Chapter 16 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 signa- ture. 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.

CHARACTERISTIC VIBRATION SIGNATURES

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 analysism, 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 oper- ating condition of critical plant systems.

TYPES OF SIGNATURE ANALYSIS

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

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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 perfor- mance improvement tool.

Automatic Trending Analysis

A predictive maintenance program utilizing a microprocessor-based vibration ana- lyzer 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/alm status reports.

The use of just these standard capabilities greatly reduces unscheduled failures. How- ever, 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 correc- tive 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 auto- mated 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 oper- ating parameters, such as load, will directly affect the signatures generated by a machine.

Unlike trending analysis, which is based on broadband and narrowband data, compar- ative 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 mainte- nance program, this form of trending is very effective in identifying changes in machine condition.

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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 fre- quency 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 dis- play, 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 water- fall 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 signa- tures, 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.

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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 nec- essary 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 (Le., 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 over- hauled, replaced, or when a new process setup is established. A current set of valid reference data is essential when performing comparative analysis.

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Table 16.1 Yibration Severity Standards*

Condition Machine Classes (IPS-PK) I I1 I11 IV

Good operating condition 0.028 0.042 0.100 0.156 Alert limit 0.010 0.156 0.255 0.396 Alarm limit 0.156 0.396 0.396 0.622 Absolute-fault limit 0.260 0.400 0.620 1 .ooo

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

Machine Class Descriptions: Class I

Class I1

Class I11

Class IV

Small machine-trains or individual components integrally connected with the complete machine in its normal operating condition (i.e., drivers up to 20 hp). Medium-sized machines (i.e., 20- to 100-hp drivers and 400-hp drivers on spe- cia1 foundations). Large prime movers (i.e., drivers greater than 100 hp) mounted on heavy, rigid foundations. Large prime movers (Le., drivers greater than 100 hp) mounted on relatively soft, lightweight structures.

Source: Derived by Integrated Systems, Inc., from I S 0 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 normal- ized 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 compar- ing the signatures of all measurement points on a common shaft. It is a useful tech- nique 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.

Industriul Standards One form of comparative analysis is direct comparison of the acquired data to indus- trial 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 x 0.3) and 5400 rpm (1800 x 3.0). A 3600-rpm machine would have a filtered narrowband between 1080 (3600 x 0.3) and 10,800 rpm (3600 x 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 micropro- cessor comparisons do not require knowledge of the machine-train or vibration analy- sis 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 operat- ing 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: multi- ple 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 dis- play 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 signa- ture 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 sub- tracted 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).

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Figure 16.3 Multiple-signature displuy.

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DIFFERENCE OF

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Figure 16.5 Difference of two signatures.