Time Waveform Analysis

TIME AND FREQUENCY ANALYSIS

TIME DOMAIN ANALYSIS

Machinery vibration analysis techniques

Use of time domain analysis

Time domain analysis Frequency analysis

Signal processing and presentation Phase measurement

Demodulation

Instrument setup Time waveform shape analysis Synchronous vs nonsynchronous data Random noise and vibration Conclusions

USE OF TIME DOMAIN ANALYSIS A graphic description of the overall physical behavior of a vibration structure as a function of time Clarification of FFT processed data The position of the measurement point at each instant of time relative to the position at rest Overall peak amplitude

USE OF TIME DOMAIN ANALYSIS (cont.) The symmetry of a signal; this relates to the linearity of the vibrating system, the nature of the forcing function, and the severity of the vibration A measure of damping in the system Direction of the initial exciting force

Phase and amplitude relationships of different frequencies and different positions The nature of amplitude modulation or frequency content

SIGNAL PROCESSING and PRESENTATION Instrumentation Presentation Presentation setup Differential time

12-1

Waveform Characteristics Objectives • Describe five waveform characteristics. • Identify waveform symmetry using APD. • Discuss waveform modulation and how it translates to the FFT.

1

12-1

Waveform Characteristics

• A number of different displays "averaging modes" use the time domain. Displays such as synchronous time averaged data is averaged in the time domain. • APD (Amplitude Problability Distribution) this is a function of Wavepak, displays the symmetry and skewness of the waveform signal. • Each defect type has a characteristic waveform, which subsequently translates to the frequency domain. • There are characteristics and specific events that do not translate to the frequency domain as discrete peaks. In order to truly understand this limitation, the analyst must first understand how the time domain data is gathered and transformed into a spectrum through the Fast Fourier Transform (FFT) process.

12-2

Waveform Characteristics • Time domain data, raw transducer output, signal voltage and many other terms refer to waveforms. • Waveform or time domain data is comprised of amplitude with respect to time. Signals with an amplitude, whether vibration, current, voltage changes, or other signal types, change with time.

12-2

Waveform Characteristics

Waveform Characteristics

There are certain things to look for when conducting waveform analysis, the waveform provides specific characteristics for defects of a single or multiple nature. BAL - C-20 FLOAT ROLL FAN 8

C-20 FLOAT-FIH FAN BEARING INBOARD HORIZONTAL Waveform Display 25-APR-96 09:36

6

RMS = 1.28

Acceleration in G-s

4

LOAD = 100.0 RPM = 3550.

2

RPS = 59.17

0

PK(+) = 6.94 PK(-) = 5.84

-2

CRESTF= 5.40

-4 -6 -8 0

20

40

60

80

100

120

12-3

140

Time: 135.09 Ampl: .00000

Time in mSecs

Note: The waveform is only as good as its definition. If the resolution of your waveform lacks definition, the data can be worthless, or poor at best. • Once the characteristics have been properly identified, the analyst can rule out certain fault types. • For example: • If a waveform is periodic (sinusoidal) • looseness • cracks • resonance • antifriction bearings Could probably be ruled out. You may not know what the problem is, but you know what it is not.

12-3

Waveform Characteristics Listed below are waveform characteristics an analyst should look for when analyzing the waveform: • Amplitude • Periodic • Asymmetry • Spikes/Impacts • Electrical vs Mechanical • Non-Periodic • Distortions • Modulation

• Complexity • Discontinuities • Low Frequency Events • Truncation/Restrictions to Motion

12-4

Amplitude • When diagnosing machinery faults using the time waveform, similar to spectral data, we are concerned with the amplitude of the waveform. • When we are discussing bearing and gear waveforms, we use the peak to peak amplitude of the waveform. This is often referred to as g swing. • The g swing is the sum of the absolute value of the maximum positive and negative amplitude in that period. • MasterTrend calculates this value and gives us the ability to trend and alarm based on this and other waveform values.

2

12-4

Amplitude

12-5

Periodic C-20 - C-20 FLOAT ROLL FAN

C-20 FLOAT-FIH FAN BEARING INBOARD HORIZONTAL

8

RMS = 1.28

4 Acceleration in G-s

• Sometimes referred to as a deterministic simple signal, this is an ideal signal which repeats itself exactly after a fixed period.

Waveform Display 25-APR-96 09:36

6

2

FAULT

LOAD = 100.0

ALERT

RPM = 3550. RPS = 59.17

0

• This is not possible in the real world. However, there are some machinery faults which have this characteristic.

PK(+) = 6.94 PK(-) = 5.84

-2

CRESTF= 5.40

ALERT -4

FAULT

• A single plane balance problem will have a very periodic waveform due to the mass rotational center and the rotor shaft of other component center line differences.

-6 -8 0

20

40

60

80

100

120

140

Time:

76.72

Ampl: -.109

Time in mSecs

12-5

Periodic

12-6

Complexity BAL - ZONE 6 EXHAUST C-30 Z6X -FOH FAN BEARING OUTBOARD HORIZONTAL

0.6

Waveform Display

• To determine the complexity of the waveform, establish whether the signal is: • periodic in nature • estimate the harmonic content • determine if the signal is synchronous • non-synchronous • identify whether the waveform correlates directly to the spectral data.

13-JUN-95 14:52 0.4

Acceleration in G-s

RMS = .1390 LOAD = 100.0

0.2

RPM = 1000. RPS = 16.67

-0.0

PK(+) = .3672 PK(-) = .4322 CRESTF= 3.11

-0.2

-0.4

-0.6 0

100

200

300

400

500

Time in mSecs

12-6

Complexity

Impacts/Spikes C-20 - C-20 FLOAT ROLL FAN

C-20 FLOAT-FOH FAN BEARING OUTBOARD HORIZONTAL

6

Waveform Display 25-APR-96 09:37

4

Acceleration in G-s

RMS = 1.59 LOAD = 100.0

2

RPM = 3508. RPS = 58.47

0

PK(+) = 4.88 PK(-) = 5.25 CRESTF= 3.30

-2

-4

-6 0

20

12-7

40

60

80

Time in mSecs Label: LOOSE, OUT OF BALANCE

100

120

140

• Impacts or Spikes may or may not be repetitive in nature. • The non repetitive spikes generate white noise. • Repetitive impacts or spikes, such as those produced by rolling element bearing defects or broken gear teeth, may excite discrete frequencies and therefore show up well in the spectrum. • This characteristic is best detected by defining a waveform amplitude type in acceleration. Acceleration data is proportional to force. • The crest factor, which is equal to the maximum peak (positive or negative) divided by the RMS of the waveform, is a good indicator of the impacting. This value can be setup as an analysis parameter and trended in MasterTrend.

3

12-7

Impacts/Spikes

12-8

Discontinuities • This characteristic is usually associated with faulty equipment due to the discontinuous nature of the data. • Data with this characteristic has breaks in the data where there appears to be a loss of input signal or a significant increase/decrease in amplitude. • This is not a uniform change such as resonance, load changes, or even sudden component failures. • Discontinuous data is typically unpredictable, and very distinct. • If you see this type of waveform pattern ( YOU HAVE A PROBLEM )

Repetitive Spikes

12-8

Discontinuities

12-9

Asymmetry • Asymmetry refers to the relationship between the positive and negative energy. • A waveform is asymmetric when there is more energy in the positive plane than the negative or vice versa. • Asymmetry refers to the direction of movement relative to the transducer mounting with a positive signal representing energy into ( towards ) the accelerometer and a negative signal representing away. • A tool which is designed to check this type of characteristic is the APD, Amplitude Probability Distribution.

12-9

Asymmetry

Asymmetry MISC - #1 H2O BOOSTER

4661

2.5

-MIV MOTOR INBOARD VERTICAL Waveform Display

2.0

16-NOV-95 10:18

1.5 Acceleration in G-s

12-10

Select the Analyze Data feature in Diagnostics Plotting when in Waveform Analysis.

RMS = .5155 LOAD = 100.0

1.0

RPM = 1789. RPS = 29.82

0.5

PK(+) = 2.24

0

PK(-) = 1.43 CRESTF= 4.35

-0.5 -1.0 -1.5 -2.0 0

60

120

180

240

300

Time in mSecs Label: LOOSE BASE

4

12-10

APD

12-11

APD Amplitude Probability Distribution • An APD or Amplitude Probability Distribution is similar to a Hystorgram. • The signal is broken down into amplitude percentages, and then the amplitude is plotted. • The X-Axis is the amplitude and the Y-Axis is the percentage of the signal that falls into that amplitude range. • The APD is typically used for acoustical analysis. It can also be used for machine vibration analysis to find the balance of the signal (asymmetries), the direction, and possibly the location of a specific defect especially those that may not stand out in the waveform or the spectrum.

12-11

Sinewaves

12-11

Sinewaves

• Sinewaves are very symmetrical, which means there is a balance of energy in the positive and negative planes. • If most of the vibration signal is evenly distributed and sinusoidal, there is a strong possibility it is due to a synchronous component such as imbalance, misalignment, gears, blades, etc.

• Notice that the APD at the bottom of the above display shows a set of peaks at the maximum and minimum amplitude locations. • This could also be called a Hysteresis display. The probability of the signal being in the ± 10 volt location is much more probable that the signal being at the zero location of the display.

• The waveform and APD show the shape of a sinewave and the probability related to this type of signal.

12-13

Triangle Wave

12-13

Triangle Wave

• With a triangle wave, we see the relationship of the waveform and a different type of APD display. • Note that the data is skewed to the negative plane. Again, this provides the analyst with the direction of motion. The following illustration displays the direct relationship between the waveform and the APD. Bear in mind that the APD provides another tool to determine location, direction, and asymmetry.

5

12-14

Squarewave

12-14

Squarewave

• The squarewave on the next slide provides some insight into the use of the APD for checking asymmetries. Remember that symmetry refers to the balance of energy. Therefore, with a slightly more complex signal, this becomes more important especially when performing Root Cause Failure Analysis(RCFA). • In the next illustration, the signal is asymmetric, and there is more energy in the positive plane than the negative. • The energy in the positive plane shows movement toward the transducer, and the negative plane is obviously the opposite.

12-15

Truncation/Restrictions to Motion

12-15

Modulation

• Truncation means to abruptly shorten, or to appear to terminate. • In waveform analysis, this characteristic indicates restrictive motion.

• All the waveform characteristics up to this point have dealt with signals of a constant amplitude. • A varying signal will cause the waveform to become modulated. The type of modulation occurring determines its classification. Commonly referred to as Beat frequencies, these may be broken into three specific categories. • Amplitude • Beating • Frequency

12-16

Amplitude • The spectrum will have a peak at the signal's frequency with one peak on each side spaced at the frequency of the amplitude change. These peaks are referred to as sidebands. • Amplitude modulation is common when analyzing inner race bearing defects. This occurs when the defective bearing component passes in and out of the bearing load zone. The middle of the load zone is typically where the highest amplitudes in the waveform show up.

12-16

Amplitude

•The spectrum and waveform show slot pass frequency from an AC induction motor. The primary signal at 34xTS is marked with a vertical line. The sideband cursors mark the amplitude change at 120 Hz.

6

12-17

Beating

12-17

Beating AMGL - CENTAC 3 STAGE COMPRESSOR PK Vel in In/Sec

• A beat is comprised of two unrelated single frequency signals, closely spaced in frequency. • Beating is often found in two pole induction AC motors. The close proximity of two times line frequency and the second harmonic of turning speed cause this beat. • An example of beating is shown next. The 2x RPM and 2x line frequency are separated by less than .5 Hz. The waveform shows the amplitude modulation associated with beating.

#1 CENTAC -1BA MOTOR OUTBD AXIAL TO 200 Hz

0.04

REFERENCE SPECTRUM

0.03

06-DEC-94 10:15 OVRALL= .0581 V-DG

0.02

PK = .0380

0.01

LOAD = 100.0 RPM = 3575.

0

RPS = 59.58

60

80

100

120

140

160

180

Acceleration in G-s

Frequency in Hz 0.10 0.05

WAVEFORM DISPLAY 06-DEC-94 10:15

-0.00 -0.05 -0.10 -0.15 -0.20

RMS = .0678 PK(+) = .1300 PK(-) = .1790 CRESTF= 2.64 0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Time in Seconds

Freq: Ordr: Dfrq:

12-18

Frequency

1.997 1.000

12-18

Frequency

• Rarely seen in a routine environment, this is a change in frequency without a change in the signal amplitude. Frequency modulation typically occurs in gearmeshing vibration, due to the small speed fluctuations caused by tooth spacing errors and faults as they develop. A very wide spread of sidebands in the spectrum is usually an indication that significant frequency modulation is present. • On our example shown next. The vertical line in the spectrum marks gearmesh frequency at 24xTS. The sideband cursors mark the output shaft speed with labels identifying sidebands spaced at input shaft speed. The waveform has been expanded to show the frequency modulation occurring. A good illustration is shown between 170 and 180 msecs.

Frequency modulation

12-19

Low Frequency Events

119.00

Spec: .02520

12-20

Low Frequency Events

When performing detailed analysis, you need to be able to collect and analyze data in excess of one minute for low frequency problems. This is extremely important when the machine in question has an operational speed below 200 RPM. The challenge in identifying low frequency defects is having sufficient time in the waveform. A low frequency event may only appear once in the collected time domain. As discussed earlier, this event will not be transformed into the spectrum.

7

12-20

Electrical vs. Mechanical

12-20

Electrical vs. Mechanical

• Determining if the source of energy is mechanical or electrical is sometimes difficult. • Appropriately set up waveforms can be a great help. Setting up for a long enough time to capture the operational conditions and the machine shutoff point can identify the source. • The advantage of using the time domain as opposed to the frequency domain is there is no need to worry about the screen update time or sampling rate.

Waveform and Spectrum Relationships

12-21

Waveform and Spectrum Relationships

12-21

• Each spectrum has an associated waveform. The spectrum is made of this waveform. As discussed earlier in this section, some of the characteristics in the waveform do not translate to the FFT due to the way the calculations are made. The assumption is that there is a repetitive cycle of events made up of sines and cosines. However, this is not actually the case. • If an event happens only once, then this event has no frequency; therefore, the spectral representation is a continuous spectrum. • In the waveform shown next, there is no repetition in the event; therefore, there is no frequency.

12-22

Modulated Waveforms

12-22

Modulated Waveforms

• Finally, when modulation is involved, there is a direct relationship between the waveform and the spectrum depending on the differential time (∆t). • Knowledge of the modulation ∆t helps determine the resolution required for detailed spectral analysis. Also, from our previous discussion on modulation, we know there is a carrier frequency that the modulation must follow. Gears, bearings, and electrical defects each have carrier frequencies. For gears the carrier is the frequency where the gears mesh. However, a carrier frequency for an electrical defect could be the line frequency (FL ) or 2 * FL.

8

12-23

Waveform Analysis As Confirmation • Every fault condition has a corresponding waveform characteristic. • Unbalance, for example, has a sinusoidal pattern with one major event per revolution. • Misalignment, which is primarily offset, typically has harmonic activity with the waveform having the same number of events per cycle as the spectral data has peaks. A misalignment condition generating a second and possibly a third order peak shows two or three sinewaves per revolution. • Looseness will have a complex waveform with many peaks within one revolution. This will confirm the spectral characteristics of multiple harmonics of turning speed.

12-25

Vertical Turbine Pump Unbalance Example

• The multiple point spectrum plot below shows radial and axial measurements taken from the top of the vertical motor. FWEL - FRESH WATER BOOSTER PUMP 1

MIV

FWEL - FRESH WATER BOOSTER PUMP 1

Single Spectrum - Amplitude Relations

PK Velocity in In/Sec

Multi-spectral - Data Comparison

Plot Scale

131-546-03-MOA 09-FEB-96 09:22

0.7

131-546-03-MOV

0

09-FEB-96 09:22

131-546-03-MOH 09-FEB-96 09:22 9

12

15

18

21

12-26

• The sharpness of the peak indicates that it has been created from a waveform dominated by a single frequency. 131-546-03-MOV

1.0

6

MIH

Vertical Turbine Pump Unbalance Example

131-546-03 - PTS=MOH MOV MOA .65

3

MOH

MOV

Max Amp

0

12-24

Vertical Turbine Pump Unbalance Example

24

27

MOTOR OUTBOARD VERTICAL Route Spectrum 09-FEB-96

0.8

OVRALL= PK

FWEL - FRESH WATER BOOSTER PUMP 1

131-546-03-MOV MOTOR OUTBOARD VERTICAL

0.8

0 5

10

15

20

25

30

35

45

50

Ordr:

1.000

Freq:

1776.2

Spec:

.646

Fan Bearing Looseness Example

12-28

The fan bearing looseness data provides the initial spectral data for diagnostics and the waveform data to confirm the looseness fault diagnosis.

Waveform Display

RMS = .2679

0.4 Acceleration in G-s

40

09-FEB-96 09:22

0.6

Time Waveform - Sinusoidal

1776. 29.60

0.2

Frequency in Order

• Approximately 270 milliseconds of time (8 shaft revolutions) shows the clear one per revolution signal generated by the unbalance condition.

RPM = RPS =

0.4

0

12-27

09:22 .6466 V-DG .6464

LOAD = 100.0 0.6

Frequency in Order

Vertical Turbine Pump Unbalance Example

=

LOAD = 100.0 RPM = 1776.

0.2

RPS = 29.60

-0.0

PK(+) = .5932 PK(-) = .6215

-0.2

CRESTF= 2.32

Direct Driven Overhung Fan

-0.4 -0.6 -0.8 0

60

120

180

Time in mSecs

240

300

The fan ran in an out of balance condition for two years. The bearings now have excess clearance, allowing the shaft to move around.

9

12-29

Fan Bearing Looseness Example

The spectral plot below shows vibration in the horizontal direction on the fan outboard bearing.

Note the small amounts of harmonic activity and axial data amplitude. C-20 - C-20 FLOAT ROLL FAN

C-20 FLOAT - PTS=FIH FIV FIA FOH FOV FOA

C-20 - C-20 FLOAT ROLL FAN

Max Amp

14-JUN-95 08:10

Scale

C-20 FLOAT-FOV 14-JUN-95 08:10

1.0

C-20 FLOAT-FOH 14-JUN-95 08:09 C-20 FLOAT-FIA

0

14-JUN-95 08:09 C-20 FLOAT-FIV 14-JUN-95 08:09 C-20 FLOAT-FIH 14-JUN-95 08:08 0

2

4

6

8

10

Frequency in Order

12

14

16

Ordr:

1.000

Freq: 3499.0 Sp 1:

Route Spectrum 14-JUN-95 08:08

0.5

OVRALL= .5095 V-DG PK Velocity in In/Sec

Single Spectrum - Amplitude Relations

C-20 FLOAT-FOA

Plot PK Velocity in In/Sec

Multi-spectral - Data Comparison

C-20 FLOAT-FIH FAN BEARING INBOARD HORIZONTAL

0.6

.46

12-30

Fan Bearing Looseness Example

PK = .5065

0.4

LOAD = 100.0 RPM = 3498. RPS = 58.30

0.3

0.2

0.1

0 0

2

4

6

.395

8

10

12

14

Ordr:

16

1.000

Freq: 3499.0

Frequency in Order

Spec:

.395

Label: HARMONICS-BALANCE/LOOSENESS

Fan Bearing Looseness Example

12-30

12-31

Fan Bearing Looseness Example C-20 - C-20 FLOAT ROLL FAN

• The cursor markers note the locations of harmonics of running speed. • Virtually all the vibration energy in this spectrum is caused by turning speed and harmonics. The sides, or skirts, of this peak are also very narrow. • The number of harmonics tells us that the spectrum is derived from a complex, repetitive time waveform.

C-20 FLOAT-FIH FAN BEARING INBOARD HORIZONTAL Waveform Display 14-JUN-95 08:08

3

Acceleration in G-s

Time Waveform - Sinusoidal Character

4

2

RMS = 1.06

1

RPM = 3498.

LOAD = 100.0 RPS = 58.30

0

PK(+) = 3.08 PK(-) = 3.01

-1

CRESTF= 2.88

-2 -3 -4 0

30

60

90

120

150

180

210

240

Time in mSecs Label: HARMONICS-BALANCE/LOOSENESS

Fan Bearing Looseness Example • A clear and repeatable waveform occurs once per shaft revolution, 1 x RPM. • There is also multiple peaks within one revolution The waveform shows the acceleration created on the bearing housing by the looseness. • The repeatability of the waveform in time with respect to the shaft turning speed and amplitude means that the vibration force is tied to the shaft running speed.

12-31

12-32

Motor to Pump Misalignment Example

The pump has had high vibration since installation and numerous seal/packing and bearing failures. The maintenance personnel stated that the alignment was “difficult” because the base was drilled incorrectly at the manufacturers facility. P1H P1V

P2H P2V P1A

M2H M2V

M1H M1V M1A

M2A

P2A

10

12-33

Motor to Pump Misalignment Example At first glance, the problem might appear to be unbalance. If we take a closer look we see that 2X running speed peaks are present in all directions.

• Harmonics of running speed are denoted by the fault frequency markers (dashed lines). • The first through sixth orders of running speed are visible with the 2X T.S. predominant.

#1 - TIMBERLINE BOOSTER (PROSPECT

TIMBSTRPRO - PTS=MOH MOV MIH MIV MIA

#1 - TIMBERLINE BOOSTER (PROSPECT

TIMBSTRPRO-MIV MOTOR INBOARD VERTICAL

0.24

Max Amp .43

TIMBSTRPRO-MIA

Reference Spectrum A AA AA

0.21

21-JUN-95 16:11

21-JUN-95 16:12 Plot Scale

OVRALL= .1780 V-DG

TIMBSTRPRO-MIV 21-JUN-95 16:11

0.5

TIMBSTRPRO-MIH

0

21-JUN-95 16:11 TIMBSTRPRO-MOV 21-JUN-95 16:11 TIMBSTRPRO-MOH 21-JUN-95 16:11 0

5

10

15

20

25

30

35

40

45

50

PK Velocity in In/Sec

0.18

Single Spectrum - 2xTS

PK Velocity in In/Sec

Multi-Spectral - Amplitude Comparison

12-34

Motor to Pump Misalignment Example

PK = .1771 LOAD = 100.0

0.15

RPM = 1768. RPS = 29.47

0.12

A=MOTOR HARMONIC

0.09

: 1.00

0.06 0.03

55

0 0

Frequency in Order

3

6

9

12

15

18

21

Frequency in Order

12-35

Motor to Pump Misalignment Example

The waveform is repetitive for each revolution with two distinct peaks for each period. #1 - TIMBERLINE BOOSTER (PROSPECT

TIMBSTRPRO-MIV MOTOR INBOARD VERTICAL

Waveform Display

0.6

1

21-JUN-95 16:11

2

RMS = .1784

0.4 Acceleration in G-s

Time Waveform - Twice per Revolution

0.8

LOAD = 100.0 RPM = 1768.

0.2

RPS = 29.47

-0.0

PK(+) = .5682 PK(-) = .5457

-0.2

24

27

Ordr:

1.004

Freq: 1774.9 Spec: .01562

12-36

Pump Bearing Looseness Example

• The diagram above shows a centerhung pump with bearing housing dimensions worn oversize . • The worn housings makes the pump very loose . • Typical of many looseness problems, this has grown worse over time. A small dimension problem has gradually made itself worse. Speed 1775 RPM H.p. 150

CRESTF= 3.19

-0.4 -0.6 -0.8 0

60

120

180

240

300

Time in mSecs

CWTR - COOLING WATER PUMP 1 341-545-01 - PTS=PIV PIH POV POH POA Max Amp 341-545-01-POA 18-APR-96 08:47 PK Velocity in In/Sec

Multi-spectral - Broadband

.14 Plot Scale

341-545-01-POH 18-APR-96 08:47

0.14

341-545-01-POV

0

18-APR-96 08:46 341-545-01-PIH 18-APR-96 08:46 341-545-01-PIV 18-APR-96 08:46 0

400

800 Frequency in Hz

1200

1600

CWTR - COOLING WATER PUMP 1 341-545-01-POA PUMP OUTBOARD AXIAL

0.14

Route Spectrum 18-APR-96 08:47

0.12

OVRALL= .3663 V-DG PK Velocity in In/Sec

Many harmonics of running speed are visible on all measurement positions. Baseline or floor energy is also very visible.

12-38

Pump Bearing Looseness Example Single Spectral - 9-15xTS and Broadband

12-37

Pump Bearing Looseness Example

0.10

PK = .3675

0.08

RPM = 1775.

LOAD = 100.0 RPS = 29.58 0.06

0.04

0.02

0 0

400

800 Frequency in Hz

1200

1600

Freq:

29.58

Ordr:

1.000

Spec: .03901

11

12-38

Pump Bearing Looseness Example

• A cursor is positioned at 1x running speed and on the harmonics of running speed. • The peaks are broad and have wide skirts. • Notice, no individual peak exceeds .1 in/sec, but the overall energy is .3663 in/sec. • This is common with looseness. Broad humps of energy show up in the 9X to 15x running speed range. • This indicates that the time waveform cannot be cleanly transformed into a spectrum. Therefore, the waveform must have random, non-periodic energy present.

Rolling Element Bearing Example

341-545-01-POA PUMP OUTBOARD AXIAL Waveform Display

8

18-APR-96 08:47

6 Acceleration in G-s

12-40

• Maintenance personnel reported vibration from the back end of the motor after only 200 hours operating time on a newly installed drive. • The analyst investigated and found visible flakes of a bronze colored material near the back end of the motor. The motor manufacturer was contacted to determine if the 6330 bearings had a bronze retainer and the reply given was no.

CWTR - COOLING WATER PUMP 1

Time Waveform - Random Energy

• There is no similarity in its pattern from revolution to revolution. Non-periodic, random patterns do not convert well in the FFT process. It is very difficult to assign specific frequencies and amplitudes to patterns in waveforms like the one on the next page. • This difficulty leads to the broadband energy humps in the spectrum. Broader humps indicate more random energy. Higher humps indicate more impacting in the waveform.

12-39

Pump Bearing Looseness Example 10

12-39

Pump Bearing Looseness Example

RMS = 2.12 LOAD = 100.0

4

RPM = 1775. RPS = 29.58

2

PK(+) = 7.68

0

PK(-) = 6.42 CRESTF= 3.63

-2

Ski Lift Motor

850 HP DC Motor

-4

Right Angle Gearbox

6330 Bearings

-6 -8 0

60

120

180

240

300

Time in mSecs

12-41

Rolling Element Bearing Example • All the levels appear very low in amplitude, but notice the location of the dominant peaks. • There appears to be groups of many peaks closely spaced in the mid to higher frequency range. These “mounds of energy” can indicate bearing defects.

Rolling Element Bearing Example • The fault frequencies for the 6330 bearing ball pass frequency outer race are marked. Notice the number of peaks surrounding the higher frequency defect harmonics. • The large number of harmonics and sidebands will be created from a complex waveform. NSTR - BACKSIDE QUAD

Scale

BACKSIDEQD-MOH 05-JAN-96 08:56

0.20

BACKSIDEQD-MOA

0

05-JAN-96 08:57

BACKSIDEQD-MOV 05-JAN-96 08:46 0

400

800

1200

Frequency in Hz

1600

2000

Analyze Spectrum C CC CC C C CC C

05-JAN-96 08:56

0.24 PK Velocity in In/Sec

Plot

Single Spectrum - Bad Bearing

.19

BACKSIDEQD-MOH MOTOR OUTBOARD HORIZONTAL

0.30

Max Amp

PK Velocity in In/Sec

Multi-spectral - Non-Synchronous Energy

NSTR - BACKSIDE QUAD

BACKSIDEQD - PTS=MOV MOA MOH

12-42

PK = .3611 LOAD = 100.0 RPM = 1298.

0.18

RPS = 21.64 >SKF 6330 C=BPFO

0.12

: 77.64

0.06

0 0

400

800

1200

Frequency in Hz

1600

2000

Freq:

77.50

Ordr:

3.582

Spec: .03297

Label: OUTER RACE FREQUENCIES W/CAGE SB

12

12-43

Rolling Element Bearing Example

Rolling Element Bearing Example The number and height of the spikes in the time waveform confirm the presence of severe impacting.

NSTR - BACKSIDE QUAD BACKSIDEQD-MOH MOTOR OUTBOARD HORIZONTAL

8

Waveform Display 05-JAN-96 08:56

The waveform shape is random and complex. This shape cannot be transformed into a clean spectrum, so the spectrum on the previous page with broad humps of energy is created.

RMS = 1.75 Acceleration in G-s

Time Waveform - Bad Bearing

6

4

LOAD = 100.0

2

RPS = 21.64

RPM = 1298.

PK(+) = 5.81

0

PK(-) = 5.13 CRESTF= 3.32

The bearing cage turned out to be bronze! It was deteriorating and did not have much life left. The outer race had major spalls from impacting balls. The bearing was replaced.

-2

-4

-6 0

40

80

120

160

12-43

200

Time in mSecs Label: OUTER RACE FREQUENCIES W/CAGE SB

12-44

This is an example of Unbalance.

12-44

This is an example of Unbalance. The cursor on the previous slide is marking 1xTS (1 Order) at 59.34 Hz in the Spectrum. How does that frequency relate in the Waveform. The discussions on waveform analysis are not intended for the analyst to discard the Spectral analysis. The Spectrum is Amplitude vs. Frequency. The Time Waveform is Amplitude vs. Time.

12-45

This is an example of Unbalance. Now let us look at the Waveform in “Time”.

12-45

This is an example of Unbalance. The cursors are marking the harmonics of the turning speed frequency, harmonic cursors was selected. The frequency at 59.34 Hz 0r 59.35 Hz. = (1 Order). The time is 16.85 msec. 16.85 msec divided by 1000 = .01685 sec, this is the Time to complete 1 revolution. Frequency = 1 divided by the Time 1 divided by .01685 = 59.347 Hz = turning speed of the rotor.

13

12-46

This is an example of Unbalance.

12-47

Misalignment Example

• Change the display to “Revolutions” of the shaft. • Notice the time is now 1.000 that is (1 Order). • Viewing the Waveform in “Revolutions” can often make analyzing a little simpler.

• Now look at a Misalignment example. • The cursors are marking harmonics of turning speed. The peak at 2x turning speed is the highest amplitude. We have 3 or 4 peaks per revolution of the shaft in the Time Waveform.

12-48

Misalignment Example

12-49

Misalignment Example

• Take a closer look at the misalignment waveform pattern. • Harmonic cursors are marking the harmonics of what frequency? • From this display you still do not really know! You only know that these marked peaks are harmonic.

• From this display the same frequency was marked and the “Set Mark” enabled, and the “Difference” cursors was selected.

12-49

Misalignment Example Look at the

time, it is 13.05 msec.

13.05 msec divided by 1000 = .01305 sec 1 divided by .01305 sec = 76.63 Hz

12-50

Misalignment Example • Change the display to “Revolutions” of the shaft. • Mark the same frequency, “Set Mark” select “Difference” cursors.

76.63 Hz x 60 = 4598 rpm When we look at the freq: 76.65 in the display at lower right hand corner we can see the frequency has already been calculated for us. The harmonics displayed are harmonics of 76.63 Hz. This the frequency of 1xTS.

14

12-51

Misalignment Example

12-52

Misalignment Example

• We can control the cursor and look at the “ time”. • The “ time” is in Orders .999 orders. • We must remember it is very difficult marking exact frequencies in the Time Waveform.

• 1x turnining speed is at 76.63 Hz. 76.63 Hz x 60 = 4598 rpm. • We can see two events occurring in 1 revolution of the shaft. • How often is the second event occurring in the Time Waveform • We will mark the 1st event, select “Set Mark”, select “Difference” cursors

12-52

Misalignment Example

12-53

Bearing Problem

Move cursor to the peak representing the 2nd event in one revolution. – Look at the “ time” between these two frequencies. It is 6.523 msec. 6.523 divided by 1000 = .006523 sec.

On the following slide the cursor is marking 1xTS, we have peaks at the bearing defect frequencies.

– 1 divided by .006523 sec = 153.3 Hz – 153.3 Hz x 60 = 9,196 rpm

Also displayed on the following slide is the Spectrum with Fault Frequencies for the BPFI .

– The 1x TS was 4,598 rpm, 4,598 x 2 = 9,196 rpm

The Primary calculated defect frequency for the BPFI is 5.91 orders. There are about 10 harmonics of 5.91 orders in the spectral data.

Now, it is easy now to see that this frequency is occurring at 2 x TS of the rotor. It is repeated every revolution of the shaft.

12-53

Bearing Problem

12-54

Bearing Problem • How do the bearing frequencies relate in the Time Waveform? • This display shows the Fault Frequencies for the BPFI displayed.

• We must realize that the dotted lines do not automatically fall on the defect frequency we may want to mark. Just any frequency was selected. Notice where the fault lines are now.

15

12-55

Bearing Problem

12-55

Bearing Problem

• In the plot displayed below the cursor was placed on a different frequency before the fault lines where brought up. We can see that the fault lines will fall where we place the cursor.

Our main concern is knowing the spacing of the defect frequencies. This is what is displayed when we bring up the fault frequencies in the Time Waveform, the “Spacing”. Let us examine the Waveform further: The Primary calculated defect frequency for the BPFI = 134.4 HZ, so the repetition rate of the impacts would calculate to 134.4 Hz. We still have to find the impacts that are occurring at that spacing. This will take some time for the analyst to develop this ability to spot the equal spacing.

12-56

12-56

• When initially viewing the Waveform we look for events that are repeated, we also look for events that are equally spaced. In this plot there are several events that are repeated and equally spaced.

• We know from the Spectral display that we have an inner race defect. Let’s display the fault frequency for the BPFI, first without a cursor marking any event.

12-57

12-57

Bearing Problem

Bearing Problem

Bearing Problem

• All we are trying to do with this display at this point is to look for impacts that may represent the BPFI. There could be BPFO’s, BSF’s also. We will focus on the BPFI’s.

Bearing Problem

• After placing the cursor on a peak we suspect is an impact from a BPFI, then displaying the fault frequency for the BPFI, we can see we have several peaks that match up.

16

12-58

Bearing Problem

12-58

Bearing Problem

• We can view an expanded plot to see this a little clearer.

• Place the cursor on an impact that matches up with a fault line. Select the “Set Mark” option. Select “Difference” cursor. Move the cursor to the next fault line, now look at the Freq: at lower right hand corner. This should be very close to the Primary Calculated Freq. for the BPFI. In this example it is very close.

12-59

Bearing Problem

12-59

Bearing Problem

• Alarms can also be utilized in Waveform analysis.Select Set-Up from Tool Bar and you can set the Alarms and display them in the Waveform.

12-60

Bearing Problem

12-60

Bearing Problem

• The value for the Crest-Factor has been set to Peak 1.5 for the display seen below.

17

12-61

Part 1 - Summary TABLE 4.5. TIME WAVEFORM SHAPE ANALYSIS SHAPE harmonic

MECHANISM mass unbalance, resonance, eccentricity, misalignment, bow, blade/diffuser interaction

truncated 1x

excessive mass unbalance, thermal growth, bearing clearance problems, pedestal nonlinearity, rubs

truncated harmonics

gear mesh, blade pass, natural frequencies, nonlinear behavior

subharmonics

rubs, oil whirl, resonance, trapped fluid hysteresis, looseness

orders

misalignment, looseness, generator faults

order excited natural frequencies

heavy 1x behavior can excite order located natural frequencies

multiple harmonics

generators (slot passing), gears, vane pass, bearings, naturally generated harmonics superimposed on 1x

beats

grinders, motor driven fans, pumps where two forcing frequencies are close

pulses

bearings, recips, flat spots, gear teeth (broken) – some functional; some fault based

modulated frequencies

motor faults, gears, bearings – a forcing frequency is modulated by a fault frequency

modulated pulses

bearings, gears, rolls – natural frequencies or forcing frequency modulated by low frequency that is generated by the fault

truncated beats

truncation of signal by bearings, supports, foundations or couplings – nonlinear behavior

synchronous vs nonsynchronous

nonsynchronous frequencies cause moving, non stationary waveform

random vibration and noise

pumps, fans, steam flow, late life bearings

DIGITIZED TIME DOMAIN — TRENDS

• Waveform data may be used for much more than what is typically seen in industry. The ability to check for specific characteristics such as periodicity and modulation, helps the analysis process. • Energy balance (asymmetry) may be checked for direction of signal and for the predominant traits of the signal. • Overall waveform is much more understandable and useful than most would lead us to believe. However, this section enhances your analysis abilities using the time waveform.

DIGITIZED TIME DOMAIN — DETAILS

What is this spectrum Lines?

PRESENTATION OF TIME WAVEFORM

What is this spectrum Lines?

TIME DOMAIN WAVEFORM — TREND

Table 4.4. An Approach to the Presentation of a Standardized Time Waveform4.1 TIME (sec.)

DISPLAY

PURPOSE

T/100 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - DETAILS OF T/80 HIGHER FREQUENCY T/20 T/10 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- -TRENDS OF T/3 HIGHER FREQUENCY T/2 T - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - BALANCING/PHASE 2T 3T 10 T- - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - PHASE TRENDS 20T 80T 100T - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - LONG TERM TIME TRENDS

4000 HP Induction Motor with 200T Display

18

TIME DOMAIN WAVEFORM — SHORT TERM

4000 HP Electric Motor with 20T Display

TIME DISPLAY — SMALL MOTOR 400T

Small Motor with a 400T Display

GEARBOX 10T

Gearbox with 10T Display

TIME DOMAIN WAVEFORM — BALANCING

4000 HP Electric Motor with T Display

SMALL MOTOR 100T

Small Motor with a 100T Display

GEARBOX — 1T ON MESH

Gearbox with T Display Shows Gearmeshing Effects

19

PRESENTATION SETUP

STANDARD SETUP

Visual process Setup to accommodate visual analysis – to evaluate • periodicity

– to evaluate amplitude changes

Processing types – dual processing – expansion

Standard Time Waveform Display from an FFT Analyzer

DUAL PROCESSING

DUAL PROCESSING Dual Processing means:the capability to produce each Spectrum and Time waveform data independently (spectrum Fmax not equal to waveform Fmax) SPECTRUM: – 10x operating speed – fmax = 250 Hz

TIME WAVEFORM: – Period =

= 0.0421

– Display = 8 cycles x 0.0421

0.336 sec

– Use 0.4 sec then =

Dual Processing to Enhance the Time Waveform

PHASE MEASUREMENT

Use of Cursor to Measure Period and Amplitude

=

TRUNCATED 1X

Single Frequency Due to Mass Unbalance with Clipping Due to Excessive Amplitude

20

TRUNCATED HARMONIC

Clipped Vane Pass Signal from Hull of a Ship

ORDERS

Nonlinear Generator Pedestal Response to Differing Vertical Stiffness

BEATS

Beats Caused by Two Adjacent Cavitated Vacuum Pumps

SUBHARMONIC

Loose Bearing Housing — ½ Orders and Multiples

FAR REMOVED ORDER

Slot Passing Frequency, 36X, Generated by Air Gap Variation (120 Hz)

PULSES

Pulses from a Bearing Defect

21

AMPLITUDE MODULATION

Sidebands Caused by Amplitude Modulation — Broken Rotor Bar

TRUNCATED BEATS

Truncated Beat Waveform from a Motor Driven Fan

RANDOM NOISE and VIBRATION — RMS AVERAGING

PULSE INDUCED NATURAL FREQUENCIES

Pulse Induced Natural Frequencies in Printing Roll

SYNCHRONOUS ORDERS

Exciter to Generator Misalignment Causing 1x and 2x

CONCLUSIONS True physical behavior Determine origin of frequencies Determine severity

Excessive Flow Noise Caused by Pump Operating Off the Curve-Recirculation

22

NONSYNCHRONOUS MULTIPLE FREQUENCIES

Boiler Feed Pump Drive — Nonsynchronous Second Order and Multiples

NONSINUSOIDAL AMPLITUDE MODULATION

SINUSOIDAL AMPLITUDE MODULATION

Amplitude Modulation by a Single Frequency

MACHINE RESPONSE TO IMPACT EXCITATION

Response of a Machine to Impulse Excitation Amplitude Modulation in a Gearbox — Nonsinusoidal

IMPACT INDUCED NATURAL FREQUENCIES

Broken Gear Tooth Yielding Pulse Induced Natural Frequencies and Abrupt Pulse Loading in Time Domain

LIGHT IMPACT INDUCED ORDERS

Gradual Entering Pulse Caused by Misalignment, Eccentricity, or Tooth Profile Wear in a Gearbox

23

FREQUENCY MODULATION

Torsional Vibration a Form of Frequency Modulation

MECHANISMS FOR ORDER GENERATION

DIFFERENCE FREQUENCIES

Two Lobed Blower Generated Difference Frequencies — Pressure Pulsations Generated by Lobes Passing Discharge Port

BEAT MECHANISM

Natural excitation Nonlinear parameters Signal truncation

Figure 4.50. Beat Mechanism

TRUNCATED BEATS

SUM and DIFFERENCE FREQUENCY TABLE

Hypothetical Vibration Response Exhibiting Beat Frequency

24

SUM and DIFFERENCE FREQUENCY MECHANISMS

Rotating Machinery Fault Diagnosis Using Sum and Difference Frequencies (Sidebands) (After Eshleman 4.2)

SUM and DIFFERENCE FREQUENCIES — TURBINE PIPING

Frequency Domain Record of Line Shaft Turbine Piping Vibration

DEMODULATION Mechanism Rolling element bearings Techniques

SUM and DIFFERENCE FREQUENCIES — PISTON PUMP/ENGINE

Pulsating Torque from an Engine-Pump Unit

SPECTRUM SHAPE

Misalignment Induced Air Gap Vibration

MODULATION MECHANISM High frequency vibration amplitude is altered due to mechanical defects Low frequency modulator points to problem area Used extensively for gears and bearings

25

GEARBOX MODULATION IMPACT INDUCED MODULATION

Amplitude Modulation in a Gearbox — Nonsinusoidal

DEMODULATION

Figure 4.58. Spectrum of a Demodulated Signal

BEARING DEFECT FREQUENCY ZONES

Machine Vibration Response to Bearing Faults

Pulse Induced Natural Frequencies in Printing Roll

BEARING DEFECT

Outer Race Defect on Rolling Element Bearing — Pulsation Effect

DEMODULATION OF A SIGNAL

Demodulation of a Signal © © Copyright 2002 by Ronald L. Eshleman. All rights reserved. This figure may not be reproduced without permission of Ronald L. Eshleman.

26