. Sensors are installed at the crosshead guides (4 red hexagons) and on the frame (4 blue diamonds). [Reference 1]
Apr.2012 No.2 Vol .32 ORBIT 35
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Compressor Frame Vibration
Vibration measured at the frame
results principally from the response
of the mechanical system to the
forces and moments that are
occurring in the machine at the
normal running conditions. These
include the following factors:
Gas Load Forces: These forces act
on the piston and stationary compo-
nents at 1X and at integer multiples
of running speed. They are generally
significant up to about 10X and in the
direction of the piston rod travel. For
large slow speed compressors (up to
roughly 500 rpm), gas forces are typi-
cally the largest contributor to piston
rod and compressor frame load.
Inertial Load Forces: These forces
are caused by the acceleration
of the reciprocating components
(piston, piston rod, and crosshead).
These components represent
a large amount of mass to be
accelerated back and forth with
each stroke. Inertial loads of
400,000 Newton (~90,000 pounds)
of force or more are not uncommon
with very large compressors.
Reciprocating & Rotating Masses
Unbalance Forces: These forces
are predominant at 1X and 2X
compressor speed, and are caused
by asymmetrical crankshaft design
and imperfect manufacturing toler-
ances. They are usually much smaller
than inertial and gas load forces.
FIGURE 2: Time waveform plot of the velocity signal from a frame-mounted vibration sensor. Observe that many different frequency components are present in the signal.
FIGURE 3: Frequency domain (spectrum) plot of velocity signal shown in Figure 2. Fast Fourier Transform (FFT) processing allows us to see the various frequency components that are included in the complex waveform.
Gas Unbalance Forces: These are
caused by pressure in the pulsation
bottles and pulsation at the cylinder
nozzle area and on piping. Allowable
pulsation levels are defined in API-618.
Although these pulsating forces are
usually much smaller than the forces
listed above, they can be destructive
to piping and piping support systems
if they happen to correspond to reso-
nant frequencies for the structures.
As a consequence of these factors,
the extent of vibration is inherent
with the reciprocating compressor
design and its response to all the
applied forces and moments. This
causes these machines, even when
in good condition, to vibrate much
more than a comparable rotating
machine. The examples in Figures
2 and 3 show that many harmonics
are produced by the complex shape
of the frame velocity waveform.36 ORBIT Vol .32 No.2 Apr.2012
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Frame vibration frequencies typically
include components below 10 Hz.
For this reason, a velocity transducer
(with extended low frequency
response) is usually better suited than
an accelerometer for detecting an
increase of rotation-related forces
(due to gas load or inertial loads,
imbalance, foundation looseness,
excessive rod load, etc.). The preferred
location for the frame vibration
transducer is on the side of the frame
oriented in the direction of piston
rod travel, on the centerline of the
crankshaft and at a main bearing
where dynamic load is transmitted
(Figure 1). Magnitude for a filtered
frame velocity signal is usually low
(less than 7 mm/s); however, at low
frequencies, even small amplitudes of
measured velocity may correspond
to large amounts of displacement.
On the other hand, measuring only
frame vibration can be insufficient
for effective condition monitoring,
as the increase in frame velocity
from incipient failures developing
at the running gear or cylinder
assembly will be small and typically
covered by the larger signal that
is produced by normal machine
movement. Experience has shown
that by the time the malfunction has
been detected by the frame velocity
transducer and the compressor shut
down, major secondary damage may
have already occurred because of
the malfunctions. These malfunctions
include liquid or debris carryover,
loose piston or piston nut, loose
crosshead nut, or loose cylinder liner,
and typically manifest themselves as
impacts transmitted at the crosshead.
Monitoring Vibration & Impact
Vibration transducers monitoring
rotating machinery generate station-
ary signals; this means they have
constant frequency content over each
revolution of the rotor (Figure 4).
In contrast, vibration measurements
on reciprocating compressors present
both stationary and non-stationary
content. In particular, the signal gen-
erated by an accelerometer placed
vertically on a crosshead guide is
characterized by different frequencies
with different amplitudes that occur
at specific points in the revolution.
Figure 5 shows a typical waveform
from a crosshead accelerometer.
The signal shows high amplitude,
FIGURE 4: Example of stationary vibration sample taken at an electric motor bearing. The higher frequency components are typical of the characteristic vibration produced by the interaction of the rolling elements with the bearing races.
FIGURE 5: Timebase waveform of a crosshead acceleration signal.
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short duration impulse peaks fol-
lowed by a ring down that occur
at certain parts of each crankshaft
revolution. This signal is not filtered
so the transducer is picking up
the widest range of frequencies
(typically from 10 Hz to 30 kHz).
These acceleration peaks can be
referred as responses to impulse
events occurring during compressor
operation (valve opening and closing,
gas flow turbulence, crosshead
pin shifting at load reversal, etc.).
Such impulses excite the structural
resonances of the machine compo-
nents - resulting in high frequency
free vibration and the characteristic
impact/ring-down profile.
As mentioned, the main source of
vibration on the compressor frame
is related to periodic forces. While
the overall frame vibration increase
is certainly a concern, the primary
interest of crosshead vibration
monitoring is detecting peaks
associated with structure response
to impulsive events. Conditions
that increase the excitation of such
resonances are generated by develop-
ing faults such as fractured or loose
components or excess clearance.
Loose rod nuts, loose bolts, excessive
crosshead slipper clearance, worn
pins as well as liquid in the process
can be detected at early stages of
development using crosshead impact
monitoring, thus allowing appropriate
countermeasures and avoiding
potential catastrophic consequences.
Of all vibration measurements that
can be applied to reciprocating
compressors, crosshead accelera-
tion is probably the most effective
protection measurement available,
if appropriately employed.
While crosshead acceleration has
proven itself to be a sound measure-
ment for detecting mechanical
failures, industry has little experience
in applying and analyzing it, resulting
in increased risks of false or missed
alarms, and poor diagnostic value
from diagnostic systems. The follow-
ing paragraphs describe some basic
requirements for a reliable monitoring
system and diagnostic software.
Requirements for Monitoring Systems
General considerations on the
effective employment of crosshead
acceleration for monitoring and
protection are described here:
Transducer SelectionAmplitude measurement units should
be generally selected based upon the
frequencies of interest. For crosshead
vibration monitoring an accelerometer
should be selected as it emphasizes
the higher frequency components.
The unit of measurement used should
be the natural units of the transducer
used (signal integration is not a
recommended tool for this purpose).
Transducer MountingFrequency response is sensitive
to mounting techniques and may
be affected by any reduction of
the mechanical coupling between
accelerometer and mounting surface
such as the use of an adhesive,
magnetic isolation base, or non-flat
mounting surface. The transducer
should be installed directly on the
machine structural component to
be measured, avoiding brackets or
plates as a support, or mounting on
flanges or covers. Accuracy of an
accelerometer can also be affected
by ground loops, base strains, and
cable noise. These can be minimized
by following the recommendations
from transducers and monitoring
systems manufacturers as well as
applying appropriate cable tie-downs.
Signal Processing & Alarming
One of the concerns in applying
crosshead vibration measurement
for compressor shutdown is the risk
of false alarms due to spurious peaks
in the signal. The peak detection
circuit in the protection system should
be designed to manage impulsive
vibration in order to avoid nuisance
alarms; this can be accomplished
by counting the number of readings
that exceed an alarm threshold in a
set time before triggering an alarm.
Additionally, an appropriate time delay
needs to be configured for the alert
and shutdown thresholds. Careful set-
ting of these thresholds, counts and
alarm delays will allow us to minimize
the possibility of false alarms. The
recip Impact/Impulse channels
in the Bently Nevada* 3500/70M
monitor include these features.
Signal FilteringAnother essential aspect to care-
fully consider is signal filtering. As
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described previously, an accelerom-
eter can detect vibration components
up to very high frequencies. While
acceleration analysis in a broad
frequency range may have diagnostic
value, the main object of crosshead
impact monitoring is protecting the
machine from the consequences
of mechanical failures. A signal
with too high corner frequency for
the low-pass filter may introduce
the risk of false alarms due to the
presence of high frequency content
not related to mechanical malfunc-
tions (and consequent impacts
transmitted to the crosshead guide
Amplitude Measurement
Our last important note is about
vibration measurements taken in
either root mean square (rms), zero-
to-peak (peak or pk), or peak-to-peak
(pp) amplitude measurement systems.
A few international standards
recommend rms measurement for
assessing machinery health based
on overall casing vibration and this
is traditionally adopted by many
practitioners. Rms values provide an
indication of the energy content of a
signal, and for malfunctions such as
loose foundation or load unbalance,
this energy content relates well with
machine condition, as well as opera-
tor perception of machine condition.
However, rms calculation applied to
an impulsive frequency-rich signal
such as crosshead vibration (Figure
5) does a poor job in correlating with
other critical conditions such as
mechanical knocks, which have rela-
tively little energy content, but prove
vital in assessing machine condition.
For these types of malfunctions,
peak amplitude measurement is
recommended as it correlates
well with both high-energy and
low-energy malfunctions typical of
reciprocating compressors. Applying
rms processing to crosshead
vibration signals would provide
under-predicting values.
Crank Angle Domain Analysis
When viewed in the time domain, the
non-stationary crosshead vibration
signal looks like multiple disconnected
events (Figure 5), so diagnostic
methodologies such as spectral
analysis provide little value due to the
discontinuous frequencies involved.
The most appropriate analytic
methodology is therefore based on
signal timing; Bently Nevada 3500
monitors synchronize the vibration
signal with crankshaft rotation to
associate peaks to a piston posi-
tion along the stroke. Individual
monitoring and alarming on crank
angle bands allows association
of peaks to the problem area.
For example, a peak occurring when
the piston is travelling toward the end
of its stroke near Top Dead Center
(TDC) can be correlated to liquid or
debris ingression in the compression
chamber. When the piston moves
towards its TDC position, the impact
with the non-compressible material
will generate an impulse event. The
monitoring system will then raise
an alarm for the corresponding
crank angle band (for example,
starting 10 degrees before top
dead center and ending 10 degrees
after). Figure 6 shows case of
liquid ingestion as detected by the
crosshead guide accelerometer.
YEARS OF FIELD EXPERIENCE HAVE DEMONSTRATED THAT TECHNIQUES WHICH MAY BE WELL UNDERSTOOD FOR MEASURING AND ANALYZING THE VIBRATION OF PURELY ROTATING MACHINERY CAN PRODUCE CONFUSING RESULTS WHEN APPLIED TO RECIPROCATING MACHINERY.
Apr.2012 No.2 Vol .32 ORBIT 3
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FIGURE 6: Crosshead acceleration in crank angle domain, presenting a high peak at Top Dead Center (TDC). The horizontal axis represents 360 degrees of crankshaft rotation (one full revolution), where 0 indicates TDC. The System 1 plot also displays a Throw Animation (in the upper right corner of this plot) showing the piston movement synchronized with the plot cursor. In this example the cursor is set at 2.5 degrees, and the animation shows that the piston is very close to the TDC position
FIGURE 7: The 3500/70M module returns two waveform samples to System 1 software from a single crosshead acceleration signal with two different filtering characteristics.
Understanding Frequency ContentAdditional advanced analysis tools
are available in System 1* diagnostic
software. As noted before, not all
impulse response events within the
crosshead accelerometer signal
contain the same frequencies.
Mechanical knocks excite resonances
of the reciprocating compressor
components such as crosshead
guides, distance pieces, etc. that
generally lie below 2 kHz. In contrast,
events originating in gas flow noise,
valve opening or valve closing events
express a much higher frequency.
Searching for a mechanical event in
an acceleration signal that contains
the whole transducer frequency
response range is practically impos-
sible due to the high amplitude and
frequency peaks that cover smaller,
yet more critical, peaks related to
mechanical events. Such overlap
prevents early indication of an incipi-
ent malfunction. It is for this reason
the signal must be filtered. Figure
7 shows crosshead acceleration in
the crank angle domain using 3 to
30 kHz (left plot) and 3 to 2 kHz (right
plot) band pass filtering. The peaks
present in the narrower pass-band
correspond to mechanical impacts,
which are difficult to distinguish in
the signal with broader filter corners.
System 1 software is integrated with
the 3500/70M monitor to allow dual
signal processing and both storing
and displaying the accelerometer
signal with two different filter settings.
40 ORBIT Vol .32 No.2 Apr.2012
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Diagnostic Approach
To wrap up this first installment, let us
consider how we can effectively asso-
ciate a malfunction to a specific vibra-
tion pattern and to obtain an early
failure diagnostic. Experience has
shown that associating vibration with
additional measured dynamic param-
eters such as rod load have proven
to be of great value in pinpoint