❖ Topics
• Imbalance, eccentricity and bent shaft
• Mechanical looseness
• Analysis of gears
• Analysis of belt driven machines
• Rolling element bearing analysis
• Analysis of pumps, compressor and fans
• Analysis of induction motors
• Misalignment, Cocked bearing and soft foot
Mass unbalance
“A shaft’s geometric centerline and mass centerline do
not coincide.”
Understanding imbalance
▪ The “ center of geometry “ is the line through the shaft and bearings.
▪ The “ center of mass ” is the point about which the mass is evenly
distributed.
Causes of imbalance
▪ Uneven dirt accumulation on fan rotors
▪ Lack of homogeneity in materials, especially in
casting (e.g. bubbles, porous section, blow holes)
▪ Cracked rotors
▪ Uneven corrosion and erosion of rotors
▪ Uneven mass distribution in electrical windings
Spectral data
▪ We expect to see a high 1X peak in the radial direction
▪ Sinusoidal time waveform
▪ Phase analysis is important
Ex2
1.0
IF - Example 2
-F1H Fan Inboard Horizontal
Waveform Display
02-Feb-00 15:13:51
0.8
PK = .5289
LOAD = 100.0
RPM = 2985.
RPS = 49.76
0.6
Acceleration in G- s
0.4
PK(+) = .8332
PK(-) = .8893
CRESTF= 2.38
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
0
0.5
1.0
1.5
2.0
2.5
3.0
Revolution Number
3.5
4.0
4.5
5.0
Imbalance (Types)
– Static Unbalance
– Couple Unbalance
– Dynamic Unbalance
❖ Static Unbalance
▪ Static unbalance is caused by an unbalance mass out of the gravity
centerline.
Imbalance (Types)
▪ The static unbalance results in 1X forces on both bearings of the
rotor, and the forces on both bearings are always in the same
direction.
▪ The vibration signals from them are "in phase" with each other.
Imbalance (Types)
❖ Couple Unbalance
▪ Couple unbalance is caused by two identical unbalance masses
located at 180° in the transverse area of the shaft.
Imbalance (Types)
▪ Couple unbalance may be statically balanced. When rotating dynamic
unbalance produces a vibration signal at 1X, radial predominant and
in opposite phase signals in both shaft extremes.
Imbalance (Types)
❖ Dynamic Unbalance
▪ Dynamic unbalance is combination of both static and couple
unbalance at the same time.
Imbalance (Types)
▪ In practice, dynamic unbalance is the most common form of
unbalance found. When rotating the dynamic unbalance produces a
vibration signal at 1X, radial predominant and the phase will depend
on the mass distribution along the axis.
Vertical oriented machines
▪ High vibration is at 1X in radial direction
▪ Vibration typically highest at vertically highest point
▪ In phase on both extreme of shaft
Overhung rotor unbalance
▪ High vibration at 1xTs (1 Order)
▪ High in radial and axial direction
▪ Measurement should be taken
from the bearing closest to the
overhung impeller
▪ Phase reading will be ”0” degree
in axial direction
Imbalance case study
Background|:
▪ The following data is taken from a Recirculation Fan designed to circulate
the hot air through an Oven to aid with drying the process. The oven is
vertically mounted and the product comes into the oven from the top and
exits at the bottom. There is one Recirculation Fan and one Extract Fan. Loss
of function from either fan results in the oven being taken offline.
▪ The spectral plots shows a
dominant 1xTs peak (1 Order)
with very little other vibration
present.
Route Wav eform
08-Nov-04 14:16:45
RMS = 4.66
LOAD = 100.0
RPM = 1246.
RPS = 20.77
4
Velocity in mm/Sec
▪ Time waveform is sinusoidal as
shown in velocity
Bm/c - TOP RECIRC FAN
TRF B m/c -F2H Fan Outboard Horizontal
8
PK(+) = 7.03
PK(-) = 7.40
CRESTF= 1.59
0
-4
-8
-12
0
1
2
3
Revolution Number
4
5
Imbalance case study
▪ The amplitudes should be checked in both radial directions to
confirm this problem.
Bm/c - TOP RECIRC FAN
TRF B m/c - Multiple Points (08-Nov -04)
Max Amp
4.27
Plot
Scale
TRF B m/c -F2V
RMS Velocity in mm/Sec
5
TRF B m/c -F2H
TRF B m/c -F1V
0
TRF B m/c -F1H
0
8000
16000
24000
Frequency in CPM
▪ The fan was recommended to be cleaned at the next available
opportunity and for it to be re-tested afterwards.
Eccentricity
“Shaft centerline is not coincident with the rotational
centerline.”
Eccentricity
▪ Eccentricity
generates
very
similar vibration pattern to
imbalance.
▪ The object (pulley, gear, etc.) will
“wobble” around the false
center, producing a strong radial
vibration.
▪ In case of eccentricity, a strong
parodic vibration is produced
along the length of belts.
Spectral data
▪ We expect to see a high 1X peak in the radial direction
▪ Horizontal and vertical phase reading usually differ either by 0° or by
180°.
Bent shaft
“A shaft bending is produced either by an axial
asymmetry of the shaft or by external forces on the
shaft producing the deformation. ”
Spectral data
▪ The bent shaft creates axial and radial vibration.
▪ High 1X in axial vibration
• 2X if bend is closer to coupling
▪ Axial phase is 180° out-of-phase
Mechanical looseness
▪ Looseness can be broken down into two main categories, Structural
(non-rotating) looseness and Component (rotating) looseness
Mechanical looseness
❑
Component (Rotating) looseness generally occurs when there is
excessive clearance to the components within the machine, such as:
• Excessive clearance between the shaft and bearings.
• Excessive clearance between the shaft and an impeller etc.
❑
Rotating looseness occur due to:
• Improper fit, bearing loose on shaft, and excessive clearance.
• Can also occur due to significant bearing wear .
Spectral data
▪ Multiple harmonics and can extend beyond up to 10X order
▪ The Noise floor can be raised
Do you notice that the
negative peaks are
bigger than the
positive?
Mechanical looseness
❑
Structural (non-rotating) looseness occurs when there is free
movement within the machines support structure causing excessive
vibration. This can be a result of:
• Loose support bolts to the components feet and supports
• Cracked welds
• Deterioration of the base itself.
Spectral data
▪ Structural looseness may produce a 1X signal in the radial direction
predominant in the horizontal reading.
▪ Measurements should be made on the bolts, feet and bases
▪ 180° phase difference will confirm this problem.
Case study
▪ Introduction:
▪ Data had been collected on the following fan for several months as part of a
routine periodic vibration data collection. During a routine visit to the
machine it was observed that there was a lot of low frequency activity
showing around the bearing on the inboard of the fan (F1H).
Case study
▪ The multiple plots shown above indicate the change over time from
the data taken on F1H.
• It is quite apparent that the data shown here is indicating multiple
harmonics of the 1xTs frequency (the rise energy as you move further
away from the 1xTs).
• This type of data is common to that of a looseness problem.
Case study
▪ The waveform data taken for this particular point is not showing a
random type of waveform pattern which you would expect from
Structural looseness, but there is a more a repeatable (timed
interval) pattern.
M2237
3
40 - Precip Fan
-F1H Fan Inboard Horizontal
Analyze Waveform
18-Sep-02 09:24:16
2
RMS = .3747
LOAD = 100.0
RPM = 998. (16.63 Hz)
Acceleration in G-s
1
PK(+) = 2.36
PK(-) = 2.83
CRESTF= 7.55
0
-1
-2
-3
-4
0
100
200
300
400
Time in mSecs
500
600
700
800
Case study
▪ This type of waveform would more be indicating Component
looseness and may indicate a problem with a loose bearing.
Conclusion:
▪ It was recommended that the bearing should be inspected at the
next available opportunity.
• Upon inspection it was found that the bearing was a ‘Taper-Lock’ bearing
and the taper lock was loose, thus resulting in excessive clearance
between the bearing and the rotor.
Gear box defects
▪ Gears are commonly used in
industry to provide the speed and
power transmission.
▪ Gears can provide speed changes
and torque transmission without
slip.
▪ Regardless of gear type they all
produce the same basic vibration
patterns and characteristics when a
defect is present.
▪ We will explore the failure modes,
measurement techniques, and
methods available for the vibration
analyst to detect and diagnose fault
conditions.
Forcing frequencies
▪ Input speed
▪ Output speed
▪ The gear mesh frequency (GMF)
• Input speed x Tin
▪ Hunting tooth frequency: HTF
▪ HTF = GMF X Na/ (Tin x Tout).
Gear Mesh= Number of teeth X Shaft speed
Output speed= Input speed X Input teeth/Output teeth
Forcing frequencies
▪ Two mating gears will generate a frequency known as the GMF and
will show in the spectral data regardless of gear condition.
Calculating GMF – Single Reduction
▪ Single Reduction Gear Train
– The GMF is simply defined as the number of teeth on a gear
multiplied by its turning speed
GMF = (#teeth) x (Turning speed)
▪ Example:
– Consider the following gear train,
INPUT
OUTPUT
Input
= 1490RPM
Gear 1
= 44 Teeth
Gear 2
= 71 Teeth
GMF = #teeth x turning speed
GMF = 44teeth x 1490 RPM
GMF = 65560 CPM
or 65560/60 = 1092.6 Hz
Calculating GMF – Multi Reduction
▪ Calculating the GMF for gearboxes that have multiple trains use
the following.
GMF = (#teeth) x (Turning speed)
Gear Ratio = (#teeth in) / (#teeth out)
Speed out = (Speed in) x (Gear Ratio)
▪ Example:
– Consider the following gear train:
INPUT
OUTPUT
Input
= 1490RPM
Gear 1
Gear 2
= 15 teeth
= 21 teeth
Gear 3
Gear 4
= 19 teeth
= 54 teeth
Calculating GMF – Multi Reduction
INPUT
Input
= 1490RPM
Gear 1
Gear 2
= 15 teeth
= 21 teeth
Gear 3
Gear 4
= 19 teeth
= 54 teeth
OUTPUT
Gear Ratio 1
Speed Out
= 15 teeth / 21 teeth
= 1490 RPM x 0.714
= 0.714
= 1064.28 RPM
Gear Ratio 2
Speed Out
= 19 teeth / 54 teeth
= 1064.28 RPM x 0.351
= 0.351
= 374.47 RPM
GMF 1 = 1490 RPM x 15 teeth
= 22350 CPM
GMF 2 = 1064.28 RPM x 19 teeth = 20221.32 CPM
Gears – Sideband Frequencies
▪ Sidebands are the most common indication that a gear is defected.
• Sidebands are equally spaced frequencies in the spectral data that
materialise either side of the main GMF peak.
• The sideband frequency spacing is equal to either the turning speed of
the input gear or the turning speed of the output gear.
▪ Sidebands show in the data when either the
gear is worn, loose or eccentric.
• The speed of the shaft with the bad gear on it
will produce the most dominant sidebands in
the spectral data.
Gears – Sideband Frequencies
▪ The spectral data shows GMF with sideband data.
• The sidebands are equally spaced at intervals of 310 CPM. This is
indicating the gear that rotates at 310 RPM is the one that is worn or
damaged.
Vibration analysis
▪ 1X of input, output speed and intermediate shafts
▪ Gear mesh (GM) frequency peak will be present
• 2xGM and 3xGM may also be present
• Spur gears: Higher in radial direction
• Helical gears: Higher in axial direction
Time waveform analysis
▪ Time waveform is a very powerful analysis tool when attempting to
diagnose gear faults.
▪ Gears can produce different types of waveforms, the one shown
below is indicating gear wear.
• As the defective teeth come into mesh the noise generated increases
showing an increase in amplitude in the vibration data
X401A
1.5
FPP - SAND MILLS (OLD)A
-G3A Shaft 02 Inboard Axial
Route W aveform
07-Nov -02 09:11:53
1.2
PK = .4580
LOAD = 100.0
RPM = 311. (5.19 Hz)
0.9
Acceleration in G-s
0.6
PK(+) = 1.27
PK(-) = 1.13
CRESTF= 3.91
0.3
0
-0.3
-0.6
-0.9
-1.2
-1.5
0
1
2
3
Revolution Number
4
5
6
Gear Eccentric/ Backlash
▪ Eccentric gears produce greater modulation: Higher amplitude
modulation.
▪ 1xGMF and 3xGMF will be prominent.
▪ Gear backlash will also generate shaft speed sidebands around the
gear mesh frequency.
▪ Backlash may also excite the gear natural frequency
Misaligned gear
▪ Misaligned gears will also generate a 1xGM frequency with
sidebands, however 2xGM will be dominant
▪ It is therefore important to set your frequency range (Fmax) high
enough to see these frequency
Cracked or broken tooth
▪ A cracked or broken tooth will generate a high amplitude peak at the
turning speed of the gear.
▪ Gear natural frequency may be excited
▪ There will be sidebands of the turning speed of that gear.
Case study
▪ The following case study is from a motor gearbox unit that drives a
roller.
• Product (Fibre) is fed along the top of the roll while being washed through a
series of baths.
• There are several of these Wash Nip Rollers in a continuous stream, failure of
any one of them results in lost production
• Data is collected on a fortnightly
basis as part of a routine data
collection route
Case study
▪ The spectral data shown below is taken from the motor in the axial
direction
• (As the motor is mounted directly into the gearbox the first helical gear is
mounted on the end of the motor shaft).
• The GMF is highlighted by
the primary cursor at 49
Orders
• The fault frequency data
(dotted lines) indicate the
sideband data showing
gear wear on the first gear
in the gear train
Case study
▪ The waveform data is showing a distinct pattern commonly
associated with gears.
▪ The amplitude increases In noise as the damaged teeth come into
mesh
• Producing over 2G-s of force in both the positive and negative direction
Case study
▪ The gears were inspected due to the critical nature of the asset. It
was found the gear to be severely damaged.
▪ A new gearbox was fitted and new data was taken showing the
difference between the good and bad gear
Belt defects
▪ Belts are the most common low cost way to transmit power from one
shaft to another.
• Belt drives rely on friction between the belt and pulley to transmit
power between drive and driven shafts
▪ The ability of belt to transmit power depends upon
1.
2.
3.
4.
Belt Tension (tension on the belt holds it tightly against the sheave)
Friction between the belt and sheave
The arc of contact between the belt and sheave (Wrap)
The speed of the belt
Belt defects
▪ Belt defects can be considered non-critical faults
▪ Relative ease of replacement and requiring minimum downtime
• But belt defects are a major contributor to the overall vibration of the
machine resulting in premature failure of other machine components.
▪ Belts can be easily damaged by
heat, oil and grease.
▪ Due to slippage on the sheaves
they can not be used where
exact speed changes are
required (except for timing
belts).
Belt defects
▪ Belt defects, such as cracks, broken or missing pieces, hard and soft
spots can generate belt frequency (1xbelt) and harmonics
• The 1xbelt frequency is sub-synchronous
▪ The predominant harmonic is typically the 2xBelt frequency and can
be seen in the radial plain in-line with the belts.
• Severity is judged by the number and amplitude of the harmonics seen
in the spectral data
Belt defects
▪ Just like two mating shafts, belt drive systems can also be misaligned
in both angular and offset directions.
Offset Misalignment
Angular Misalignment
▪ Pulley misalignment results in high axial vibration at the shaft
turning speed.
• If the belt is also defected then 1xbelt frequency and harmonics may
also show in the axial direction
Belt defects- Frequency
Calculation
▪ The fundamental belt frequency can be calculated using the following
equation:
Belt Freq. = (3.142 * Pulley Ts * Pulley PCD)
Belt (Length)
• Where:
• Ts = Turning Speed
• PCD = Pitch Circle Diameter
• Note: The PCD and belt length must be in the same units
▪ A timing will belt will also have a specific frequency related to the
number of teeth on the pulley
Timing Belt Freq. = (Pulley Ts) * (# Pulley Teeth)
Belt defects- Frequency
Calculation
▪ Belt Frequency Calculation
▪ Belt Frequency = (3.142 * 1480 * 300) / (2000)
▪ Belt Frequency = (1395048) / (2000)
▪ Belt Frequency = 697.524 CPM
– This is sub-synchronous to the 1xTs of the pulley
Motor RPM
Pulley Diameter
Belt Length
= 1480 RPM
= 300 mm
= 2000mm
Belt defects- Spectral data
▪ The spectral data above is data taken of a motor from an Air
Handling Unit.
• The frequency highlighted by the primary cursor is showing the 1xTs
of the motor (1st Order)
• The first peak is the
fundamental frequency
of the belt rotation.
• The second peak is the
2xbelt
frequency
suggesting
there
is
damage to the belt
1 x Belt Frequency
showing with harmonics
Dominant 2 x Belt
Frequency
Case study
▪ The following data was taken on an Air Handling Unit. The Air
Handling Unit is a supply fan from shared services. This is a stand
alone unit with no stand by capability
• The primary cursor is
highlighting the 1xbelt
with several harmonics.
• The 2xbelt is very
dominant suggesting
there is damage to the
belts.
Case study
▪ As this is a critical machine it was recommended on the next available
opportunity that the belts needed to be checked for damage and realigned.
• The machine was stopped and the belts were inspected based upon the
recommendation.
• Significant damage was found to several of the belts during this inspection
as well as worn pulleys.
Bearings
“A bearing is machine element that constraints relative
motion between moving parts to only in the desired
motion”
Rolling contact bearings
▪ Load is transferred trough rolling element such as balls, straight and
tapered cylinders and spherical rollers
Tapered Bearing
Ball Bearing
Cylindrical Bearing
▪ Rolling element bearings have specific bearing failure modes that can
be observed in the spectral and waveform data.
Load and life
▪ L10 life factor
• The life expected due to normal fatigue by 90% of bearings (10 % failure)
• Ball Bearings:
• Roller Bearings:
Load and life
▪ As 10% load increases from misalignment reduces the calculated
bearing life by one-third!
▪ If you increased the load on the bearing by 20% the life is halved!
▪ If you double the load, you reduce the life to one-seventh of its
design life.
Bearing defects
▪ Caused of rolling element bearings defects
•
•
•
•
•
•
Inappropriate use of bearings
Poor handling and installation
Improper lubrication, lubrication method or sealing device
Inappropriate speed and operating temperature
Contamination by foreign matter during installation
Abnormally heavy load
Poor design, poor installation practices,
and poor maintenance leads to reduced
bearing life
Defect frequencies
▪ Bearing frequencies differ from most other frequencies present
within the spectral data.
▪ We can detect these frequencies in the time waveform and spectrum
(velocity, acceleration and envelope).
▪ We can calculate the frequencies or find them in a database.
▪ Or we can estimated them
•
Bearings generate ‘non synchronous’ frequencies, harmonics and
sidebands
▪ There are four main fundamental bearing defect frequencies
these are:
Defect frequencies
Outer Race
Inner Race
Defect frequencies
▪ Bearing defect frequencies are calculated based upon the geometry
of the bearing these calculations may include:
•
•
•
•
Number of rolling elements
Pitch Circle Diameter
Rolling element diameter
Contact angle
▪ Defined within Machinery Health Manager there are over 100000
predefined bearing stored in the CSI bearing warehouse
BEARINGS in CSI Warehouse:
c:\RBMsuite\SysData\CSI_CMP.WH
****************************************************
BRG ID Bearing Type
12143 RHP 6218
24421 SKF 6313E
25372 SKF I-26313
#B/R
11
8
19
FTF
0.418
0.376
0.433
BSF BPFO
2.967 4.598
1.894 3.009
3.568 8.219
BPFI
6.402
4.991
10.781
How Bearing Faults Generate
Vibration
Defect frequenciescharacteristics
▪ Characteristics of Bearing Defects
•
•
•
•
High frequency raised noise level (Hump of energy)
Non-Synchronous harmonic peaks (Both low and high frequency)
Time waveform will show a lot of noise/impacting
Early stages of bearing wear may show better if viewed in
acceleration in the frequency domain
• Fundamental bearing defect frequency (First calculable frequency)
may not be present in the spectral data
Vibration analysis method
Very high frequency
▪ Acoustic emission
▪ Shock pulse SPM, Spike energy, SEE and PeakVue
High frequency
▪ Enveloping and amplitude demodulation
▪ Acceleration spectrum
Mid-low frequency
▪ Velocity spectrum
▪ Time waveform analysis
▪ Overall vibration level
Stage-one bearing fault
▪ Sub-surface damage only
• Friction and minor impacts
▪ Very high frequency vibration
•
•
•
•
•
Friction: greater than 20kHz in earliest stage
you can’t hear it (without assistance)
Noise due to inadequate lubrication
Very low levels
Very short duration impacts
▪ ‘Stress waves’ or ‘shock pulses’
• 1kHz to 15kHz
▪ Traditional vibration analysis techniques are inadequate at this
stage.
Stage-one bearing fault
Monitoring techniques
▪
Vibration Analysis (typical):
1) Standard FFT: no visible indication in velocity spectrum (may show in
acceleration)
2) Spike Energy: slight increase in value (e.g. 0.25 gSE)
3) Envelope: noise floor may rise
4) PeakVue: bearing frequency peak(s) corresponding to fault type amplitude
at 2-7 g’s depending on type and location
▪ Oil Analysis (typical):
1) Readings: Slight increase in elemental Fe, particle count, and WPC
2) Visual Ferrography:
• Small platelet shaped particles (<30 μ) from contact fatigue
• Small spherical shaped particles (<5 μ) from surface fatigue
Action required
Damage
Life
Action
• Very difficult to see damage
• Sub-surface damage only
• 10-20 % of L10 life
• Lubricate correctly
• Continue monitoring
Stage-two bearing fault
▪ Sub-surface damage only
• Friction and minor impacts
▪ Very high frequency vibration continues to increase in amplitude
▪ Envelope (demodulation) spectrum should show signs
• Defect frequencies present in spectrum
▪ Velocity spectrum still won’t indicate fault. Acceleration spectrum
should indicate fault.
Stage-two bearing fault
Monitoring techniques
▪ Vibration Analysis (typical):
1)
2)
3)
4)
Standard FFT: no visible indication in velocity spectrum
Spike Energy: increase in value (e.g. 0.50 gSE)
Envelope: defect frequency will be present and noise floor should rise
PeakVue: bearing frequency peak(s) with increasing harmonics amplitude at
3-10 g’s depending on type and location
▪ Oil Analysis (typical):
1) Readings: elemental Fe stable but increase in particle count, WPC, and PLP
2) Visual Ferrography:
• Platelet shaped particles (30-50 μ) from contact fatigue
• Possible spherical shaped particles (<5 μ) from surface fatigue
Action required
Damage
Life
Action
• Difficult to see damage
• Sub-surface damage only
• 5-10 % of L10 life
• Lubricate correctly
• Monitoring more frequently
Stage-three bearing fault
▪ More significant damage:
• Minor damage trough to more significant damage
• Bearing can fail in many ways for many reasons
▪ Very high frequency vibration continues to increase in amplitude
▪ Envelope (demodulation) spectrum will be effective
• Defect frequencies present in spectrum
• Filters must be setup correctly
▪ Classic pattern appears in the spectrum:
• Harmonics due to impacts
• Modulation (sidebands) due to cyclic change in load
Stage-three bearing fault
▪ Outer race fault (outer race rotating)
Stage-three bearing fault
▪ Outer race fault (outer race rotating)
Stage-three bearing fault
▪ Inner race fault (inner race rotating)
Stage-three bearing fault
▪ Inner race fault (inner race rotating)
Stage-three bearing fault
▪ Ball or roller faults
Stage-three bearing fault
▪ Ball or roller fault
Monitoring techniques
▪ Vibration Analysis (typical):
1)
2)
3)
4)
Standard FFT: visible defect frequency in both velocity & acceleration
spectrum
Spike Energy: increase in value (e.g. 1.0 gSE)
Envelope: defect frequency will be present and noise floor should rise
PeakVue: bearing frequency peak(s) with increasing harmonics and
sidebands amplitude climbs to 5-10 g’s or higher (depending on type and
location)
▪ Oil Analysis (typical):
1) Readings: small change in elemental Fe, substantial increase in WPC and
PLP
2) Visual Ferrography:
• Sharp increase in large particles (>30μ), both platelets and cutting wear
• Increased three-dimensional appearance to wear particles
Action required
Damage
Life
Action
• Easy to see damage
• A range of severity
• <5 % of L10 life
• Replace as soon as possible
• Monitoring more frequently
Stage-four bearing fault
▪ Significant damage:
• Damage far more extensive
• Damage in one component causes damage in another components
• Failure is imminent
▪ Very high frequency vibration may trend downwards.
• The bearing degrades so much that the spectrum becomes a mass of
noise.
▪ At this point the bearing will fail at any point.
▪ Spectrum, time waveform and envelope (demodulation) spectrum
analysis still effective - at first.
Stage-four bearing fault
▪ As wear continue the geometry can change.
• Defect frequency can change
▪ As wear continues clearance can increase.
• looseness.
• Increased noise
▪ As wear continues it is difficult to distinguish the frequencies.
• Defect frequencies swallowed by the noise in velocity, acceleration and
envelope spectrum
▪ The end is nigh!
Stage-four bearing fault
▪ Outer race faults
Monitoring techniques
▪ Vibration Analysis (typical):
1)
2)
3)
4)
Standard FFT: discrete bearing frequencies replaced by broadband noise
Spike Energy: falling levels until just before failure, then levels rise sharply
Envelope: defect frequency will be present and noise floor should rise
PeakVue: bearing frequency peak(s) with increasing harmonics and
sidebands amplitude climbs to 10 g’s or higher (depending on type and
location)
▪ Oil Analysis (typical):
1) Readings: small change in elemental Fe, substantial increase in WPC and
PLP
2) Visual Ferrography:
• Broad range of huge particles (75μ+) from fatigue and adhesion
• Particle counts/ferrous density are excessive
Action required
Damage
Life
Action
• Very easy to see damage
• A range of severities
• <1 % of L10 life
• Replace now
Bearing fault- BPFI example
▪ Typical data showing a defected inner race
• Fundamental frequency showing
• Harmonics low and high frequency + sidebands
Bearing fault- BPFO example
▪ Data showing a defect related to the BPFO
• The fundamental frequency is showing
• Harmonics from low to high frequency
Bearing fault- BSF examples
▪ Bearing defect showing the BSF – Rolling elements
• Sidebands around the BSF = FTF
Bearing fault- FTF examples
▪ The FTF is the only bearing frequency that is sub-synchronous
• May not detect then with conventional vibration data
• FTF defect at 0.4 orders shown in Peakvue
Bearing fault- typical waveform
▪ As a bearing becomes defected then the amount of noise/force
generated as the rolling elements impact the defective area
increases.
• This can show significant G-levels in the time waveform. This value is
trended in the software as the Peak-Peak value
• This data is taken
from a pump with a
damaged bearing
– The force levels
are reaching
40G-s
Electric motor
▪ A motor can be simply broken down into two key components
• Rotor
✓ Consists laminations with solid conductors called rotor bars
✓ A circular flow of current through these rotor bars causes the rotor to become an
electromagnet which will rotate in a magnetic filed.
• Stator
✓ Consists of wire wound in coils and placed in slots of an iron core.
✓ The stator produces a rotating magnetic field.
Spectral data
▪ The most common electrical frequency that appears in the spectral
data is the 2 x Line Frequency.
▪ The spectral plot is
showing a peak at
100Hz (6000cpm)
– 2xLf
– This can be mistaken
for misalignment
Electrical defects- causes
▪ Common fault types that can produce the 2xLf peak are as follows:
•
•
•
•
•
•
Dynamic Eccentricity – Usually Rotor Related
Static Eccentricity – Usually Stator Related
Loose Iron or Slot Defect – Rotor or Stator
Open or Shorted Windings
Insulation Breakdown or Imbalanced Phase
Loose Connectors
Static eccentricity
▪ Static eccentricity produces an uneven stationary air gap between the
rotor and stator that produces a very directional source of vibration.
• Soft foot and warped bases can produce an eccentric stator
Dynamic eccentricity
▪ Eccentricity rotor produce a rotating variable air gap between the
rotor and the stator.
• You will see the 2xLF along with pole pass frequency side bends
Case study
• The following case study was taken from a glass manufacturer. The
data was from the ‘Electric Front Wall Cooling Fan’.
• This fan unit is a critical fan to the process and has no standby unit.
• In this particular instance the motor failed shortly after the data was collected.
Case study
• The multi-plot above shows the same measurement point going back
over the last 5 route readings.
– This particular plot is useful for determining rate of change.
– It is quite clear how this particular frequency suddenly appeared
• Conclusion
– As the motor failed shortly
after data collection no
action was taken to prevent
failure.
– The investigation in the
motor showed one of the
connectors had come loose
causing the motor to burn
out.
Misalignment
“Shafts are misaligned when their rotational centerlines
are not collinear when the machines are operating
under normal conditions.”
Misalignment (Types)
– Parallel Misalignment
– Angular Misalignment
– Most Common both of above
❖ Parallel Misalignment
▪ Parallel misalignment is produced when
the centerlines are parallel but offset.
Misalignment (Types)
❖ Angular Misalignment
▪ Angular misalignment is seen when
the shaft centerlines coincide at one
point along the projected axis of both
shafts.
Belt misalignment
▪ High 1X peak in the axial direction
▪ Axial phase reading will be 180° out-of-phase
Cocked bearing
▪ Cocked bearing is a form of misalignment
▪ Cocked bearing can result from poor
installation practices
▪ The machine itself distorts due to thermal
growth or soft foot conditions
Soft foot
▪ Feet of machine are not all flat on the base. Feet not at same height
or bent.
▪ Can be checked by loosening/tightening of hold down blots.
▪ If a soft foot condition exists, there will be a high 1X peak in the radial
direction, and often a 2X
▪ and 3X component as well
▪ Main indicator is high 2xLF (100/120 HZ) on motor.
How to Check for Soft foot
How to check for Soft Foot
 Move indicators to 12 o'clock position, depress indicators and then zero.
 Loose one base bolt. If indicator moves away form zero, place the amount of shims that will
slide under that foot. Retighten bolt and make sure the dial indicator needle does not move.
 Repeat this procedure for the remaining feet.
 If it lifts more than 0.002" or 0.05 mm, then the soft foot condition must be corrected.
Thermal Growth
• Machines that operate at a considerably hotter or colder condition than the
ambient room temperature should be thermally compensated.
• They will “grow” or “shrink” as they
heat up, or cool off
The machine manufacturer’s specs
are a good place to start
But, the machine manufacturer probably does not know:
•
The exact temperature of the driver and driven machines
•
Ventilation quality or cooling effects
•
Piping strain influences
•
Piping thermal changes
Coefficient of Thermal
Expansion
•
The coefficient of thermal expansion describes how the size of an object changes
with a change in temperature.
•
It measures the fractional change in size per degree change in temperature at a
constant pressure.
•
1-foot of steel get 100 degrees hotter, it grows about 8 mils ( 0.008″)
α𝒆𝒙𝒑.𝒄𝒂𝒓𝒃𝒐𝒏 𝒔𝒕𝒆𝒆𝒍 = 𝟎.𝟎𝟎𝟔𝟑×𝒍𝒆𝒏𝒈𝒕𝒉×𝑻𝒆𝒎𝒑. 𝒄𝒉𝒂𝒏𝒈𝒆
Length (Inches)
Temp. Change
Growth (mils)
15.0
100
9.5
15.0
125
11.8
15.0
150
14.2
15.0
175
16.5
15.0
200
21.3
However, this is not a magic
formula!
• Machines do not usually heat or cool at the exact same temperature top to
bottom.
• You need to find a mean, or average temperature of the machine from the
centerline of the shaft, to the bottom of the foot.
The Best Way to Know Thermal
Growth Changes…

Measure the machine in the cold condition, and pre-set it to the
manufacturer’s recommendations.

Re-measure in the hot condition, if possible.

Some lasers can do this calculation for you, or you can simply plot it on
paper.

In addition, some laser alignment tool manufacturers sell equipment
that allow you to measure the thermal changes.
Shaft alignment by Using LASER
Equipment
The two basic components of the laser alignment system are the "emitter“
(sometimes called the "transmitter") and "detector" (sometimes called the
"receiver").
Lay out of Laser Alignment
Shaft alignment by Using LASER
Equipment
General lay out of LASER
Alignment Technique
General Lay out
Laser beam on the measuring head
Alignment Procedure
Aligning Motor to Pump
LASER Aligning Display Unit
Reading Shows Angular &
Parallel Deviation
Alignment tolerance table
Case study
Background|:
The Kiln drive gearbox motor had been replaced during a planned plant
shutdown. During the start up of the plant after the shutdown it was noted
that the motor and gearbox were excessively noisy. Vibration data was taken
during the run up of the plant to determine the cause of the problem.
• The spectral plot shown above
is the data taken from the drive
end of the motor. Here there is
a dominant 2xTs peak.
Case study
▪ In addition to the misalignment the excessive forces being applied to
the machine were causing excessive loading on the gears.
0804
04 - Kiln Driv e
-G2A Shaft 01 Outboard Axial
5
Max Amp
5.98
4
3
2
Amplitude - Mixed Units
1
0
29-Mar-01
09:40: 20
29-Mar-01
09:40: 09
After Shutdown
26-Mar-01
12:11: 12
23-Jan-01
15:02: 00
Before Shutdown
25-Oct-00
09:04: 17
08-Aug-00
14:06: 56
0
60
120
180
Frequency in kCPM
240
300
Case study
▪ During data collection it was also observed that the grouting around the front feet
of the motor had begun to crack as a result of the excessive force being applied to
the motor base and feet due to the misalignment.
Conclusion:
• It was confirmed the engineers that replaced the motor during the shutdown
and assumed as long as they kept the shims in the correct place then
alignment was not necessary.
• Corrective action was required and production was stopped so the motor
could be re-aligned and the mountings re-secured.
Its not about having the skill to do some thing. Its about having the will,
desire and commitment to be your best
HAVE A GOOD PROFESSIONAL CAREER