back to basics with time waveform

8/11/2019 Back to Basics With Time Waveform

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BACK TO BASICS WITH TIME WAVEFORM ANALYSIS IN PDM

Dennis H. Shreve

Commtest, Inc., Knoxville, TN, USA, [email protected]

ABSTRACT

With all the recent advancements in microprocessors and digital signal processing, there is a

tendency to simply look at pattern recognition (or signatures) of captured data in the frequencydomain and to make some quick decisions on root-cause problem solving. In some cases, even

the fundamental thinking (processing) on the data is taken over by the instrument itself.Modern-day maintenance folks have become dependent on advanced technology to quickly

diagnose complex machinery problems by simply placing a sensor on a machine component andawaiting a report of the fundamental problem along with a recommendation for fixing it i.e.,

balance, align, tie down loose parts, avoid resonance, or replace a defective bearing.

With all these functions now being viewed as somewhat automatic and accepted practice overthe past decade, it is easy to forget about the basics of the measurement and the large amount of

processing that has to be performed. For those more seasoned analysts in this field, there is arecollection of the time when only a signal trace of the vibration level was available on a scope

or chart. A large amount of hand calculations were then required to correlate the information topossible faults associated with specific machinery components and operations.

As the frequency data are derived from a transducer providing a raw time waveform electrical

signal of the vibration measurement, whether it be a displacement probe, velocity pickup, or an

accelerometer, it is worth going back to basics for a moment to see how this raw data can be abig help in analyzing and pinpointing some fundamental vibration issues. Oftentimes, the rawtime information is only viewed as being a stepping stone to get to a quick problem resolution

and the data are actually discarded.

This paper is intended to show the value of retaining a sample of the collected time waveform

and to describe how the raw data should be properly collected and analyzed. This additionalinformation will help one to arrive at a solid PdM strategy for pinpointing and confirming

complex vibration problems with a high degree of confidence.

1. INTRODUCTION

In recent years, there has been a bit of resurgence in the use of time waveform analysis incondition monitoring and predictive maintenance. It had been avoided for years as modern Fast

Fourier Transform (FFT) processes have prevailed as being more simplistic and fast, as the namesuggests.

The analysis of time waveform data is certainly not a new technique. In the early days of

vibration analysis, time waveform data was viewed on strip charts and oscilloscopes and


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frequency components were calculated by hand. The basic relationship between frequency andtime measurement is illustrated in the graph in Figure 1.

Figure 1 Sinusoidal time waveform over one period

f = 1/T, where f is the frequency in Hz and T is the period in seconds (the amount of timerequired to complete 1 cycle).

It is the knowledge of this relationship that permits the determination of frequency components

from the raw waveform data.

In reality, time waveforms are much more complex than a simple sine wave and a little moredifficult to decipher. Figure 2 is an example of a raw signal in acceleration from the vibration

sensor.

Figure 2 Raw time waveform signal from vibration sensor


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Note that dual differential cursor marks can show the approximate period (0.02701 sec.) and be

used to estimate frequency of the fundamental incoming signal. Calculation here from Hz toCPM shows 2222 RPM, where an actual tachometer on the shaft confirmed the running speed at

2116 RPM.

Oftentimes, raw vibration sensor signals and patterns are not so clear in how to perform a quickanalysis. This is illustrated with the input from a sensor attached to a gearbox, as shown in

Figure 3.

Figure 3 Vibration signal from sensor on gearbox

In this case, it will take some real effort (with pan and zoom functions) to correlate impacts

displayed and relative timing to known internal components and turning speeds. As always,knowing the actual make-up of the machine is the key to good analysis.

The most common application for time waveform capture is to compare a waveform pattern of

the machine with another obtained from a machine with similar defects. If necessary, frequency

characteristics can be derived.


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2. DISCUSSION

Now that it is more evident that there is true value in the raw time waveform data, there are a few

fundamental concepts and considerations that need to be explored.

2.1 Time Waveform Considerations

Time waveform can be used effectively to enhance and to confirm spectral information in thefollowing applications:

Low speed machines (typically less than 100 RPM).

Indication of true amplitude in situations where impacts occur i.e., in the assessment ofrolling element bearing defect severity.

Gearboxes.

Sleeve bearing machines with X-Y probes (2-channel orbit analysis).

Looseness. Rubs.

Beats

While it is true that time waveform can be applied to a variety of vibration problems, some

situations show that spectral and phase data provide better indications on the source of theproblem. Two primary examples are:

Unbalance on normal speed machines.

Misalignment on normal speed machines.

Experience has shown that there are a number of machinery problems where time waveformanalysis truly reigns supreme over conventional spectral methods. These include:

Cracked, broken, and deformed gear teeth.

Rolling-element bearing defects on very low speed machines (< 10 RPM).

Motor startup transient issues.

Reciprocating compressors.

There are also a number of areas where time waveform data proves to be reinforcing tospectrum. These include:

Rolling-element defects on low to moderate machine speeds (50-300 RPM).

Motor electrical problems.

Shaft scratches on the proximity probe measurement area.

Rotor rubs.

Machine tool chatter.

Misalignment vs. Looseness.

Oil whirl.


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Rotary blower rubs.

Reciprocating compressors misfires, compression, etc.

Time waveform charts can best illustrate true peak values, phase relationships, transientconditions, system impacts, damping, modulation, and beating.

Care must be taken when setting up parameters of the instrument for getting proper time

waveform data. Of special interest are the following measurement parameter settings:

Units of Measurement.

Time period being sampled.

Resolution.

Averaging.

Sampling window.

2.2 Units of Measurement

Amplitude measurement units should be generally selected based upon the frequencies ofinterest. Early on in training courses associated with vibration measurements and analysis, it is

stated that velocity is the best parameter to use for most machinery problems with running speedbetween 600 and 60,000 RPM. Displacement is recommended at low speeds and acceleration is

the parameter of choice at high speeds. The plots below illustrate how measurement unitselection affects the data displayed. Each plot contains 3 separate frequency components: 60 Hz,

450 Hz, and 1050 Hz.

Figure 4 Displacement measurement of complex signal

These data as displayed in Figure 4 were taken using displacement. Note how the lower

frequency at 60 Hz is quite prominent.

The same data is now displayed in Figure 5 with selecting velocity as the unit of measure. Notehow the 450 Hz component becomes more apparent as it rides on the 60 Hz fundamental

frequency.


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Figure 5 Velocity measurement of complex signal

Figure 6 is a plot of the exact same data and using acceleration as the unit of measurement.

Figure 6 Acceleration measurement of complex signal

Note how the large lower frequency component is diminished and how the higher frequency

component at 1050 Hz becomes dominant.

Typically, the unit of measurement displayed in time waveform data should be the natural unit of

signal produced by the transducer. For example, if a displacement reading is required for properanalysis, then a displacement transducer should be used. In most cases where modern datacollector instruments are employed with an accelerometer in condition monitoring, this means

that acceleration will be the unit of choice. It is important to note that the transducer is similar toa direction-sensitive microphone, and that the readings will be best aligned in the direction of

placement. Looking at the raw time waveform data in either velocity or acceleration, negativegoing numbers represent motion towards the sensor, while positive going numbers represent

motion away from the sensor. Similarly, displacement data being viewed will show negativenumbers with motion away from the sensor and positive numbers with motion towards the

sensor. If data are gathered from non-contact probes on sleeve bearing machines, displacementis typically the measurement of choice.

The motion of the machine component on a captured time waveform display can be thought of as

starting on the left side and progressing across and ending at the right side.

Generally, we will see 4 main types of patterns in our display: (1) pure sinusoidal (smoothmotion), (2) square or truncated, (3) triangular or saw tooth, and (4) spikes or impacts.


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2.3 Time Period Sampled

For normal PdM analysis work, the instrument is generally set up to see 6-10 revolutions of the

shaft being measured. For bearing impacting, it is best to capture 8-10 shaft revolutions, thereby

allowing at least 2 complete revolutions of the cage itself. The total sample period desired canbe calculated by a simple formula:

Total sample period [seconds] = 60 x (# of revolutions desired) /RPM

Table 1 illustrates the required time sample in seconds by machine speed.

Table 1 Recommended sample durations for 8 and 10 shaft revolutions

MACHINE RPM Time for 8 revolutions (sec.) Time for 10 revolutions (sec.)

3600 0.133 0.167

1800 0.267 0.3331200 0.4 0.5

900 0.533 0.667

300 1.6 2.0

100 4.8 6.0

While some instruments do not allow the setting of the desired time period in terms ofrevolutions or time, it is necessary to set up an equivalent frequency max (or Fmax) value. This

value is determined by the following formula:

Fmax (CPM) = (lines of resolution) x RPM /(# of revolutions desired)

Remember the inverse relationship between time and frequency in data collection, where thelower the Fmax, the longer data collection time.

Assuming that lines of resolution are fixed at 1600, Table 2 provides applicable Fmax values.

Table 2 Fmax values for 8 and 10 shaft revolutions

MACHINE RPM Fmax for 8 revolutions Fmax for 10 revolutions

3600 720 KCPM 576 KCPM

1800 360 KCPM 288 KCPM

1200 240 KCPM 192 KCPM900 180 KCPM 144 KCPM

300 60 KCPM 48 KCPM

100 20 KCPM 16 KCPM


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2.4 Resolution

For detailed time waveform analysis, it is common practice that 4096 samples (1600 lines) are

used. This degree of resolution ensures that the data collected have sufficient accuracy and that

key vibration events are captured.

2.5 Averaging

In most data collectors, a type of averaging is performed during the FFT process. Unless

synchronous time averaging is employed (to be discussed later), the time waveform presented onthe screen will be the last average taken even if multiple averages are selected in the instrument

setup. It is normal therefore to take just a single average. Overlap averaging should be disabledfor time waveform analysis.

With spectral analysis, it is quite common to employ linear averaging, where each instantaneous

spectrum is added to the next and the sum contribution at each data point is divided by thenumber of averages taken. This method is great for fault finding, and it is used in most

predictive maintenance programs. It is also a useful technique in averaging out randombackground vibrations. Oftentimes, overlapping averages are used in FFT calculations to get

better frequency and amplitude representations from windowing sampling on the captured timewaveform.

Peak hold is sometimes referred to as averaging, but is not a true averaging technique. It simply

captures, holds, and displays the highest sampled peak value. This technique is quite useful inviewing transient events and in stress and resonance analysis calculations.

Exponential averaging takes the most recent sample of data and weights it more heavily than

previous samples. It is most useful in observing conditions that are changing slowly with respectto sampling time i.e. a steady-state process.

Time synchronous averaging (TSA) can be used to synchronize data acquisition to a particular

rotating component. This technique can be useful on gears where broken teeth are suspected toassist in the location of the defective teeth relative to a reference mark. It is also useful on

reciprocating equipment to time and correlate events to a particular crank angle.

2.6 Windowing

A variety of windows can be applied to the time waveform prior to performing the FFT. Thepurpose of these windows is to shape the spectrum and minimize leakage errors, thereby

providing a more accurate frequency determination (but sacrificing absolute accuracy ofamplitude). Some instruments allow these windows to be applied to time waveform data as well.

This would force the data to zero at the start and end of the time sample potentially losing data.To eliminate this effect, a uniform (rectangular) window is recommended.


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2.7 Interpreting Waveform Presentations and Possible Faults

2.7.1 Unbalance

As unbalance is one of the predominant (~40%) faults uncovered in analyzing machineryvibration problems, the waveform as shown in Figure 7 is usually a key indicator of the problem.

Figure 7 Vibration signal showing possible unbalance

However, this classic textbook sine wave as shown is rarely seen in the raw acceleration timewaveform. This is because acceleration emphasizes the higher frequency components that are

almost always present in the vibration signal. This de-emphasizes the underlying lowerfrequency signal. A predominate 1X component (with > 80% contribution) in the waveform is a

certain indicator of an unbalance condition.

The waveform as displayed in Figure 8 is really more representative of sinusoidal vibration asviewed in acceleration. Note that there are distinct higher frequency components riding on the

lower frequency waveform.

Figure 8 Typical acceleration reading showing unbalance

2.7.2 Misalignment and Looseness

Misalignment is another predominant fault (~40%) found in the analysis of vibration data onindustrial equipment.

Although the classic symptoms of misalignment are M and W shapes showing

predominance in the time waveform, these symptoms cannot be relied upon totally. A loosenesscondition will often show a similar waveform. Both problems in machinery often show


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increased amplitude in 1X, 2X, and/or 3X RPM. If one takes a look at the accelerationwaveform and compares the information to the velocity spectrum, there are some methods to

distinguish one problem from the other.

The acceleration waveform from a misalignment will display regular, periodic spacing between

the major peaks and follow a nice pattern (i.e., one of three major peaks may be consistentlyhigher than the others with each shaft rotation). This is something that will be evident in a timedisplay and not so obvious in frequency. In addition, in the time waveform, peaks will normally

be less than 2 gs, indicating very little impacting. In the spectral display, the noise floor formisalignment will be quite low, again indicating negligible impacting.

On the other hand, time waveform data from machines with looseness are characterized by an

irregular spacing between major peaks. In addition, there is no real pattern to the occurrences ofthese peaks. They appear to be random in variation. In some cases, looseness will show impacts

in the time waveform exceeding 6 gs. These kinds of impacts also show a significant noisefloor in the spectral plot.

The relative phase angle between the 1X RPM and 2X RPM components actually determines the

shape (pattern) of the plot associated with misalignment.

Figure 9 A classical pattern showing signs of misalignment

The pattern in Figure 9 illustrates the classic patternof misalignment. In the data shown in

Figure 10, the relative phase between 1X RPM and 2X RPM was changed 90 degrees. It resultsin a very different looking pattern.


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Figure 10 Phase between 1X and 2X changed 90 degrees

Next, the 1X and 2X component vibrations are put into phase (0 offset) with each other. The

pattern as shown in Figure 11 then occurs in the captured time waveform.

Figure 11 Phase between 1X and 2X at 0 degrees

While it is true that the time waveforms do have a similar appearance, it is easy to see that

phasing of the vibration signals provides distinct variations.

2.7.3 Amplitude Symmetry

Another important trait in analyzing time waveform data is the amplitude symmetry. Timewaveform data symmetry above and below the centerline axis is important. Symmetrical data

indicates that the machine motion is even on each side of the center position. Non-symmetricaltime waveform data indicates the motion is somehow constrained - possibly by misalignment, or

rubs.


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Figure 12 Waveform with symmetry around X axis

The waveform in Figure 12 shows a nice symmetrical pattern above and below the zero

reference line.

The waveform pattern in Figure 13 is non-symmetrical above and below the zero line. Theamplitudes below the line swing significantly further than those above the line. In this particular

case, a misalignment condition was diagnosed as the source of the problem. The markers on theplot show 1X RPM.

Figure 13 Non-symmetrical waveform around X axis

2.7.4 Symmetry on the Time Axis

When the previous time waveform is observed with 1X RPM markers present, it is noted that thewaveform pattern although complex is repetitive. This pattern indicates that the vibration is

synchronous to RPM.


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The waveform pattern in Figure 14 indicates a non-repetitive pattern characteristic of non-

synchronous vibration. The vertical lines are spaced at 1X RPM.

Figure 14 Non-repetitive pattern in time

Figure 15 depicts a classical example of a time waveform where there are two frequency sources

that are not harmonically related. 58 Hz and 120 Hz are used for this example a type of signalthat might be present when a 2-pole motor exhibits an electrical hum condition.

Figure 15 Two frequency sources not harmonically related

Note that the higher frequency wave does not always start at the same part of the lower

frequency cycle, and it appears to be riding on the other wave, thereby causing symmetry to belost.


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Care must be exercised when determining symmetry of the time axis. 1X RPM markers areavailable in most software programs and they should be used to avoid confusion.

At first glance the captured waveform in Figure 16 appears to have large impacts occurring with

somewhat similar spacing. The horizontal axis is scaled in time units.

Figure 16 Waveform showing repetitive impacts

Viewing the data with the horizontal axis showing revolutions, as seen in Figure 17, the majorimpacts can be seen to be occurring at approximately the same part of the revolution. However,

a closer inspection reveals that the spacing is not exactly synchronous. In this particular case, theproblem was a single large defect on the inner race of a bearing. The change in amplitude of the

defect was due to the defect coming in and out of the load zone.

Figure 17 Impacts on scale with revolutions


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An FFT captured on this machine provided the spectral display in Figure 18.

Figure 18 FFT (spectral) data on waveform with a bearing defect

The highest amplitude at BPFI is barely noticeable in velocity at less than 0.05 IPS.

2.7.5 Beats and Modulation Effects

Another excellent application for time waveform is the observation of beat frequencies andmodulation effects. Oftentimes these phenomena are quite audible. The time span for data

collection should be set to capture 4-5 cycles of the event.

Lets first take a look at time waveform with amplitude modulation (AM) in Figure 19. Notethat, the signal seems to be getting louder and softer, and that there are rounded high spots and

rounded low spots.

Figure 19 Typical amplitude-modulated waveform


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Next, lets compare this to a waveform exhibiting a beating sensation, as shown in Figure 20.

Figure 20 Waveform showing a beating sensation

Note that it has some similarities to AM, but while high spots are rounded, low spots are quitepointed and distinct.

Figure 21 Waveform with 120 CPM beating

The time period between the beats on the waveform in Figure 21 is measured to be 0.5 s. Fromthis information, the frequency of the beat is calculated at 120 CPM. This represents the

frequency difference between the two source frequencies In this case the beat was caused byinteraction between a 2X RPM vibration source and a 2 X LF vibration source on an induction

motor running at 3540 RPM (7200-7080 = 120).

2.7.6 Impacts

When the FFT process is applied to a signal that contains impacts, the true amplitude of theshort-term vibration is often greatly diminished. The acceleration time waveform shown below

was taken from a machine running at 1800 RPM. It shows several random impacts withmagnitude exceeding 6 g pk. The cause of this signal was a failed rolling element bearing. The

shape of the waveform often appears to be a large spike followed by a ring down sometimesreferred to as an Angel Fish Pattern, as shown in Figure 22.


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Figure 22 Typical angel fish pattern from impacting

When we view the Angel Fish and its direction of swimming, we can also determine the typeof motion event. This characteristic is illustrated by Figure 23.

Figure 23 Patterns depicting impacting and binding

The pattern shown at the left in Figure 23 (swimming to the left) indicates an impact event

followed by a ring-down.

On the other hand, the pattern at the right of Figure 23 (swimming to the right) indicates some

type of binding (or buildup) followed by a relief.


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Note the spectral velocity plot shown in Figure 24. The vibration is less than 0.04 IPS, and this

data by itself would not warrant any further action.

Figure 24 Spectral plot of machine with impacting

Extreme care must be exercised when assessing the amplitude severity of 1X RPM impacts in

using spectrum alone. Note below the spectrum of a machine in Figure 25 where the key isimpacting the coupling guard. The amplitude at running speed (1775 RPM) indicates a

component at less than 0.02 IPS. The overall value of the measurement is only at 0.05 IPS, andthis low amplitude would typically not catch anyones attention.

Figure 25 Impacting data in the spectral display

Now looking at the time waveform of these data in Figure 26, we see the amplitude of impacts atrunning speed (33.8 msec. spacing) exceeding 0.15 IPS!


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Figure 26 Time waveform better captures impacting

Severe damage was evident on the key and shaft upon disassembly.

2.8 Time Synchronous Averaging

A closely related tool in analyzing waveform data for PdM is a time waveform capture calledTime Synchronous Averaging (TSA). TSA affords the analyst one of the most powerful tools

available for discovering incipient machinery failures associated with cracks and deformity ingear structures while the machine is running. Not only can one pinpoint a discontinuity in a

TWF showing the gear tooth problem, it can be located exactly from the timing mark on theshaft. The setup is fairly simple, and it requires a tachometer to measure shaft position relative

to captured data.

TSA averages several time waveforms together, not the spectrums. With the averaging, itprovides data that is synchronized to the position of the shaft relative to the tachometer signal.

Displayed data of impacts is so exact that it allows the location of a single gear problem within

the mesh.

While it is clear that TSA can be a valuable tool for the analyst, there are a few cautions and

limitations that need to be noted before blindly applying the technique. All non-synchronousfrequencies will be removed, as listed below:

All bearing frequencies.

Electrical vibration influences.

Natural frequencies.

Belt frequencies.

Also, because of the nature of the measurement, it is important that the tachometer pickup beaimed at the correct target on a reasonably steady machine speed, with little jitter.

2.9 Crest Factor

There has been considerable discussion so far on the ability to detect peak impacts and possibledefects and influences from certain machinery defects. One would be remiss in these discussions


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without touching on the term Crest Factor to assist in quantifying the contribution of impactingsignals in a captured time waveform.

Crest Factor is defined as the peak amplitude of a time waveform divided by the RMS value.

(i.e., Crest Factor = Peak / RMS)

The main purpose of the crest factor calculation is to give the analyst a quick idea of how muchimpacting is occurring in a waveform. Impacting is often associated with roller bearing wear,

cavitation, and gear tooth wear. Figure 27 provides an example of a waveform with a crestfactor of 3.

Figure 27 Time waveform with crest factor of 3

In a perfect sine wave, with amplitude of unity, the RMS value is equal to 0.707, and the crestfactor is then equal to 1.41. A perfect sine wave contains no impacting whatsoever, and thus,

crest factors with a value higher than 1.41 indicate that there is some degree of impacting.

The definition of the Fast Fourier Transform implies that any signal can be approximated by thesum of a set of sine waves. Unfortunately, this doesnt work so well when one has a signal that

consists of non-periodic events, impacts or random noise. Both impacts and random noiseappear the same in the displayed spectral data although they mean different things in the context

of machinery vibration analysis. The crest factor is therefore useful in giving the analyst a quick

idea of what event might be occurring in the time waveform.

See the captured waveforms below in Figures 28 and 29 for a good illustration of the value of

using Crest Factor in problem analysis.


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The waveform in Figure 28 has a crest factor of 3.98.

Figure 28 Time waveform with crest factor 3.98

The waveform in Figure 29 has a crest factor of 2.30.

Figure 29 Time waveform with crest factor 2.30

The data in Figure 28 represents a machine with serious rolling element bearing wear, and the

crest factor is relatively high due to the amount of impacting occurring within the bearing. Thedata in Figure 29 represents a machine with a predominant unbalance condition, but no

impacting related to bearing wear.


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Crest Factor is a quick and useful calculation that gives the analyst an idea of how muchimpacting is occurring in a time waveform. This is useful information that is lost for the most

part if one is only viewing a spectrum, as the displayed FFT data cannot differentiate betweenimpacting and random noise.

Impacting in a time waveform may indicate rolling element bearing wear, gear tooth wear orcavitation.

As a valuable tool in time waveform analysis, Crest Factor is sometimes trended over time inorder to see if the amount of impacting in a machine component is increasing or not.

3. CONCLUSIONS

It is hoped that the information conveyed in this paper will be utilized as an additional tool inperforming vibration analysis and assessing machinery performance in a condition-based

maintenance program.

Time waveform data has indeed proven to be an excellent analysistool. It is not recommendedthat it be taken on all measurement locations on a regular basis. This practice would add

significantly to the time required and the data storage requirements.

Table 3 helps to show when frequency, phase, and time data are best for collection and analysisin arriving at root-cause faults of machinery problems.

Table 3 Where frequency, phase, and waveform data are best for analysis

Application/Problem Spectrum/FFT Phase Time WaveformUnbalance X X

Misalignment X X

Resonance X X X

Rolling Elements X X

Sleeve Bearings X X X

Gears X X

Electrical Faults X X

Looseness X X X

Flow Problems X

Very Low Frequency X

Cyclical Vibration XVariable Speed X X

Because of uncertainty of phase shifts in integrating raw measurement data, it is best to use

transducer native units in conducting time waveform analysis usually acceleration.

As was stated earlier, no windowing or overlapping should be performed in collecting thewaveform sample. In fact, as we are looking for a snap-shot, 1 average will suffice.


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To obtain adequate resolution, 4096 samples are usually enough (equivalent to a 1600-line

spectrum), and the time period should be sufficient to collect 6-10 repetitions of the event ofinterest, or cycles of the shaft under review.

Time synchronous averaging can be used if, and only if, it is necessary to isolate data to theparticular shaft under review. This is particularly useful for looking for cracked gear teeth and inlooking for reciprocating machines and tying events back to a specific crank angle.

Transducer mounting must be firm, with no rocking or movement, to provide the required time

domain data for pin-point analysis work to be performed.

As a general rule, time waveform data should be captured for the following selected analysissituations to enhance and support FFT data:

Low speed machines (less than 100 RPM).

Indication of true amplitude in situations where impacts occur such as assessment ofrolling element bearing defect severity.

Gearboxes.

Sleeve bearing machines with X-Y probes (2 channel orbit analysis).

Looseness.

Rubs.

Beats.

Impacts.

Use of the appropriate measurement unit.

Rolling element bearings, gears, looseness, rubs, impacts acceleration.

Sleeve bearing machine with x-y probesdisplacement.

Initially set up to observe 6 10 revolutions of the shaft in question.

Study the following indicators in the waveform.

Amplitude.

Amplitude Symmetry.

Time Symmetry (use RPM markers).

Beats / Modulation.

Impacts (shape and amplitude).

Time waveform data has proven to enhance and confirm observations in spectral data. It is notintended as a replacement for anything, but as yet an additional tool for providing better analysis

and confidence in established PdM programs.

Analyzing patterns and following a rigid set of guidelines prevents the analyst from cuttingcorners and skipping steps that might contain vital clues to the machinery faults. Looking at a

recorded waveform display and thinking about the orientation of the sensor can be a big help invisualizing the motion in a pattern and identifying possible sources of the problem.


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REFERENCES

1. Advanced TWF Techniques, Richard Burton, Rockwell Automation, 2005

2.

An Introduction to Time Waveform Analysis, Timothy A. Dunton, Universal Technologies,1999.

3. Machinery Vibration Analysis & Predictive Maintenance, Cornelius Scheffer and PareshGidhar, 2004

4. Signal Processing for Effective Vibration Analysis, Dennis H. Shreve, IRD Mechanalysis,

Inc. 1995

5. The Time Waveform, Stuart Courtney, Entek IRD International Inc., 1998

6.

Time Waveform Analysis, James E. Berry, Technical Associates of Charlotte, P.C., 1993

BIOGRAPHY

Dennis H. Shreve holds B.E.E. and M.Sc. E.E. degrees from The Ohio State University,with specialization in high-speed data communications. He has 42 years of experience in

designing and developing electronics and software systems and leading projects for real-

time industrial process monitoring and control applications. Over the past 23 years, he has

specialized in predictive maintenance (PdM) technologies and vibration detection, analysis,

and correction methods for maintaining machinery health. Dennis is certified by Technical

Associates and ANST as a Level III Vibration Analyst, and he is a Certified Maintenance

and Reliability Professional (CMRP). He is an active member of several professional

societies, including Vibration Institute, CMVA, SMRP, ISA, and I.E.E.E., where he has written several articles andconducted public seminars. Dennis is currently employed with Commtest, Inc. as Senior Staff Engineer for the

Channel Partner Sales organization.

back to basics with time waveform

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