Vibration Fundamentals Episode 7 pptx

176 Vibration Fundamentals Figure 15.36 Angular sheave misalignment.. Such a vibration signature, referred to as frequency-domain data, is used in signature analysis to evaluate the dyna

174 Vibration Fundamentals

Figure 15.33 Bends that do not change shaft length generate radial forces only

Figures 15.32 and 15.33 illustrate the normal types of bent shafts and the force pro­files that result

V-Belts

V-belt drives generate a series of dynamic forces, and vibrations result from these forces Frequency components of such a drive can be attributed to sheaves and belts The elastic nature of belts can either amplify or damp vibrations that are generated by the attached machine-train components

Sheaves

Even new sheaves are not perfect and may be the source of abnormal forces and vibration The primary sources of induced vibration due to sheaves are eccentricity, imbalance, misalignment, and wear

Eccentricity

Vibration caused by sheave eccentricity manifests itself as changes in load and rota­tional speed As an eccentric drive (Figure 15.34) sheave passes through its normal rotation, variations in the pitch diameter cause variations in the linear belt speed An eccentric driven sheave causes variations in load to the drive The rate at which such variations occur helps to determine which is eccentric An eccentric sheave also may

175 Failure-Mode Analysis

Figure 15.34 Eccentric sheaves

Figure 15.35 Light and heavy spots on an unbalanced sheave

appear to be unbalanced However, performing a balancing operation will not correct the eccentricity

Imbalance

Sheave imbalance may be caused by several factors, one of which may be that it was never balanced to begin with The easiest problem to detect is an actual imbalance of the sheave itself A less obvious cause of imbalance is damage that has resulted in loss

of sheave material Imbalance due to material loss can be determined easily by visual inspection, either by removing the equipment from service or using a strobe light while the equipment is running Figure 15.35 illustrates light and heavy spots that result in sheave imbalance

176 Vibration Fundamentals

Figure 15.36 Angular sheave misalignment

Figure 15.37 Parallel sheave misalignment

Misalignment

Sheave misalignment most often produces axial vibration at the shaft rotational fre­quency (1×) and radial vibration at one and two times the shaft rotational frequency (1× and 2×) This vibration profile is similar to coupling misalignment Figure 15.36 illus­trates angular sheave misalignment and Figure 15.37 illustrates parallel misalignment

Wear

Worn sheaves also may increase vibration at certain rotational frequencies However, sheave wear is more often indicated by increased slippage and drive wear Figure 15.38 illustrates both normal and worn sheave grooves

177 Failure-Mode Analysis

Figure 15.38 Normal and worn sheave grooves

Belts

V-belt drives typically consist of multiple belts mated with sheaves to form a means of transmitting motive power Individual belts, or an entire set of belts, can generate abnormal dynamic forces and vibration The dominant sources of belt-induced vibra­tions are defects, imbalance, resonance, tension, and wear

Defects

Belt defects appear in the vibration signature as subsynchronous peaks, often with harmonics Figure 15.39 shows a typical spectral plot (i.e., vibration profile) for a defective belt

Imbalance

An imbalanced belt produces vibration at its rotational frequency If a belt’s perfor­mance is initially acceptable and later develops an imbalance, the belt has most likely lost material and must be replaced If imbalance occurs with a new belt, it is defective and also must be replaced Figure 15.40 shows a spectral plot of shaft rotational and belt defect (i.e., imbalance) frequencies

Resonance

Belt resonance occurs primarily when the natural frequency of some length of the belt

is excited by a frequency generated by the drive Occasionally, a sheave also may be excited by some drive frequency Figure 15.41 shows a spectral plot of resonance excited by belt-defect frequency

Belt resonance can be controlled by adjusting the span length, belt thickness, and belt tension Altering any of these parameters changes the resonance characteristics In most applications, it is not practical to alter the shaft rotational speeds, which also are possible sources of the excitation frequency

Resonant belts are readily observable visually as excessive deflection, or belt whip It can occur in any resonant mode so inflection points may or may not be observed

178 Vibration Fundamentals

Figure 15.39 Typical spectral plot (i.e., vibration profile) of a defective belt

Figure 15.40 Spectral plot of shaft rotational and belt defect (i.e., imbalance) frequencies

179 Failure-Mode Analysis

Figure 15.41 Spectral plot of resonance excited by belt-defect frequency

Figure 15.42 Examples of mode resonance in a belt span

180 Vibration Fundamentals

along the span Figure 15.42 illustrates first-, second-, and third-mode resonance in a belt span

Tension

Loose belts can increase the vibration of the drive, often in the axial plane

In the case of multiple V-belt drives, mismatched belts also aggravate this condition Improper sheave alignment can also compromise tension in multiple-belt drives

Wear

Worn belts slip and the primary indication is speed change If the speed of the driver increases and the speed of the driven unit decreases, then slippage is probably occur­ring This condition may be accompanied by noise and smoke, causing belts to over­heat and be glazed in appearance It is important to replace worn belts

SIGNATURE ANALYSIS

Most failures of rotating and reciprocating machinery exhibit characteristic vibration profiles that are associated with specific failure modes This phenomenon is due to the forcing function, caused by a developing defect, having a unique characteristic signa­ture None of the filtered bandwidth monitoring methods provides the means to detect and evaluate these unique profiles Signature analysis provides this capability and its use is required in a comprehensive predictive maintenance program

C HARACTERISTIC V IBRATION S IGNATURES

A vibration signature provides a clear, accurate snapshot of the unique frequency components generated by, or acting on, a machine-train Such a signature is obtained

by converting time-domain data into its unique frequency components using a fast Fourier transform (FFT) Such a vibration signature, referred to as frequency-domain data, is used in signature analysis to evaluate the dynamics of the machine

Frequency-domain vibration signatures form the basis for any predictive maintenance program designed to detect, isolate, and verify incipient problems within a machine-train These signatures are the basic tools used for in-depth analysis methods such as fail-ure-mode, root-cause, and operating dynamics analyses Operating dynamics analysisTM, which is beyond the scope of this module, uses vibration data and other process parameters, such as flow rate, pressure, and temperature, to determine the actual oper­ating condition of critical plant systems

T YPES OF S IGNATURE A NALYSIS

In general, new or immature predictive maintenance programs are limited to compar­ative analysis or waterfall trending Although these comparative techniques provide

181

182 Vibration Fundamentals

the ability to detect severe problems, they cannot be used to isolate and identify the forcing functions or failure modes These methods also are limited in their ability to provide early detection of incipient problems

As the predictive maintenance program matures, root-cause analysis and operating dynamics analysisTM methods can be used With the addition of these more advanced diagnostic tools, vibration signatures become an even more valuable process perfor­mance improvement tool

Automatic Trending Analysis

A predictive maintenance program utilizing a microprocessor-based vibration ana­lyzer and a properly configured database automatically trends vibration data on each machine-train In addition, it compares the data to established baselines and generates trend, time-to-failure, and alert/alarm status reports

The use of just these standard capabilities greatly reduces unscheduled failures How­ever, these automated functions do not identify the root causes behind premature machine-train component failures In most cases, more in-depth analysis allows the predictive analyst to identify the reason for pending failure and to recommend correc­tive actions to prevent a recurrence of the problem Again, the specific microproces-sor-based system used determines how much manual effort is required for more in-depth analysis

More In-Depth Trending Analysis

More in-depth analysis is called for when the automatic trending analysis described in the previous section indicates that a machine-train is exhibiting excessive vibration Obviously, machine-trains that are operating within acceptable boundaries do not require further investigation Care should be taken, however, to ensure that the auto­mated functions of the predictive maintenance system report abnormal growth trends

as well as machine-trains that are actually in alarm

Comparative Analysis (Waterfall Trending)

FFT signatures that are collected on a regular schedule provide a means of trending that can help the analyst identify changes in machine condition Changes in the oper­ating parameters, such as load, will directly affect the signatures generated by a machine

Unlike trending analysis, which is based on broadband and narrowband data, compar­ative analysis is a visual comparison of the relative change of the machine-train’s full vibration signature and its discrete frequency components over a period of time Because vibration signatures are acquired at regular intervals in a predictive mainte­nance program, this form of trending is very effective in identifying changes in machine condition

183 Signature Analysis

Figure 16.1 Illustration of a waterfall plot

Displaying the signatures in a waterfall or multiple-spectra display (sequentially by data-acquisition time) allows the analyst to easily see the relationship of each fre­quency component generated by the machine (see Figure 16.1) Any significant change in the amplitude of any discrete frequency is clearly evident in this type of dis­play, which is used in many of the figures in subsequent sections

Although comparative analysis can be used to help the analyst identify specific changes that are generated by process changes, each signature must be normalized for process variations Therefore, as part of the acquired data set, the analyst must record the specific process conditions for each data set With this information and the water­fall display of vibration signatures, the analyst can quantify the changes that result from variations of these parameters

Developing problems within a machine-train can be identified by comparing the FFT signature to the following: (1) a baseline or reference signature, (2) previous signa­tures, or (3) industrial standards This method determines if a potential problem exists and can be used to isolate within the machine-train the probable source of developing problems

184 Vibration Fundamentals

Figure 16.2 Comparison to baseline reference

Baseline or Reference Signatures

A series of baseline or reference data sets should be taken for each machine-train included in a predictive maintenance program (Figure 16.2) These data sets are nec­essary to compare with trends, time traces, and FFT signatures that are collected over time Therefore, baseline data sets must be representative of the normal operating condition of each machine-train in order to have value as a reference

In integrated process plants where most machines are subject to variable operating conditions, this exercise requires more than one reference data set for each machine-train To be of benefit, a series of baselines must be acquired from each machine-train, each of which should accurately represent a specific operating variable (i.e., product, machine setup, load, etc.) It is important that all data sets (whether baseline data or current operating data) be clearly identified in order to be useful Current operations data must be compared to a reference data set having the same operating conditions (Figure 16.2)

Note that baseline references must be updated each time a machine-train is over­hauled, replaced, or when a new process setup is established A current set of valid reference data is essential when performing comparative analysis

Signature Analysis 185

Table 16.1 Vibration Severity Standards*

Good operating condition

Alert limit

Alarm limit

Absolute-fault limit

I 0.028 0.010 0.156 0.260

II 0.042 0.156 0.396 0.400

III 0.100 0.255 0.396 0.620

IV 0.156 0.396 0.622 1.000

* Applicable to a machine with running speed between 600 to 12,000 rpm Narrowband setting: 0.3× to 3.0× running speed

Machine Class Descriptions:

Class I Small machine-trains or individual components integrally connected with the

complete machine in its normal operating condition (i.e., drivers up to 20 hp) Class II Medium-sized machines (i.e., 20- to 100-hp drivers and 400-hp drivers on spe­

cial foundations)

Class III Large prime movers (i.e., drivers greater than 100 hp) mounted on heavy, rigid

foundations

Class IV Large prime movers (i.e., drivers greater than 100 hp) mounted on relatively

soft, lightweight structures

Source: Derived by Integrated Systems, Inc., from ISO Standard 2372

Nonbaseline Signatures

Visual comparison of two signatures can enable the analyst to determine if a problem

is developing As with the case of filtered energy data, all signatures must be normal­ized for process variables such as speed, load, etc., in order for comparisons to be valid Direct comparison is useful only when both data sets reflect the same operating conditions or parameters

Common-shaft analysis is used to identify the strongest vibration by visually compar­ing the signatures of all measurement points on a common shaft It is a useful tech­nique for isolating the source of abnormal vibrations Although this method does not absolutely identify the problem, it reduces the number of machine components that must be inspected or evaluated to correct the problem

Industrial Standards

One form of comparative analysis is direct comparison of the acquired data to indus­trial standards or reference values The vibration severity standards presented in Table 16.1 were established by the International Standards Organization (ISO) These data are applicable for comparison with filtered narrowband data taken from machine-trains with true running speeds between 600 and 12,000 rpm The values from the table include all vibration energy between a lower limit of 0.3× true running speed

186 Vibration Fundamentals

and an upper limit of 3.0× For example, an 1800-rpm machine would have a filtered narrowband between 540 (1800 × 0.3) and 5400 rpm (1800 × 3.0) A 3600-rpm machine would have a filtered narrowband between 1080 (3600 × 0.3) and 10,800 rpm (3600 × 3.0)

Microprocessor Comparisons

Many of the microprocessor-based predictive maintenance systems also allow direct comparisons of the relative strengths of each frequency component Such micropro­cessor comparisons do not require knowledge of the machine-train or vibration analy­sis techniques, but both data sets must be acquired under the same operating conditions Increases in relative strength indicate more vibration and a developing problem in the machine-train

Cross-machine comparison is an extremely beneficial tool to the novice analyst Most vibration monitoring systems permit direct comparison of vibration data, both filtered window energy and complete signatures, acquired from two machines This capability permits the analyst to directly compare a machine that is known to be in good operat­ing condition with one that is perceived to have a problem There are several ways that cross-machine comparisons can be made using microprocessor-based systems: multi­ple plots, ratio, and difference

Multiple Plots

Two or more signatures can be shown on a single display This method permits the analyst to directly compare the actual signatures generated at each measurement point on both the suspect and a reference machine-train This multiple-signature dis­play permits direct comparison of each frequency component within the signatures (Figure 16.3)

Ratio Analysis

With this technique, the signature from the suspect machine is divided by the signa­ture of the reference machine, frequency by frequency The resultant display shows the relative amplitude, both positive and negative, of each frequency component in the suspect machine-train (Figure 16.4) As an example, the display may indicate that the gear-mesh energy in the suspect machine is 40% higher than that in the reference machine (i.e., ratio = 1.4) With this information, the analyst can isolate specific machine components that are potential problems

Difference Analysis

With the difference analysis technique, the signature of the reference machine is sub­tracted from that of the suspect machine, frequency by frequency The resultant plot displays the difference value, positive and negative, of each frequency component within the two (see Figure 16.5)

187 Signature Analysis

Figure 16.3 Multiple-signature display

Figure 16.4 Ratio of two signatures

188 Vibration Fundamentals

Figure 16.5 Difference of two signatures