Descripción: Vibration Analysis Level- 1 Updated [Compatibility Mode]...
Warmest Welcome to Vibration Analysis Level I Course
Vibration Analysis Level‐I y IMRAN AHMAD Director Technical SUMICO Technologies (Pvt) Ltd +92 321 427 6092
[email protected] p www.sumico.com.pk
Timings • • • • • • •
1st Session Session Tea Break 2nd Session S i Lunch/Prayer Break 3rd Session Tea Break Tea Break 4th Session
0900 1045 0900‐1045 1045‐1100 1100 1300 1100‐1300 1300‐1400 1400‐1530 1530‐1545 1530 1545 1545‐1700
• • • • • •
Typical Machinery Problems that Can Be Found Using Vibration Analysis Unbalance Mi li Misalignment t Mechanical looseness Structural problems Structural problems Bent shaft Bearing faults g
Typical Machinery Problems that Can Be Found Using Vibration Analysis • • • • • •
Gear faults Belt problems Lubrication problems Electrical motor faults Cavitations and turbulence others
What is CBM & Why ? What is CBM & Why ? • To try and maximise the plants production and increase the mean time between outages many industries are moved g y towards a ‘Condition Based Maintenance’ approach. • Condition Based Maintenance or CBM is an activity that attempts to predict and trend component failure non‐ intrusively given the end user valuable advanced warning of the problem at hand. • Maximising asset reliability is of the utmost importance in today’s global economy. – As competition and the pressure to produce products cheaper increases the higher consequence of machine/production failure becomes.
CBM Overview • Most machine faults generate some kind of signature that is unique to the particular fault developing. By using the correct technology to detect these signatures we can not correct technology to detect these signatures we can not only tell that a fault is developing, but distinguish what the fault type is. • There are several technologies available to help determine There are several technologies available to help determine the condition of the machine being monitored and the type of fault developing and these are: – – – –
Vibration Analysis y Tribology Sonics Thermography
Diagnosing a machine is just like a person… Di i hi i j lik
Vibration: The ‘pulse’ of the machine
Oil: The ‘life blood’ of the machine
Thermography: ‘Taking its temperature’
Total Picture
Motor Current: The ‘brain waves’ of the machine
Overview of Technologies Overview of Technologies •
Vibration Analysis –
Used to Detect, Analyse and Confirm plant machinery problems. This y p yp can be done in three ways: • • •
On‐line (4500T & CSI6500) for automated and continuous monitoring and protection of critical plant items Portable (2130 Analyser) Route based data collection and analysis Wireless used for remote monitoring of moving or inaccessible equipment
g Overview of Technologies •
Tribology –
Is the analysis of ‘interactive surfaces in relative motion’. • •
Lubricants are analysed on‐site using the 5200 mini‐lab series. The results are plotted in a simple to understand tri‐vector plot showing the ‘Chemistry’ ‘Contamination’ and ‘Wear’ of each lubricant, this allowing the lubricant to be changed on condition rather than on a time based interval lubricant to be changed on condition rather than on a time‐based interval. Wear
Contamination
Chemistry
g Overview of Technologies •
Sonics –
Through a process known as ‘heterodyning’ Ultrasonic sounds that are non‐audible to human ears are converted back down to a d bl h db kd frequency that is audible to human ears, allowing the operator to hear and recognise faults developing within plant operating systems, such as: such as: • • • • •
Mechanical – Bearings, Rubs, Gear Defects etc Electrical Defects Valve Operation Steam Trap Operation Leak Detection – Pressurised Systems and Vacuum Systems
g Overview of Technologies •
Thermography –
Thermal Imaging is used to locate potential problems by detecting g g p p y g abnormal temperature fluctuations at a glance. •
This can be used in a wide array of circumstances but is most commonly used in electrical control panels
g Overview of Technologies •
Corrective technologies allow the engineer to set‐up the machine to try and prevent premature machine failure from such causes as Imbalance and Misalignment d Mi li t – When these forces are induced upon a machine components such as bearings, seals and even supports fail due to stress – Technologies such as Laser Alignment and Balancing prevent these from being Technologies such as Laser Alignment and Balancing prevent these from being so much of a problem
y g Machinery Health Manager A1 - Recirculation Pump #5 -M2H MOTOR O O INBOARD O BRG. G - HORIZONTA O O
PK In/Sec
RCP#5 C # 0.025 0.020 0.015 0.010 0.005 0
•
PK In/Sec
ALERT
0
0.05 0.04 0.03 0.02 0.01 0 0
Acc in G-s s
Trend Display 36-65xTS
FAULT
1.0 0.5 0 -0.5 -1.0 -1.5 0
The machines due to be monitored are defined within the Each technology is stored and analysed from a single software platform, software. software allowing the analyst to: allowing the analyst to: 100
–– – – 1
200 300 Days: 11-Aug-95 To 11-Dec-96
400
500
Route Spectrum 11-Dec-96 17:33:57 OVERALL= .0604 V-DG PK = .0605 LOAD = 100.0 RPM = 3593. (59.89 Hz)
40
As much information as possible about the machines being monitored Store all data and information in one database isEasily cross reference data for conformation of analysis preferred when building the database. 80
120 160 Frequency in kCPM
200
240
Route Waveform 11 D 96 17 11-Dec-96 17:33:57 33 57 RMS = .4233 PK(+/-) = 1.13/1.22 CRESTF= 2.89
Collaborate all data into one single report Collaborate all data into one single report. 2
3 4 Revolution Number
5
6
7
Overview O i of Condition Monitoring Maintenance Philosophies Maintenance Philosophies
Definition of Maintenance Definition of Maintenance • The The act of causing to continue act of causing to continue (Webster) • Keeping equipment in repair (Oxford)
Maintenance Reactive Maintenance – Often called ‘Breakdown Maintenance’ and has the concept ‘fix it when it breaks’. breaks • This is probably the most common type of maintenance in industry today but can be the most costly, especially on critical machines. • Maintenance costs are usually higher due to the catastrophic failure that occurs.
Predictive Maintenance – Also known as ‘Condition Based Maintenance’. • This approach uses non-intru technologies to determine the actua condition of a machine and its rate of failure. • This can be very effective in extending machine life with big financial savings if implemented properly.
Planned Maintenance Also known as ‘Shutdown Maintenance’. This is based upon p ‘Timed Intervals’ between maintenance. Can be very effective if maintenance and resources are aimed at the machines that need it the most. However it can be very difficult to distinguish which machines actually need maintenance.
Proactive Maintenance Often referred to as ‘Root Cause Analysis’. This philosophy works hand in hand with Predictive Maintenance, eliminating the source of the fault to try to prevent it from re-occurring.
y Today’s Industrial Demand • It should be unacceptable to deliver – less performance for more money l f f – same performance for more money
• It could be acceptable to deliver – same performance for less money same performance for less money – more performance for the same money – more performance for more money
• The desire is More Performance for Less Money!!!!
j Predictive Maintenance Objectives
• To To confirm good confirm good‐condition condition machines machines • To detect developing problems • To determine the nature and severity of the d i h d i f h problem • To schedule repairs that can best fit with production and maintenance needs
q Predictive Maintenance Techniques
• • • • • • •
Vibration measurement Vibration measurement Electrical testing Motor current analysis l i Reciprocating machine testing Thickness testing Visual inspection Visual inspection And many more…
Predictive Maintenance Basic Facts
• Every Every mechanical or electrical faults on a mechanical or electrical faults on a machine has a distinct vibration behavior. • Any change in the vibration signature Any change in the vibration signature indicates changes in the dynamic operating condition of the machine condition of the machine
( ) Predictive Maintenance Mechanism (VA)
• Establish Establish a database of all the machines that a database of all the machines that need to be monitored • Establish a data collection route that best Establish a data collection route that best optimize the data collection time • Download route into the data collector D l d i h d ll • Collect data • Upload collected data into the database
Predictive Maintenance Mechanism
• Run Run exception reports to detect the exception reports to detect the problematic machines • Analyze only the machines in the exception Analyze only the machines in the exception reports • Generate repair work to be performed G i k b f d • Again collect data on the machine on which work is being done.
Predictive Maintenance Predictive Maintenance Rules + Experi
Start NO YES Create Ref.
Regular Meas.
Compare limits
Fault Diagnostics
Input m/c specs Create New Ref. & Limits
Fault correction
Vibration Fundamentals Vibration Fundamentals How Much Vibration is Too Much ? 1. Use Absolute Vibration Levels - Given Gi by b machine hi makers k - Published Vibration Severity Standards eg. ISO 2372, VDI 2056, BS 4675
2. Use Relative Vibration Levels
ISO 10816 3 ISO 10816‐3 11
0 44 0.44
7.1
0.28
4,5
0.18
3,5 ,
0.11
2,8
0.07
2,3
0.04
1.4
0.03
0,71
0.02
mm/s rms
rigid
flexible
rigid
flexible
pumps > 15 kW
rigid
flexible
medium sized machines
radial, axial, mixed flow
integrated driver
external driver
Group 4
Group 3
15 kW < P 300 kW
motors 160 mm H < 315 mm Group 2
rigid
flexible
inch/s rms
Foundation
large machines 300 kW < P < 50 MW
Machine Type
motors 315 mm H Group 1
Group A B
C D
newly commissioned unrestricted long-term operation restricted long-term operation vibration causes damage
ISO 10816 3 ISO 10816‐3 140
5.51
113
4.45
90
3 54 3.54
71
2.80
56
2.20
45
1.77
36
1.42
28
1.10
22
0.87
18
0.71
11
0.43
µm rms
rigid
flexible
rigid
flexible
pumps > 15 kW
rigid
flexible
medium sized machines
radial, axial, mixed flow
integrated driver
external driver
Group 4
Group 3
15 kW < P 300 kW
motors 60 mm H < 3 315 5 mm 160 Group 2
rigid
mil rms
flexible
Foundation
large machines 300 kW < P < 50 MW
Machine Type
motors 315 3 5 mm H Group 1
Group A
newly commissioned
B
unrestricted long-term operation
C
restricted long-term operation
Vibration standards are guidelines Vibration standards are guidelines
Just Tolerable Just Tolerable
Allowable
Just Tolerable All Allowable bl Allowable
Good
Good
Large Machines with rigid and heavy foundations whose G d Good natural Frequency Small 2)-S1
0.3
0.2
0.1
0 0
10
20
30
40 50 Frequency in Orders
60
70
80
Ordr: 49.00 Freq: 57551. 57551 Spec: .275 Dord: .00649
Case Study 5 Gearbox Case Study 5 –Gearbox • The waveform data is showing a distinct pattern commonly associated with gears. • The amplitude increases In noise as the damaged teeth come 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 5 Gearbox Case Study 5 –Gearbox • 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 A new gearbox was fitted and new data was taken showing the difference between the good and bad gear
Bearing Defects g Rolling Element g Plain Bearings Peakvue
Rolling Element Bearings Rolling Element Bearings • Rolling element bearings have specific bearing failure modes that can be observed in the spectral and waveform data. • Bearing frequencies differ from most other frequencies present within the spectral data because unless the bearing present within the spectral data because unless the bearing has a defect there will be no frequency peaks in the data relating to the bearing. Only if the bearing has a defect will frequencies show in the spectral data. There are four main fundamental bearing defect frequencies q these are:
g g Rolling Element Bearings
Outer Race
Inner Race
How Bearing Faults Generate Vibration g
How Bearing Faults Generate Vibration g
Rolling Element Bearings Rolling Element Bearings •
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 #B/R FTF BSF BPFO BPFI 12143 RHP 6218 11 0.418 2.967 4.598 6.402 24421 SKF 6313E 8 0.376 1.894 3.009 4.991 25372 SKF I 26313 25372 SKF I‐26313 19 0.433 3.568 8.219 10.781 19 0 433 3 568 8 219 10 781
Rolling Element Bearings Rolling Element Bearings • Characteristics of Bearing Defects – High High frequency raised noise level (Hump of energy) 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 l ti i th f d i – Fundamental bearing defect frequency (First calculable q y) y p p frequency) may not be present in the spectral data
Failure Mode 1 Failure Mode 1 •
The early stages of bearing defects produce low amplitudes of vibration at higher frequencies – (Appears on the right hand side of the spectrum). ( pp g p )
•
These are normally humps of energy or peaks that are harmonics to the fundamental frequency. – (The fundamental frequency should not be visible at this stage). i ibl hi )
Failure Mode 2 •
Distinct harmonics of Non‐Synchronous peaks appear. – (These should appear lower down the scale of the spectrum – towards the left / middle of the plot)
•
Sidebands may appear around these frequencies usually equating to turning speed. – (The fault frequencies may not match exactly with the peaks in the spectrum due to the fact that the bearing geometry will have changed) bearing geometry will have changed).
Failure Mode 3 •
The fundamental frequency normally appears at this stage – (First calculable frequency of the bearing – towards the left‐ hand side of the spectral plot). This is classed as advanced stages of bearing wear.
•
Sidebands may be visible that equate to other bearing frequencies – BSF, FTF etc). )
Failure Mode 4 Failure Mode 4 •
The bearing degrades so much that the spectrum The bearing degrades so much that the spectrum becomes a mass of noise. At this point the bearing will fail at any point (If it last this long – most fail around Mode 3).
g g BPFI Rolling Element Bearings ‐ •
Typical data showing a defected inner race – Fundamental frequency showing – Harmonics low and high frequency + sidebands
g g BPFO Rolling Element Bearings ‐ •
Data showing a defect related to the BPFO – The fundamental frequency is showing – Harmonics from low to high frequency Harmonics from low to high frequency
g g BSF Rolling Element Bearings ‐ •
Bearing defect showing the BSF – Rolling elements – Sidebands around the BSF = FTF
Rolling Element Bearings Rolling Element Bearings ‐ FTF •
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
FTF & BSF FTF & BSF
BPFI & BPFO BPFI & BPFO
Rolling Element Bearings Rolling Element Bearings ‐ Waveform • As a bearing becomes defected then the amount of noise/force generated as the rolling elements impact the de ect e a ea c eases 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
y g Case Study 6 – Bearing Defect •
The spectral plot below is showing the data from the inboard vertical direction of the motor. – The primary cursor is indicating the fundamental defect BPFO f BPFO frequency + harmonics. +h i – The frequency range of the harmonics covers both low and high frequency ranges suggesting the bearing is more advanced stages of failure.
Case Study 6 Case Study 6 – Bearing Defect Bearing Defect • The time waveform is showing significant impacting levels reaching in excess of +/‐ 8G‐s of force. – This This level of impacting is higher than would be suspected for a motor of level of impacting is higher than would be suspected for a motor of this type. •
The repetitive impacting The repetitive impacting pattern shown above is common to antifriction bearing defects. g – In this instance the impacting is representing the rolling elements striking a defect on the race.
Case Study 6 Case Study 6 – Bearing Defect Bearing Defect • The trend plot above is showing the increase in amplitude of the Peak‐Peak parameter. – The The peak peak‐peak peak parameter is measuring the amount of energy in the parameter is measuring the amount of energy in the time waveform from the Peak+ to the Peak‐ • •
Conclusion C l i The motor was reported as having a bearing defect to the engineering group. As the f d fundamental defect frequency was present t ld f tf t and the trend had shown sudden increases it was recommended to change the bearing at the next available opportunity the next available opportunity.
Bearing Defects g Rolling Element
Plain Bearings Peakvue
Plain Bearings Plain Bearings • Rotating elements are not used in sleeve (plain) bearings; rather the shaft rides on a layer of lubricating oil inside the bea g jou a bearing journal. – Therefore the fundamental frequencies seen from antifriction bearings do not apply to sleeve bearings.
• Since there is no contact between the bearing and the shaft monitoring of sleeve bearings for vibration analysis usually requires the use of displacement probes mounted 45 p p degrees either side of top dead centre.
Plain Bearings Plain Bearings • As there are no rotating components in the bearing that produce high frequency noise (force) there is no need to monitor a high frequency range. Usually 10 to 15 orders of turning speed will be sufficient. • Sleeve bearings have specific defects that contribute towards bearing failure, these are: – Excessive clearance Excessive clearance – Hydraulic instability (oil whirl)
Plain Bearings – Spectral Diagnostics • Excessive Clearance – When there is excessive clearance between the rotor and the bearing then this will have an effect on the system vibration. When the bearings have excessive clearance then a ‘looseness’ bearings have excessive clearance then a looseness occurs. occurs The spectral data shown below is indicating a sleeve bearing with excessive clearance excessive clearance. As the clearance increases then the harmonics of 1xTs will increase and can go up to 10–15xTs.
•
TBT
16
Fu - Turbine Brg Thrust End -R1Y Radial 'Y' Direction
Route Spectrum* 27-Jul-04 14:08:21
OVERALL= 2.93 V-DG P-P = 22.71 LOAD = 100 100.0 0 RPM= 941. (15.69 Hz)
– Like looseness the more harmonics there are the more severe the problem will be. – A good sleeve bearing will still show a few harmonics as there is a small clearance l between b t the th shaft h ft and d bearing
P-P Dis pla c cement in Microns
12
8
4
0 0
3
6 Frequency in Orders
9
12
Ordr: Freq: Spec:
1.000 15.68 7.494
Plain Bearings Plain Bearings – Spectral Diagnostics Spectral Diagnostics • Oil Whirl – One of the major problems encountered with these types of bearings is j p yp g the possibility of hydraulic instability of the shaft within the bearing; known as oil whirl or oil whip. – Oil Whirl is a result of turbulent flow within the oil resulting in the oil pushing the shaft around of centre. TBT
16
•
Fu - Turbine Brg Thrust End -R1Y Radial 'Y' Direction
Route Spectrum* 27-Jul-04 14:08:21
Oil Whi Whirll att 0 0.4 4 orders d
OVERALL= 2.93 V-DG P-P = 22.71 LOAD = 100.0 RPM= 941. (15.69 Hz)
P-P Dis pla c e m e nt in M ic rons
12
•
8
– This defect is sub‐synchronous data. – When the amplitude of the oil whirl is equal to or greater than the 1xTs peak a problem exists
IIn this instance oil whirl can be thi i t il hi l b corrected by: – Properly loading the bearing – Change the oil viscosity – Change the oil pressure Ch th il
4
0 0
The dominant peak within the spectral data will be typically at 0.4 orders. (.40‐ .48)
3
6 Frequency in Orders
9
12
Ordr: Freq: Spec:
1.000 15.68 7.494
Oil Whirl Oil Whirl
Bearing Defects Rolling Element Plain Bearings
Peakvue
Peakvue Processing Peakvue Processing •
The detection of bearing and gear defects is one of the primary expectations of a predictive maintenance program. – As analysts we can spend a lot of time tying to determine these faults. – Peakvue is a process that concentrates on these defects to help the analysts determine potential faults developing
•
Peakvue stands for the Peak Value and is a technique that detects high frequency stress waves generated from metal to metal contact, such as: frequency stress waves generated from metal to metal contact such as: – Bearing defects – Rotating elements striking a defect on the race – Gear defects – Damaged teeth in mesh – It is the detection of these high frequency stress waves that will aid with analysis analysis
Peakvue Processing Peakvue Processing ‐ Filters • In order to capture the stress wave signal the process requires the use of a filter to remove all unwanted noise that can do dominate the data ate t e data
1. Conventional Vibration Signals that are filtered from the Peakvue Signal Imbalance Misalignment Misalignment Gears Bearings Resonance
2. Peakvue filter removing low frequency noise from the stress wave data This is to prevent low frequency noise consuming the stress wave activity
3. High frequency stress wave activity occurring in the 1000Hz 20000Hz frequency range at a rate governed by a low frequency event Bearings Gears
Peakvue Processing Peakvue Processing ‐ Filters • There are two types of filters available • Band Pass Filters Band Pass Filters f
– The band pass filter removes all the data above and below the filter corner values
• High Pass Filter
f
– The high pass filter removes all data lower in frequency to that of the g p q y filter selection allowing only the high frequency stress waves to pass through
• After After the filtering process what should remain is the high the filtering process what should remain is the high frequency stress wave activity that is occurring at the rate of the excitation – such as from a bearing.
Peakvue Processing Peakvue Processing – Spectral Data Spectral Data • Shown below is a typical Peakvue spectrum with a defect present
Stress waves are showing clearly in the data at 4.6 Orders
•
The filter used is shown in the top The filter used is shown in the top right hand corner
Good G d Spectrum S t will ill show only a noise level
Noise removed by y filter
Peakvue Processing Peakvue Processing – Waveform Data Waveform Data • As stress waves are small in amplitude severity of the problem can be judged using the time waveform – Peak Value of force from the impact Peak Value of force from the impact
RMS Acc celeration in G-s
• The waveform can resemble a spectrum as there is no negative half to the data B42 - ZONE 5 DF FAN 1 16/16EXT01-M2P Motor Inboard Horz Peakvue
0.8 0.7
N
N
N
N
N
N
N
N
N
0.6 0.5 0.4 03 0.3 0.2
Route Spectrum 09-Jul-03 09:50:49 (PkVue-HP 1000 Hz) OVERALL= 1.37 A-DG RMS = 1.37 LOAD = 100.0 RPM = 1342 1342. (22 (22.37 37 Hz) >NTN 6217 N=BPFO -OB
For Peakvue analysis
Use the Spectrum
0.1
– Diagnose the defect
0
Acceleration in G-s
0
200
400 600 Frequency in Hz
800
1000
Route R t W Waveform f 09-Jul-03 09:50:49 (PkVue-HP 1000 Hz) RMS = 2.97 PK(+) = 8.35 CRESTF= 2.81
8 7 6 5 4 3 2 1 0 0
4
8
12
16 20 24 Revolution Number Label: Bearing Fault - BPFO NTN6217
28
32
36
Freq: 1.250 Ordr: .05587 Spec: .01367
Use the Waveform – Determine the severity
y Case Study 7 – Peakvue on Fan Bearingg • The following machine is a pre‐heater pre heater fan designed to fan designed to heat the product prior to it entering a Kiln – There is no standby for this machine – Failure results in stopped production
• The following data was taken from the above fan unit. – The problem bearing resided on the fan inboard bearing. – Data was collected on a monthly basis. Both conventional vibration data and Peakvue data were taken during the route collection.
y Case Study 7 – Peakvue on Fan Bearingg • The data shown below is taken using conventional vibration methods on the inboard bearing of the fan – 1x peak is highlighted showing amplitudes of 4mm/sec 1x peak is highlighted showing amplitudes of 4mm/sec – Waveform is showing less than 1G of force both +/‐ 40 - Preheater Fan M4425 -F1H Fan Inboard Horizontal
R M S Ve lo c it y in m m /S e c
5
Route Spectrum 29-Oct-02 11:19:26 OVERALL= 4.18 V-DG RMS = 4.18 LOAD = 100.0 RPM= 825. (13.75 Hz)
4 3 2 1 0 0
10
20
30 40 50 Frequencyin Orders
60
70
80
A c c e le ra t io n in G - s
1.5
• There There are indications of are indications of bearing frequencies showing high frequency Route Waveform 29-Oct-02 11:19:26 RMS = .3837 PK(+/-) = 1.19/1.05 CRESTF= 3.11
10 1.0 0.5 0 -0.5 -1.0 10 -1.5 0
1
2 3 Revolution Number
4
5
Ordr: 1.000 Freq: 13.75 Spec: 3.721
– These may be missed due to y the amplitude of the 1x peak
y Case Study 7 – Peakvue on Fan Bearingg • The Peakvue data above is taken from the same point as the previous data.
R M S A c c e le ra t io n in G - s
– This particular reading is using a 1000 Hz High Pass filter. This particular reading is using a 1000 Hz High Pass filter. 40 - Preheater Fan M4425 -F1P Fan Inboard Horz Peakvue
0.7 0.6
F
F
F
Route Spectrum 29-Oct-02 29 Oct 02 11:15:59 (PkVue-HP 1000 Hz) OVERALL= 1.10 A-DG RMS = 1.10 LOAD = 100.0 RPM= 830. (13.84 Hz) >SKF 22240CC F=BPFO -IO
F
0.5 0.4 0.3 0.2 01 0.1
•
– This is not non‐synchronous d data and the frequency d h f matches that of the BPFO for the bearing.
0 0
5
10
15 20 25 Frequency in Orders
30
35
40
A c c e le ra t io n in G - s
8 7
Route Waveform 29-Oct-02 11:15:59 (PkVue-HP 1000 Hz) RMS = 3.31 PK(+) = 7.47 CRESTF= 2.25 DCoff = -3.08
6 5 4 3 2 1 0 0
10
20 30 Revolution Number
40
50
Ordr: 8.176 Freq: 113.14 Spec: .194
Here the data is showing there H th d t i h i th is stress wave activity at 8.176 orders.
•
The waveform data is measuring over 7 G‐s of force i 7G ff as oppose to the 1G from the previous data.
Case Study 7 – Peakvue on Fan Bearing • Conclusion • There is significant bearing damage relating the outer race of There is significant bearing damage relating the outer race of the bearing. • As the machine was critical to the process, the bearing was changed on the next available opportunity that tied in with process requirements.
Electrical Defects Electrical Defects
Electrical Defects Electrical Defects • A motor can be simply broken down into two key components – Rotor – Stator
•
The stator is stationary The stator is stationary – Consists of wire wound in coils and placed in slots of an iron core. – The stator produces a rotating magnetic field.
The rotor is not stationary – 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.
Electrical Defects Electrical Defects – Spectral Data Spectral Data • The most common electrical frequency that materialises in the spectral data is the 2 x Line Frequency. – For For most industrial applications the line frequency used to supply most industrial applications the line frequency used to supply motors is 50Hz (Europe). – Therefore the frequency of concern for most electrical faults would be 100Hz (2xLf [Lf=line frequency]) Ex7
0.6
Ex7 - Example 7 -M1H Motor Outboard Horizontal Route Spectrum 08-Nov-00 14:27:35 OVERALL= .5613 V-DG RMS = .5607 LOAD = 100.0 RPM== 2967 RPM 2967. (49 (49.44 44 Hz)
RMS V Ve loc it y in mm /Se c
0.5
0.4
• The spectral plot is showing a peak at 100Hz showing a peak at 100Hz (6000cpm) – 2xLf – This can be mistaken for misalignment
0.3
0.2
0.1
0 0
500
1000 Frequency in Hz
1500
2000
Freq: 100.00 Ordr: 2.023 Spec: .386
Electrical Defects Electrical Defects – Waveform Data Waveform Data • The waveform data from a 100Hz peak will show a sinusoidal pattern like the waveform shown below Ex7
1.5
Ex7 - Example 7 -M1H Motor Outboard Horizontal
10 1.0
RMS = .5291 LOAD = 100.0 RPM= 2967. (49.44 Hz)
0.5 Ve loc it y in m m /Se c
• Again this type of pattern can be associated with can be associated with misalignment.
Route Waveform 08-Nov-00 14:27:35
PK(+) = 1.50 PK(-) = 1.77 CRESTF= 3.31
0
-0.5
-1.0
-1.5
-2.0 0
1
2
3 Revolution Number
4
5
6
– Usually misalignment would produce higher force (Higher waveform levels) than those from electrical defects due to the stress being applied to the stress being applied to the machine
Electrical Defects 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 I l i B kd I b l d Ph • Loose Connectors
Electrical Defects ‐ Peakvue • Peakvue data also shows electrical defects at the 2xLf peak. – This may be due to the rotor or stator bowing; due to heat build up. y g p
• The spectral plot below is indicating a 100Hz peak using Peakvue with a 1000Hz filter.
Case Study Case Study – Electrical Defect Electrical Defect • The following case study was taken from a glass manufacturer. The data was from the ‘Electric Front Wall Cooling Fan’. – This This fan unit is a critical fan to the process and has no standby unit. 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.
•
The Peakvue data taken on the motor non‐drive end is showing a dominant 100Hz showing a dominant 100Hz peak. – This frequency is at 2xLf and is associated with electrical problems
Case Study Case Study – Electrical Defect Electrical Defect • 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. This particular plot is useful for determining rate of change. – It is quite clear how this particular frequency suddenly appeared
•
Conclusion – As As the motor failed shortly after 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.
Belt Defects Belt Defects V‐Belts V Belts Timing Belts
Belt Defects 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 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
• However, belts can be easily damaged by heat, oil and grease and since belts slip with in the sheaves they can not b be used where exact speed changes are required (except for d h t d h i d( tf timing belts)
Belt Defects Belt Defects • Belt defects can be considered non‐critical faults by many maintenance groups due to the relative ease of replacement requiring minimum downtime. equ g u do t e – But belt defects are a major contributor to the overall vibration of the machine resulting in premature failure of other machine components.
Sources of belt drive defects Poor Maintenance Enviromental Factors Poor Installation Poor Design g Other Defects
Belt Defects Belt Defects – Belt Types Belt Types • There are many different types of belt drive systems. This section covers the most commonly used types of belt in industry today. dust y today • •
V‐Belts V‐belts are the most common type of belts used. They are ‘V’ shaped in cross‐section cross section, this allowing the belt to wedge against the side of the this allowing the belt to wedge against the side of the sheave. – This design allows the belt to be run faster than most other type of belt applications with power transmission efficiencies as high as 95%
Belt Defects • Timing Belts • These are flat belts with equally spaced teeth that mesh These are flat belts with equally spaced teeth that mesh with notches on the pulley. Timing belts are different from other belt drives as they do not induce any slip. – M Most commonly used where constant velocity and strict timing l d h l i d i i i application is required.
Belt Defects Belt Defects – Fault Characteristics Fault Characteristics • Belt defects, such as cracks, broken or missing pieces, hard and soft spots can generate vibration at the turning speed of the belt (1xbelt) and harmonics – Due to the length of the belt in relation to the pulleys (sheaves) the 1xbelt frequency is sub‐synchronous 1xbelt frequency is sub synchronous and very often the 2xbelt and very often the 2xbelt frequency may be sub‐synchronous as well
• 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 Belt Defects – Fault Characteristics Fault Characteristics • Just like two mating shafts, belt drive systems can also be misaligned in both angular and offset directions. – When When misalignment is induced into a belt drive system then the life of misalignment is induced into a belt drive system then the life of the belt is significantly reduced as well as the overall vibration of the system increases.
Offset Misalignment Angular Misalignment
• Pulley misalignment results in high axial vibration at the shaft turning speed. – If If the belt is also defected then 1xbelt frequency and harmonics may the belt is also defected then 1xbelt frequency and harmonics may also show in the axial direction
Belt Defects Belt Defects – Calculations Calculations • 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 Belt Defects – Calculation Example Calculation Example • • • •
Belt Frequency Calculation Belt Frequency = (3.142 Belt Frequency (3.142 * 1480 1480 * 300) / (2000) 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 Belt Defects – Spectral Data Spectral Data • The spectral data above is data taken of a motor from an Air Handling Unit. – The The frequency highlighted by the primary cursor is showing the 1xTs of frequency highlighted by the primary cursor is showing the 1xTs of the motor (1 Order) •
1 x Belt Frequency showing with harmonics Dominant 2 x Belt Frequency
There are a lot of sub‐ synchronous peaks showing in this data this data. – The first peak is the fundamental frequency of the belt rotation. – The second peak is the 2xbelt The second peak is the 2xbelt frequency suggesting there is damage to the belt – As the harmonics of the belt increase in number they surpass the 1xTs of the motor surpass the 1xTs of the motor and in this case the third harmonic becomes non‐ synchronous data.
Case Study 9 Case Study 9 – Belt Defect Belt Defect • The following data was taken on an Air Handling Unit. The Air Handling Unit is a supply fan from shared services. This is a sta d a o e u t t o sta d by capab ty stand alone unit with no stand by capability BL31 - 559 AHU Supply -M2H Motor Inboard Horizontal
559S
0.5 J
J
J
J
J
J
J
J
J
Route Spectrum* 22-Feb-05 13:53:33
J
OVERALL= 1.22 V-DG RMS = .7701 LOAD = 100.0 RPM = 1272. (21.21 Hz)
0.4
>Belt Freqs J=Belt 1 Freq
0.2
0.1
x - Fa n spee d
0.3
X - M ot or s p e e d
RM S Ve loc ity in mm /Sec
•
•
0 0
•
4000
8000 Frequency in CPM Label: Belt defect/worn belts & sheaves
12000
16000
Freq: q 835.69 Ordr: .657 Spec: .04393
The data shows the motor turning speed t t i d along with a sub‐ synchronous peak of the belt frequency. The primary cursor is The primary cursor is highlighting the 1xbelt with several harmonics. The 2xbelt is very The 2xbelt is very dominant suggesting there is damage to the belts.
Case Study 9 Case Study 9 – Belt Defect Belt Defect • As this is a critical machine it was recommended on the next available opportunity that the belts needed to be checked for da age a d e a g ed damage and re‐aligned.
• •
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. Both the belts and pulleys were replaced and correctly aligned before re‐starting the machine.
Resonance
Resonance • Resonance is defined as: An excitation of a natural frequency by a periodic forcing excitation of a natural frequency by a periodic forcing function. • All assets contain natural frequencies that vary depending upon the stiffness and mass. – Resonance Resonance can be considered to be a vibration amplifier, that takes the can be considered to be a vibration amplifier that takes the force level of the periodic forcing function and amplifies it; which significantly increases the movement of the asset.
If Vibration is a Fire The Resonance is a Fuel If Vibration is a Fire, The Resonance is a Fuel
Example of Resonance Example of Resonance •
The example shown represents the effect on amplitude of the forcing function when in resonance. – In plot 1 the 1xts is running below the natural frequency (Fn). – Fn can be seen in plot 2. – Plot 3 shows the increase in amplitude of the forcing function when run at the natural frequency – t lf thi i this is resonance Before Excitation
1 Frequency
Resonance Curve
2
Frequency
Amplified Signal
3 Frequency
Resonance •
There are two factors that determine the natural frequency of an asset these are; 1. Mass – The heavier an object the lower the natural frequency 2. Stiffness – The more rigid a structure the higher the natural frequency
• Resonance is becoming more of a problem in industry in ece t yea s due to recent years due to: – Older equipment having to run faster to meet current production demands (often above what it was designed for) – Equipment is being built cheaper and lighter
• This is resulting in amplification of the forcing function creating excessive machine movement resulting premature machine failure.
Effects of Resonance • The ODS data is showing a steel frame structure deflecting at one corner in the vertical direction due to a resonant co d t o condition.
Characteristics of Resonance Characteristics of Resonance • Characteristics of Resonance – Resonance is very directional in nature (Movement may be greater in y ( y g one plain than the other) – Vastly different amplitudes of the forcing function from one direction to the other (between Horizontal and Vertical – Rule of thumb ratio is 3:1 difference) – Resonance is very speed sensitive (small changes in speed can show large differences in amplitude of the forcing function) – Resonance can occur at any frequency but most commonly associated with the 1xTs
Resolving a Resonance Resolving a Resonance • There are a number of alterations to the system that can be made to resolve a resonance condition. – However if structural changes are to be made you need to be careful you don’t excite another natural frequency once the change has been made?
• Once you are sure you have a resonant condition it can be corrected by one of the following methods: – Change the Mass Ch th M – Change the Stiffness – Remove the forcing function – Dampen the structure Dampening is a method used to convert mechanical energy into thermal energy. It does not remove the resonant condition only gy y controls the amount of movement.
Resonance Resonance – Spectral Data Spectral Data The spectrum is showing the 1xTs peak of the motor with amplitudes reaching 19mm/sec.
•
– This is high for the 1xTs.
Very often this type of data can be mistaken for Imbalance as this defect can also produce a high 1xTs peak.
•
– However Imbalance is a centrifugal force and should show similar amplitudes in both radial plains where as resonance is very directional. 40 - No 1 GCTCompressor M4551 -M2H Motor Inboard Horizontal
27
Route Spectrum 13-Feb-03 10:14:46
24 OVERALL= 19.95 V-DG RMS = 19.85 LOAD = 100.0 RPM= 1484. (24.73 Hz)
R M S Ve loc it y in m m /Se c
21
18
15
In order to help resolve this issue we need to check the amplitude of the 1xTs 90 degrees to this point (horizontal to vertical) – This can easily be done by using the ‘multi point plot’ in the software
12
9
6
3
0 0
•
500
1000 Frequency in Hz
1500
2000
Freq: 24.72 Ordr: 1.000 Spec: 19.50
Resonance Resonance – Multi Plot Multi Plot The multi point plot allows the analyst to display several measurement points on the same plot. Here we are showing all the radial points from the motor. motor – It is very clear that the amplitudes of the 1xTs peak are excessive in the horizontal direction when compared to the vertical. This is a characteristic of a resonant condition. 40 - No 1 GCTCompressor GCTCompressor M4551
- Multiple Points (13-Feb-03)
24 20
Max Amp 22.0
16 12 8
R M S Vee lo c it y in m m /S e c
•
4 0 M2V 10:15
M2H 10:14
M1V 10:14 Point= M2H 13-Feb-03 10:14:46 RPM= 1484. M1H 10:14 0
500
1000 Frequency in Hz
1500
2000
Freq: Ordr: Sp 3:
25.00 1.011 19.35
Case Study 10 Case Study 10 – Resonance • The following case study is taken from a motor and a reciprocating compressor. The unit is mounted on a steel frame which, in turn sits on spring mounts designed for dampening c , tu s ts o sp g ou ts des g ed o da pe g • Recently the motor had been replaced due to bearing defect; however the new motor was smaller and lighter but defect; however the new motor was smaller and lighter but delivered the same power as the previous motor. • When the compressor was put back into service it was noted there was excessive vibration coming from the unit. The unit was left to run like this for several months until the vibration became to excessive vibration became to excessive.
Case Study 10 Case Study 10 – Resonance • Data was taken across the unit using route based data collection. CP1
60
SL - Compressor -M1H Motor Outboard Horizontal Route Spectrum 02-Feb-04 15:09:54 OVERALL= 45.58 V-DG PK = 45.32 LOAD = 100.0 RPM= 1490. (24.83 Hz)
PK Velocity in mm/Sec P
50
40
30
20
10
0 0
300
600
900 Frequency in Hz
1200
1500
1800
Freq: Ordr: Spec:
24.83 1.000 45.19
• The plot above is taken from the motor showing a 1xTs peak in excess of 40mm/sec in excess of 40mm/sec.
Case Study 10 Case Study 10 – Resonance • This data is very high in amplitude. • The data was then displayed in a multi plot format to show The data was then displayed in a multi plot format to show how the amplitude was across the radial plains. • Due to the vastly different amplitudes at the 1xTs frequency the defect on this motor was Resonance. CP1
SL - Compressor - Multiple Points (02-Feb-04)
50
Max Amp 44.1
40
30
PK V elo ccity in m m /Sec
Amplitude differences between radial plains
20
10
0 M2H 15:26
M2V 15:26 0
4000
8000 Frequency in CPM
12000
16000
Case Study 10 Case Study 10 – Resonance • Recommendation • It was determined that the change in motor size may be the It was determined that the change in motor size may be the cause of the resonance as the mass had been altered. A visual inspection of the frame work also revealed that one of the support beams had cracked along the weld this altering the support beams had cracked along the weld – this altering the stiffness of the structure. The support was welded and strengthened and more data was acquired to determine if any effect on the resonance had occurred.
Case Study 10 Case Study 10 – Resonance • The spectra, shows the ‘Before’ and ‘After’ plot of the motor inboard horizontal. It shows a significant drop in amplitude of the 1xTs peak. – By stiffening the structure the natural frequency had increased moving it away from the 1xTs peak thus resulting in a significant drop in it away from the 1xTs peak thus resulting in a significant drop in amplitude. CP1
SL - Compressor -M2H Motor Inboard Horizontal
50
Max Amp 44.1
40
PK Ve lo c it y in m m /Se c
30
20
10
0 07-May-04 10:08:05
02-Feb-04 15:26:38 0
1000
2000 Frequency in Hz
3000
4000
Summary of Faults Summary of Faults
Belt Frequency
Misalignmeent
Electrical Imbalance
Resonance
Looseness Electrical
Advanced Bearing Wear
Lower Gearmesh G Severe Frequencies Misalignment Severe Looseness
c c
c
c
Early Bearing Wear Gearmesh Frequency Electrical Slot Pass Frequency
c c c
F re q u e n c y In T e rm s Of RPM
M o s t L ik e ly C a u s e s
1 x RPM
U n b a la n c e
2 x RPM
M e c h a n ic a l Loosenes s
3 x RPM
M is a lig n m e n t
Le s s th a n 1 x RPM
O il W h irl (le s s t h a n 1/ 2 R P M
S y n c h ro n o u s (A . C . L in e F re q u e n c y ) 2 x S ynch. F re q u e n c y M a n y T im i es RP M (H a rm o n ic a lly R e la t e d F re q . )
E le c t ric a l P ro b le m s T o rq u e P u ls e s B a d G e a rs A e ro d y n a m ic F o rc e s H y d ra u lic F o rc e s M e c h a n ic a l L o o s e n e s s
R e c ip ro c a t in g F o rc e s H ig h F re q u e n c y B a d A n t ii-F F ric t io n (N o t H a rm o n ic a lly B e a rin g s R e la t e d )
O t h e r P o s s ib le C a u s e s & R e m a rk s 1 ) E c c e n t ric jo u rn a ls , g e a rs o r p u lle y s 2 ) M is a lig n m e n t o r b e n t s h a ft - If h ig h a x ia l vib ra t io n 3 ) B a d B e lt s - If R P M o f b e lt 4) Res onanc e p ro c a t in g fo rc e s 5 ) R e c ip 6 ) E le c t ric a l p ro b le m s 7) Loosenes s 8 ) D is t o rt io n - s o ft fe e t o r p ip in g s t ra in 1 ) M is a lig n m e n t - if h ig h a x ia l vib ra t io n 2 ) R e c ip ro c a t in g fo rc e s 3) Res onanc e 4 ) B a d b e lt s - if 2 x R P M o f b e lt U s u a lly a c o m b in a t io n o f m is a lig n m e n t a n d e x c e s s ive a x ia l c le a ra n c e s (lo o s e n e s s ). 1 ) B a d d rive b e lt s 2 ) B a c k g ro u n d vib ra t io n 3 ) S u b -h a rm o n ic re s o n a n c e 4 ) " B e a t " V ib ra t io n C o m m o n e le c t ric a l p ro b le m s in c lu d e b ro k e n ro t o r b a rs , e c c e n t ric ro t o r u n b a la n c e d p h a s e s in p o ly -p h a s e s y s t e m s , u n e q u a l a ir g a p . R a re a s a p ro b le m u n le s s re s o n a n c e is e x c it e d G e a r t e e t h t im i es RP M of bad gear N u m b e r o f fa n b la d e s t im e s R P M N u m b e r o f im p e lle r va n e s t im e s R P M M a y o c c u r a t 2 , 3 , 4 a n d s o m e t im e s h ig h e r h a rm o n ic s if s e ve re lo o s e n e s s 1 ) B e a rin g vib ra t io n m a y b e u n s t e a d y - a m p lit u d e a n d fre q u e n c y 2 ) C a vit a t io n , re c irc u la t io n a n d flo w t u rb u le n c e c a u s e ra n d o m , h ig h fre q u e n c y vib ra t io n 3 ) Im p ro p e r lu b ric a t io n o f jo u rn a l b e a rin g s (F ric t io n e x c it e d vib ra t io n ) 4 ) R u b b in g
Useful References • • • •
Simplified Handbook of Vibration Analysis Volume 1 – Arthur R. Crawford Simplified Handbook of Vibration Analysis Volume 2 – Arthur R. Crawford BS ISO 13373‐1 2002 – Condition Monitoring and Diagnostics of Machines – General Procedures BS ISO 13373‐2 BS ISO 13373 2 – Condition Monitoring and Diagnostics of Condition Monitoring and Diagnostics of Machines – Processing, Presentation and Analysis of Vibration Data
