A Technical Publication for Advancing the Practice of ...€¦ · detecting electrical faults....

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 pg10

Vibration Data identifies Hot spot on Motor rotor • pg26

new DePArTMenT iTeMs

Application notes • pg25 system 1* software Tips & Tricks • pg42

eDiTOr’s 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 couldn’t 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!

editor’s notepad

Cheers!

gary

gary Swift

editor

Orbit Magazine

[email protected]

greetings, and welcome to

Orbit! This issue’s 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 o’ConnellDesign: gina Alterieuropean Circulation: estelle Séjournénorth 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 don´t

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

1995–2006 (retired)

Nicolas peton

MDs Technical Leader for south

and west europe

ge Measurement & Control

rob Winter

senior specialist

Learning and Development


Arun menon

global Director

Machinery Diagnostics services


Reg

iSte

Rto

dAy

!

4 ORBIT Vol .32 • No.2 • Apr.2012

news

Advanced Machinery Dynamics Course11–15 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 O’neill, 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

[email protected]

Caner Kuzkaya

Vice President, Artesis A.s.

[email protected]

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

doesn’t 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.

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

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

hasn’t 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 machine’s 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.

• ItcannotbeusedforDCor

single-phase motors.

• Forvariablefrequencydrives,

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.


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 line’s 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

John W. kingham

Field Application engineer

[email protected]

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. i’ve 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 ½X 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):

20 ORBIT Vol .32 • No.2 • Apr.2012

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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 isn’t 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

didn’t delete the plot group, at the end of the day, i’d 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 isn’t 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.

22 ORBIT Vol .32 • No.2 • Apr.2012

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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.

i’Ve 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 issues—editor]

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

Vibration Data identifies Hot spot on Motor rotor

Roengchai Chumai

Technical Leader

Bently nevada Machinery

Diagnostics services

[email protected]

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 THAiLAnD’s eCOnOMY.

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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.

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.

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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 speed

120f = 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 Frequency

1500 cpm / (60 Hz/cpm) = 25 Hz.

so with no slip, synchronous vibration frequency at

1500 rpm is 25 Hz.

slip Frequency

since the motor was actually running at 1494 rpm,

the slip frequency was:

1500 cpm – 1494 cpm = 6 cpm.

Converting to hz

6 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 speed

with the motor running at 1494 rpm, actual 1X

frequency is found by converting to Hz:

1494 cpm /(60 Hz/cpm) = 24.9 Hz.

Summary

The 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.

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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

[email protected]

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.

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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]

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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.

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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.

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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 pinpointing a

specific component failure. Details of

these other dynamic parameters will

be presented in following Orbit issues.

Due to the complexity of the signal

content and the vibration signatures

that differ from case to case based

on operating conditions and failure

modes, several different automated

diagnostic approaches have been

developed. This includes rule-based

and model-based approaches

that are driven by data or by “first

principles” of Physics relationships.

each approach presents pros and

cons and will be further discussed

in following issues as well.

references1. ge energy Brochure, Condition

Monitoring solutions for reciprocating

Compressors, geA-14927

* denotes a trademark of Bently nevada, inc., a wholly-owned subsidiary of general electric Company.


experieNCe hAs shOWN

thAt ASSoCiAtiNg VibRAtioN

WiTh ADDiTiONAl meAsureD

dyNAmiC pARAmeteRS SuCh

AS Rod loAd hAVe pRoVeN

to be oF gReAt VAlue iN

piNpoiNtiNg A SpeCiFiC

COmpONeNT FAilure.”

“

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How to Create a Machine reference Dataset

Stuart Rochon

Bently nevada Field

Application engineer (FAe)

[email protected]

Dear System 1 Software User,

In this installment of System 1* Tips and Tricks, we’ll show you how to select

reference data for an entire machine train. Reference data is helpful to compare a

previous known operating condition or state to a current condition or state. Reference

data can be collected for certain operating conditions. For instance, you may want to

collect reference data after a machine overhaul so that the data

can later be compared and overlaid as a quick visual comparison

to look for change in condition. We hope you enjoy this issue.

Sincerely,

Your USA Southern Region FAE Team

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sysTem 1* sOFTWAre Tips & TriCks

Tip ApplicabilitySystem 1 Versions

• Applies to all versions.

System 1 Features

• Applies to system 1 Data Plots

Recommended user level

• Power user

• Diagnostic user

• Mid Level user

1. Open system 1 Display. if the Plot session Manager

window is not visible, open it using the View menu at the

top of the screen. Choose the Collection groups tab at

the lower right corner of the enterprise View (Figure 1)

Figure 1: system 1 Display, showing the Plot session Manager pane in the lower right corner.

2. Choose the Keyphasor* channel that is associated

with the machine you want to save reference

data on (Figure 2). right click the Keyphasor

channel and select Configure Reference

sample range from the shortcut menu).

Figure 2: selecting Keyphasor channel and opening the reference range Dataset Manager.

3. when the reference range Dataset Manager opens

(Figure 3), click Add and enter a name and description

for the new reference dataset (you cannot use spaces

in the name, but underscore characters are OK).

Figure 3: Click Add to name the new reference range data set.

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4. normally i would select reference data from

an earlier cold startup, using the Transient

data source. But since this is a simulated

example, i will use Trend data (Figure 4).

Figure 4: reference range Dataset Configuration dialog with Trend data selected.

5. next, we will choose the time date range close to the

event we want to reference. we can narrow down

the sample range by choosing the beginning and

ending speed of the sample range. To do this, from

the “Variable Filter” choose user Selected from the

drop-down list (Figure 5). next, we can select the

variables from the pop-up window (Figure 6).

Figure 5: Choosing user selected from the Variable Filter.

6. For this example i selected the speed and

“Direct” (unfiltered amplitude) variables.

After making your selections, click oK.

Figure 6: selecting the variables to be included in the reference dataset.

7. now select speed from the “show Plot” menu, and

click plot. (Figure 7). The speed data for the selected

date range will appear in a trend plot (Figure 8).

Figure 7: selecting speed data to be shown in the plot.

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Figure 8: new trend plot showing the machine speed data for the startup transient.

8. Click on the plot to show your cursor, so you can

select your start and end points in the dataset.

Move the cursor to the beginning of the startup

transient (Figure 9). This will be at the point where

the machine speed begins to ramp upward.

write down time and date to the minute.

Figure 9: Plot cursor is positioned at the beginning of the speed transient corresponding to the machine startup.

9. now move the cursor to the end of the startup speed

ramp (Figure 10). write down the date and time to the

minute. we have now identified the exact time range

that we want to include in our reference dataset.

Figure 10: Locating the end of the machine startup transient.

10. in the reference range Dataset Configuration

dialog, select the data source ‘From’ and ‘Two’ times

that we identified in steps 9 and 10 (Figure 11).

Figure 11: selecting the ‘From’ and ‘To’ times for the identified data range of interest.

11. now we can choose which measurement points

(channels) to include in the dataset for the speed and

Direct variables that we selected in step 6 (Figure 12).

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Figure 12: selecting the measurement points (channels) that we want to see for values of Direct vibration amplitude and machine speed.

12. we can also repeat the same steps to select filtered

variables such as 1X & 2X (Figure 13) and gap voltage

(Figure 14). Once all selections are made, click Apply

or oK. now your new reference dataset will be saved,

and it will be available for use as a plot overlay.

Figure 13: This example shows the selection of channels of 1X and 2X filtered data to be included in the reference dataset.

Figure 14: This example shows the selection of channels of gap voltage data to be included in the reference dataset.

13. To use the reference data, open the plot group

you want to use the reference data in, or start

a new plot session. in the Plot Configuration

dialog under Overlay, highlight Select overlay

from the drop-down menu (Figure 15).

Figure 15: use the Plot Configuration dialog to select the reference dataset to be overlaid in the plot group.

14. From the reference range Dataset Manager

pop-up window (Figure 16), select the reference

dataset that you just finished creating, and

click oK. The reference data will now be shown

in your plot along with the current data.

Figure 16: selecting which reference dataset to be shown as an overlay in plots. in this example, there is only one reference dataset in the list, since we have only created a single reference dataset so far. However, in actual practice, it is quite common to have a long list of specified reference datasets that represent various machine operating conditions.

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Figure 17: This example shows an Orbit Timebase plot with the current values shown as blue curves, and the overlaid historical reference data in orange. it is obvious that the shape of the orbit and the waveforms have changed over time.

ReFeReNCe dAtA iS helpFul to CompARe A pReViouS kNOWN OperATiNg CONDiTiON Or sTATe TO A CurreNT CONDiTiON Or sTATe.”“

Viewing reference Data

now for the fun part! The historical reference data that

you selected to overlay will appear in the plot as orange

curves, while current values are shown in blue. when

the reference data matches the current value data

perfectly, the blue curves completely cover the orange

curves, so the reference data is not visible at all.

so any time that you see the orange color exposed, it

means that something has changed between the time the

reference data was collected and the current conditions.

with appropriate plot sessions, even an inexperienced

person can immediately spot that a change has occurred,

and call for a deeper look at the machine condition.

* denotes a trademark of Bently nevada, inc., a wholly owned subsidiary of general electric Company.


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