Cb m Decision Making With

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CBM Decision Making with Expert Systems

ByDaming LinandMurray WisemanOptimal Maintenance Decisions (OMDEC) Inc.Extracted from Chapter 11 of Reliabiltiy-centered Knowledge

Depending on the physics governing a given application, we learned, in Chapter 7. (page 95), that wemay choose from a variety of algorithms with which to carry out the signal processing portion ofCBM. Decision making, (the third CBM sub-process), proceeds similarly, using one or more of adiverse array of decision support tools. In Chapter 10. Example 1 Creating a decision model (page 127)we developed a CBM decision policy usingstatisticalmodeling techniques and software. A decisionpolicy assists maintenance personnel to interpret and act upon a set of condition monitoring (CM) data.Extensive human knowledge and experience may be available with which to build a CBM decisionpolicy. A rule-based expert systemencapsulates known relationships between CM data and thedeterioration in an asset that takes place due to one or more failure modes. An algorithm (known as aninference engine) applies the knowledge base to the current set of CM data. In this chapter we describe

an expert system developed by DLI Engineering[1]

called ExpertALERT.

Figure 11-1 CBM signal processing and Decision making using an Expert System

Figure 11-1 outlines the signal processing and decision making portions of this CBM approach. It tracesthe flow of information through the signal processing steps (steps 1-5) and the decision makingprocedure (step 6) that uses a rule-based expert system.

Each machine to be monitored is set up with permanent testpoints[2]

positioned strategically (Figure11-2) in relation to the components of interest. The equipment is monitored using ExpertALERT over

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a period of time thereby establishing a baseline spectrum for each test point[3]

and each orientation.The baseline spectra are updated automatically by the software and set at the average + 1 standarddeviation.

Figure 11-2 An example of test point locations showing the three axes - Axial, Radial, and Tangential

The six steps of Figure 11-1 are described in each of the following sections.

Step 1 Data normalization

We desire to scale the abcissa of the spectrum in multiples (orders) of the forcing frequency.[4]

If theshaft speed is known (from a tachometer signal) the algorithm accomplishes this directly. If it is notknown a strong peak is chosen in a window around the nominal speed, or a number of nominal speeds(in the case of a variable speed drive) and the algorithm can successfully match peaks, harmonics andsidebands in order to determine the correct speed for normalizing the spectrum.

The normalization procedure also converts vibration amplitudes to a logarithmic scale in units of VdB.This assists in the visualization of significant, yet low energy peaks, alongside the dominant peaks dueto the fundamental forcing frequency. The VdB scale simplifies the interpretation of changes invibration levels, for example:

A 6VdB increase = a doubling of vibration amplitude A 20 VdB = an increase in vibration amplitude by 10 times.

Step 2 The screening matrix

Next, automated spectral peak extraction and a noise floorcalculation are performed. The resulting datapopulates a screening matrix. The columns of the screening matrix represent 10 preselected orders ofshaft rate (for example 1x, 2x, .10x), the two highest non-synchronous peaks in a low and high range

spectrum, and a noise floor[5]

value.

As an example, let us assume an equipment item has two test points. Then the screening matrix willhave (10 orders + 2 peaks x 2 ranges) x 3 orientations x 2 test points + 1 noise floor = 85 columns. Onerow of the screening matrix will hold the changes in amplitude from the previous inspection. A secondrow will hold the deviations from the baseline spectrum. A third row will hold the correspondingvibration amplitudes. Hence, in this instance, 85 x 3 rows=255 extracted features will have been placedinto the screening matrix, ready for further processing.




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The noise floor calculation measures any general increase in random noise. Both impacts and randomnoise in a time waveform cause the spectrum to become elevated. As bearings wear, they typicallyproduce larger quantities of non-periodic vibration and impacts. This raises the noise floor of thespectrum. The automated diagnostic system uses an algorithm to calculate the level of the noise floor.This value is then compared to a baseline value. Increases in noise floor level add to the severity (seestep 6) of the bearing wear diagnosis and may even trigger a diagnosis in certain cases when bearing

tones are not evident.

Step 3 Cepstrum analysis

A cepstrum transformation[6]

of the fft spectrum is performed next. A cepstrum plot highlights series ofspectral peaks that are evenly spaced in the spectrum. These are called harmonics Harmonics can besynchronous (multiples of shaft speed) or non-synchronous. The algorithm searches the spectrum fornon-synchronous harmonics and any sidebands. If found they are flagged as possible bearing tones, tobe processed further in steps 5 and 6.

The physics of each situation dictate the signal processing method selected. Non- synchronous peaks,such as those at 3.61 and 7.22 orders (Figure 11-4), are candidates for bearing tones that signal

bearing faults. If, in addition, the non-synchronous peaks display sidebands spaced at orders of the shaftspeed, an inner race defect is likely. Figure 11-5 illustrates the physical explanation for bearing tonesand the appearance of sidebands, with respect to to an inner race spall.

Figure 11-5 Physical explanation of non-synchronous peaks and their 1x sidebands related to an inner race spall.

Step 4 Demodulation

Figure 11-3 Cepstrum showing peaks with 1x and 3.61xspacings

Figure 11-4 Spectrum showing the synchronous andnon-synchronous harmonics and their 1x spacedsidebands. The abcissa is scaled in orders ormulitples of the shaft speed.




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Demodulation (also called envelope detection) is a signal processing technique used by ExpertALERTto supplement and verify the information drawn from the cepstrum and spectrum analyses.Demodulation provides an independent confirmation of bearing defects.

If there is a spall on a bearing race, each time a ball passes it will impact and ring the bearing causingit to resonate at high frequencies. The resulting vibrations can be demodulated in order to extract theforcing frequency that is causing the ringing. The forcing frequencies will appear as peaks in the

demodulated spectrum. If they match the bearing tones from the screening matrix and the cepstrum, theyprovide further confirmation of a bearing defect. A distinct advantage of demodulation is that highfrequencies do not travel far in a machine. Thus the demodulation process can localize the defectivebearing. For example, if you see bearing tones in the narrow band spectral data from two differentlocations on the machine at the same frequency, and the demod data has matching peaks at one location(but not the other), you can assume that the common location is the one with the bearing problem. The

spectra of Figure 11-6, Figure 11-7, Figure 11-8, and Figure 11-9 illustrate this point precisely.[7]

Figure 11-6 Spectrum from motor location showing bearing tone peak

Figure 11-7 Demodulated spectrum from motor location showing matching peak




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Figure 11-8 Spectrum from pump location showing same bearing tone

Figure 11-9 Demodulated spectrum from pump location, but showing no bearing tones. Hence ExpertALERT canconclude that the bearing defect is on the motor.

Step 5 Component specific diagnostic matrices

The screening matrix is transformed into component specific diagnostic matrices (CSDMs). Thistransformation extracts values at specific frequencies that characterize possible faults in a givencomponent. It is interesting to note that the techniques of Steps 2, 3, and 4 require no specific knowledgeof bearing geometry (e.g. number of rolling elements, inner and outer race diameters, and so on) for the

accurate detection of developing faults.[8]

Step 6 Decision making

Steps 1 to 5 may be considered the signal processing portion of ExpertALERT. They extract informativefeatures from the raw vibration data upon which the reasoning engine of the expert system may nowoperate. Step 6 performs the decision making function, interpreting the extracted features andidentifying the likely fault. In Step 6 each CSDM is processed through a series of diagnostic templatesconsisting of rulesthat pass or fail every fault known to occur in the component. Furthermore, theexpert system computes ascorebased on the features excedance above the threshold value in each

rule.[9]

The knowledge in the diagnostic templates was developed from an understanding of the physicsof the machinery and its causal relationship with the monitored data.




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A simple example is the rule for imbalance. This rule checks the matrix elements (of the CSDM) thatcontain the rotational rate levels and exceedances over baseline. The rule then determines whether thesevalues are are high in a radial direction. If so, other checks determine that the problem is notmisalignment or looseness. The imbalance diagnosis is finally confirmed.

Figure 11-10 Vertical pump and 1x vibration readings

As an example, consider (for simplicity only the 1x vibration levels of) the vertical motor andcentrifugal pump (with coupling), in Figure 11-10. Excessive 1x vibration may indicate motorimbalance, pump imbalance, angular misalignment, foundation horizontal flexibility, a radial or thrustbearing clearance problem, or motor cooling fan blade damage. Expert system rules based on knowledgeof the configuration need to deduce the fault and identify the faulty component.

Looking at the axial and radial data at both locations we might surmise angular misalignment since 1xaxial is abnormally high at both motor and pump. Alternatively, it could be motor imbalance or pump

imbalance, since 1x radial is abnormally high at either end and radial is higher than axial. Axial motionis, in fact, characteristic (due to rocking) of unbalance in a vertical pump. Another characteristic of avertical pump is that one direction, the direction of external structural support, is always stiffer than theother directions. The radial axis in this case is the direction of structural flexibility, so that radially thepump is being wagged by the motor imbalance. The low 1x levels at the pump in the tangentialdirection can be explained by the fact that the tangential axis is the direction of high structural stiffnessand therefore the tangential component of the vibration due to motor imbalance does not transmit to thepump.

Rules are activated by machinery component type as defined by the user in the ExpertALERT software.For example, a rule for bearing wear in a compressor will look slightly different from the rule forbearing wear in an AC motor. Each individual machine component type may have numerous rules forbearing wear. If the the extracted features satisfy the requirements for a rule, it means the fault conditionexists.

After information has been extracted from the spectra as described above in steps 1 to 5, it is passedthrough all of the rules that apply to the general machine type to see if any faults exist. The rules areempirically based on thousands of machine tests collected over more than 20 years and are constantlyrefined as new information becomes available. If a rule is edited for any reason, the change is runthrough all past diagnoses to ensure that it does not change any previously correct results.

Motor (VdB at 1x)

Orientation Amplitude Exceedenceover baseline

AxialRadialTangential

105118117

71010

Pump (VdB at 1x)

AxialRadialTangential

10411392

992




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A typical rule looks something like this in terms of its logic:

1. If the sum of the exceedance over baseline of all perceived bearing tones in all three axes and alltest points (Cepstrum confirmed) is higher than a threshold, or the sum of the noise floor readingsfrom all spectra has increased over the baseline or alarm by a certain amount, then the rule passes.

2. If the sum of the amplitudes of all of the perceived bearing tones exceeds some threshold then therule passes.

3. If none of the perceived bearing tones are above a minimum threshold, the rule does not pass.4. If the sum of the shaft rate harmonics from 16x to 100x are above some value, add to the severity.5. If the noise floor is above some level add to the severity, and if its above a higher level, add more

to the severity.6. If the sum of the other un-defined peaks that were not confirmed by Cepstrum are above some

threshold, add more to the severity.7. If sub harmonics of the shaft rate have exceeded the baseline by a certain amount, add to the

severity.

Note that these rules are empirically based. Which is to say, the rule thresholds for absolute levels or forexceedances over a baseline, have been tweaked until they come out with the correct answer as

determined by a human expert and/or direct field feedback. In other words, the thresholds mentioned inthe example rule above, have been tuned to come out with the correct answer for any machine to whichthis particular rule applies. There are sufficient rule templates for each machine type to catch practicallyall possible bearing wear patterns that may exist in the data.

Once a fault has been diagnosed, the user will continue to monitor the machine and look for changes inseverity of the fault. The rate at which the severity increases gives a good indication of when thebearings should be overhauled.

The amounts by which the values in the CSDM exceed the threshold values (set up in the rules based onexperience and knowledge) is scored and converted into arelative severity. This normalizes a scale withwhich to judge the state of health of each component. Thus the relative severity for all components in

the equipment can be trended on a single graph, as in Figure 11-11. The graph provides a decisionsupport tool for performing a corrective action on a component whose severity is high or has increasedsubstantially. In the following section, we will propose to extend the automated diagnosis one stepfurther to extimate remaining life and provide an optimized repair decision.

Figure 11-11 Severity graphs for an equipment item with three components

A proposed hybrid decision tool

Following step 6, the automated diagnostic tools hand over their findings to the human decision makers.Can we process each diagnostic fault and its respective severity one step further to provide:




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1. A residual life estimate relative to each failure mode, and2. An optimized decision as to whether

i. to effect repair immediately, orii. to repair within a particular time period from the current time, oriii. to continue operation until the next inspection. ?

The severity values computed for each fault, as well as the absolute and relative values of the relevantfeatures, may be used as covariates in a proportional hazard model such as that described in Chapter 10.The next section describes the ABB fault simulator that may be use to demonstrate this proposed

extension to ExpertALERTs output report.

The ABB fault simulator

Figure 11-12 The fault simulator (top left) gradually induces one or more failure modes (for example, misalignment orunbalance). The failure mode (unbalance) causes the failure mechanism (right) to proceed towards failure. Thefailure is the loss of function to hold the Tee in place by spring friction forces under the stress of vibration forces

transmitted through the structure.

In the fault simulator, a spring and friction failure mechanism has been set up with the followingcharacteristics desirable for the study of a failure modeling and prediction methodology.

1. A functional failure is clearly defined (by the release of the tee causing the golf ball to trigger aswitch).

2. The (random variable) time to failure can depend both on working age and CM data.3. A life cycle can be as small as 1 minute, permitting a large sample of life cycles from which to

build and subsequently test the predictive model.

How predictive can such a model be?The goodness (predictability) of the model depends on two factors:

1. How good the data is (its intrinsic information content regarding a progressing failure mode), and2. How big the sample is (the number of life cycles used to build the model).

The better the data the smaller the sample you need. The less the data correlates with the targetedfailure mode, the larger the sample you need for obtaining a good model.




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Figure 11-15 Histogram showing the errors in replacement time estimate over 678 inspections. For example the TREcalculated at 412 inspections were within 5% of the actual (functional or potential) failure time.

The hazard function curves (in Figure 11-16) for potential failures and functional failures provides anoverall performance check on the effectiveness of the CBM program.

Figure 11-16 Hazard functions for potential and functional failures

If the difference between TF (total failures) and the FF (functional failures) hazard curves is small, thatindicates that the CBM program is effective. That is, functional failures (those that have importantconsequences) are being preempted by the CBM detection and correction of potential failures (that havenone or relatively minor consequences).




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Figure 11-17 ExpertALERT operating the ABB Asset Optimizer Workplace

Figure 11-17 illustrates a typical report issued by ExpertALERT. It contains quantitative informationrelating to the detected fault as well as a recommendation and a Figure of Merit indicating the faultseverity.. We propose to link these outputs from ExpertALERT to an EXAKT decision agent. The agentwill apply a model of the severity ratings and other data computed by ExpertALERT in order toenhancing the ExpertALERT report with an optimized recommendation and a time-to-failure estimate.

[1]www.dliengineering.com,Automated Bearing Wear Detection, Alan Friedman, Published in Vibration Institute

Proceedings 2004[2]

Testpoints may be equiped with permanent triaxial accelerometers, or a triaxial accelermoter connected to a portable data

collector may be used. The barcoded test points must offer a solid screwed mounting for accelerometer.[3]

In both a low and high frequency range

[4]This simplifies distinguishing the non-synchronous peaks and their sidebands from the dominant forcing shaft frequency

and its harmonics. A necessary step in the diagnostic process.

[5]An increase in the noise floor level is an indication of impacting and non-periodic (or random) vibration. Both of these

are associated with later stage bearing wear.

[6]One may say in a general sense that the more harmonics and sidebands present, the worse the condition of the bearing.

Thus, not only does one wish to know if a peak is part of a larger family of peaks, one also wants to get an idea of how muchenergy is contained in the series. Cepstrum analysis is used for automating this task. The Cepstrum is a power spectrum of apower spectrum of a waveform; therefore, any periodicities in the spectrum (such as harmonic series or sideband families)will clearly appear as a peak in the Cepstrum.




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[7]Alan Friedman, DLI Engineering,Demodulation- June 1999 issue of P/PM

[8]Nevertheless, the CSDM may include specific frequencies based on bearing manufacturing data, and knowledge rules

may include these frequencies, thus extending diagnostic confidence.[9]

Rule thresholds include both absolute amplitudes as well as exceedences over (mean + 1 sigma) baseline.


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