Introduction

An oil analysis program without a series of goals islike a ship without a rudder, directionless and proneto going off track at any moment. These goals may beas simple as "finding a glycol leak in an engine beforea catastrophic failure occurs" or they may be moresophisticated such as "increasing productivity andequipment uptime by 5% through improved filtrationand fluid cleanliness." Whatever your motivation, awell planned oil analysis program needs a series oflimits or alarms by which the success or failure ofreaching these pre-defined goals can be gauged.

Basic Limits - The Status Quo

Most oil analysis users rely on their commercial oilanalysis lab to provide flagging limits. These limitsmay be based on the labs statistical analysis of manytens of thousands of similar components, previoussample results from the component in question, OEMlimits provided by the component manufacturer, ordata supplied by the lubricant manufacturer. A goodlab will consider all of the above when flagging datafrom each set of test results. Usually labs provide two(sometimes three) warning levels such as caution toindicate the onset of a problem requiring noimmediate action, and critical to warn of a moreserious problem condition. Critical and caution levelsmay be set as positive going limits, such an increase inwear metal concentration, negative going, such asdecrease in total base number (TBN) or RBOT value,or both positive and negative such as viscosity whereboth an increase in viscosity due to for example oiloxidation, and a decrease caused by shearing may beexpected.

Statistical Analysis

Many labs perform statistical type calculations todetermine caution and critical levels. For example,elemental wear metals limits are often set based on anaverage over several hundred or even thousandsample histories, with caution and critical levels set atone and two standard deviations higher than theaverage value. While this approach has merit formobile equipment, particularly when the data setfrom which the calculation is performed is large, forsmaller sample sizes or stationary industrialequipment, meaningful statistical analysis is difficult.For example, two manufacturing plants may use thesame make and model of gear reducer, however, onemay run intermittently, it may be loaded at only 50%of its maximum rated capacity or it may run outsidein sub-zero temperatures. All of these factors willaffect the wear rates making comparison betweendifferent components almost impossible. In this typeof situation, it is usually better for the end user to settheir own alarms and limits.

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Advanced Strategies for Selecting Oil AnalysisAlarms and Limits

Mark BarnesThe Fluid Life Corporation

9321 48th StreetEdmonton, AB

Canada

Traditionally, oil analysis users have relied upon their commercial oil analysis lab, lubricant supplier or equipmentOEM to provide warning limits for routine oil analysis tests. While this approach has some merit in a predictiveapproach to oil analysis, particularly for mobile equipment where wear metals limits and other parameters arereasonably well defined, plant equipment oil analysis users and those wishing to take a proactive approach to oilanalysis have started to set their own limits based on their own unique goals or equipment environment. In thispaper, we review some of the methods available to set basic alarms and limits as well as discuss advanced strategiesfor data evaluation and interpretation.

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Figure 1: Cumulative frequency distribution approachto setting alarms. Data shows typical iron wear (in

ppm) for an diesel engine at 300 hours oil service (Ref:Noria Corp./KOWA)

Another approach which is often used is to look at thecumulative frequency distribution. This is illustratedin Figure 1. In this approach, a series of ranges aredefined and the number of events or sample resultsfor which fall inside each range is counted. The totalnumber events in each range are then expressed as apercentage of the total number of observations, whichin turn can be related to a cumulative percentage.Using this approach, cautionary limits are often set at85% (i.e., the value below which 85% of all samplesfall) and critical limits at 95%. Again, like all statisticalapproaches to setting limits, this method relies onhaving an appropriately large data set of equipmentoperating under similar conditions.

Another factor which must be considered, partic-ularly when looking at wear metal concentrations is tolook at the wear rate, rather than the absolute value ofa test parameter. For example, an engine maynormally produce 25 ppm of iron during a 250 houroil change interval. However, if this oil changeinterval is stretched to 300 hours and no otherproblems occur, it would be expected that the amountof iron in the sample might increase to 30 ppm, 1 ppmfor every 10 hours of operation. Similarly, mostcomponents exhibit significantly higher wear rateswhen first installed due to break-in effects. Ifprovided the correct information, most oil analysislabs will allow for these effects, however all too often,samples are sent to the lab without component hours,making the labs job of providing meaningful flagginglimits almost impossible. In many circumstances, itmay be more appropriate to set limits in terms of wearrates (i.e., 1 ppm/10 hours) rather than set a discretenumber limit such 25 ppm.

OEM Limits

Many equipment OEM's, particularly in mobile fleet

applications, provide specificguidelines for wear metals, contam-inants (such as dirt, soot and fuel)and lubricant properties. Often theselimits are set using the samestatistical analysis methods used bycommercial oil analysis labs. Whilethese limits provide good generalguidelines for interpreting oilanalysis data, they should beconsidered just that, guidelines.

For example, the author wasquestioned by a locomotive engine manufacturerabout flagging limits applied to one of their engines inuse by a major Canadian railroad company. Thepertinent data for this case study is illustrated inFigure 2, showing the correlation between sodiumconcentration, measured to indicate a coolant leak,and lead, the main component of the engine bearingBabbitt overlay layer. The question that was posed bythe engine manufacturer was "why did you flagsodium as unacceptable at 69 ppm when ourcautionary limit for sodium is set at 200 ppm?". Theanswer to this question lies in the data illustrated inFigure 2. When sodium levels are relatively low (<25ppm), the amount of bearing wear, as illustrated bythe lead levels is also comparatively low. However,when the sodium values spikes at 69 ppm, only 35%of the OEM's "allowable" limit, the amount of leadgenerated has tripled. Obviously elevated lead levelsare an indication of elevated wear and acorresponding reduction in bearing life.

Figure 2: Correlation between sodium (from a coolantleak) and lead (Babbitt wear) for a locomotive engine

(Ref: Fluid Life Corp.)

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0-54 27 42 35 35 55-109 82 35 30 65 110-164 137 14 12 77 165-219 192 6 5 82

540-594 567 1 1 98 595-649 622 1 1 99 650-677 677 1 1 100 total -- 118 100 --

date sodium/ppm lead/ppm 99/05/26 21 2 99/06/26 24 2 99/07/18 17 1 99/08/29 69 7 99/11/02 6 2

If this engine had been left unchecked the sodiumlevels would have continued to rise, along with leadproduction, significantly reducing the life expectancyof the engine bearings. It is conceivable that if thisengine had been left to run until the sodium levelsreached the recommended 200 ppm warning limit,the wear rate may be ten times higher than undernormal operating conditions. Fortunately in this case,the customer heeded the lab's warning, inspected theengine and located a cooler leak. The take homemessage is that caution should be exercised whenrelying on OEM flagging limits. Again, it is far betterto set your own limits based on your own data.

Lubricant Manufacturer Limits

Most lubricant manufacturers are able to providelimits for key fluid property tests such as viscosity,neutralization numbers or RBOT number. Just likeOEM limits, fluid property test limits provided by alubricant manufacturer should be consideredguidelines. For example, a lube supplier may set acautionary limit for total acid number (TAN) at 3.0,above this value, the oil is likely to thicken or maycause corrosive wear to occur. However, dependingon the situation and metallurgy of the component,significant corrosive wear may have already occurredwhen the TAN value reaches 2.5.

These types of limits can however be valuable inallowing the end user to base oil changes not just onguess work or the conservative estimates provided byOEM's but on hard fast scientific data. For example,the author was asked by a reliability coordinator at alarge pulp mill whether the maintenance engineersdecision to change oil in their turbo-generator system"because it hadn't been changed for 5 years" wasjustified. After conducting an RBOT test, it was foundthat the RBOT value, a measure of the degree ofdegradation of a lubricating oil was at 90% of the newoil value, indicating that the oil was in excellentshape. Based on one single test, and knowing theappropriate new oils specifications and limits fromthe lubricant supplier saved this mill $25,000 for anunnecessary oil change. However, just like OEMlimits, it is unwise to put too much faith in lubemanufacturers limits, without considering otherfactors such as wear rates and contamination levels.

User Defined Limits

Once you are familiar with the test methods beingused and has sufficient historical data, it is best tostart setting you own limits since these limits can beset to reflect the real life conditions and reliabilitygoals in place. For example, the author was asked bythe chief engineer of a coastal ferry for information on

what limits they should be setting for water in theirstern propeller tubes. After checking with the OEMand lube supplier, nobody could supply reliableguidelines for sea water ingression limits. Discussingthe problem with the engineer, it was determined notto be specifically the water, which in a stern tube isvery difficult to eliminate, but the electro-chemicalcorrosive effects of salt and other minerals present insea water that was the problem. Based on a trendanalysis of this ships historical data, which is shownin Figure 3, it was deduced that by using sodium as aflag instead of water as an indicator of sea wateringression a limiting value of 20 ppm could be set;sodium concentrations below 20 ppm did not causebearing wear, while the presence of sodium above thislimiting value tended to cause white metal bearingwear, as indicated by the increase in lead levels. Inthis case, in the absence of reliable OEM, lubricantmanufacturer or lab analysis flagging limits, the enduser was able to determine an appropriate limit forpredictive purposes based on his own historical data.

Many oil analysis users rely on software to track andtrend their oil analysis data. The more sophisticatedsoftware programsavailable today allowusers to do bothgraphical trend analysisas shown in Figure 3 andbasic statistical analysisof their own data base todetermine alarms andlimits. These limits canthen be set as defaultvalues, so that datareturned from the lab is

Figure 3: Trend Analysis of sodium and lead forstarboard stern tube showing the effects of sea wateringression on white metal bearing wear (Ref: Fluid

Life Corp.)

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97/03/22 14 0 97/04/20 8 0 97/05/23 8 0 97/07/04 25 39 97/12/30 8 0 98/03/02 6 0 98/04/04 7 0 98/04/26 9 0 98/05/28 10 0 98/06/24 29 27 98/07/25 5 2 98/08/23 5 2 98/09/24 4 0 98/10/24 5 2 98/11/26 6 2

flagged according to the users own specifications. Inthe future, it may even be possible to allow user tocompare their data not only with their own samplehistories, but with "data warehouses" compiled by oilanalysis labs, which allow end users to compare theirdata with a much greater data set, thus improvingstatistical accuracy.

The Importance of Understanding OilAnalysis Test Methods

One of the keys to understanding oil analysis flagginglimits and applying them appropriately is tounderstand the tests to which these limits apply. Takefor example the case illustrated in Figure 4. In thissituation, oil samples were taken on a monthly basisfrom a natural gas engine. Based on the labs statisticalanalysis, in conjunction with OEM data, the ironcaution limit was set at 25 ppm and critical limit at 95 ppm.A sample taken in April 97 was found toexceed the critical limit, however, based on pressurefrom production it was decided to continue inoperation and re-sample the following month. Thesample taken the following month in May 97 wasfound to have dropped below the cautionary ironlimit.

Figure 4: Iron and sediment trend analysis for naturalgas engine showing the relationship between large and

small particle wear (Ref: Fluid Life Corp.)

To the uneducated observer, it may appear that theproblem has miraculously gone away. However, thesediment test data, which shows the total amount oflarge particle contamination (in mg/L) greater than

5 microns reveals a very different story. By not actingwhen the iron level exceeded the caution point, theproblem has been allowed to progress such that muchlarger particles are being generated. Since spectro-analysis using an ICP instrument is limited to sub 5 micron particles, the iron count by ICP drops, whilethe number of large particles caused by a moreserious wear condition is increasing. Again if youplan on setting your own flagging limits, it isimportant to understand the test method used towhich the limit applies and most importantly itslimitations.

Predictive vs. Proactive Limits - SettingGoals

Whether you rely on your lab, equipment OEM orlube supplier to provide limits or even if you arecurrently setting your own limits, most oil analysisprograms tend to focus on predictive data patternssuch as "is the iron wear rate normal" or "is theviscosity of my oil still in grade". However, themaximum benefit can be achieved through oilanalysis when a proactive approach adopted. Thisapproach requires setting limits which reflect a certainmaintenance goal, such as "to extend the meantime

between engine overhauls"or "reduced the number ofservo-valve failures eachyear". Since most lubricationrelated problems areassociated with contami-nation, goal based limits willusually apply to tracking andtrending contamination. Forthe purposes of discussion,

we will focus on the two most common contaminants,namely particulate contamination and water.

Particulate Contamination

Industry studies have revealed that between 75-85%of all failures are caused by particulate contamination.For this reason, many oil analysis users are starting toperform routine particle counting, either onsite orthrough their commercial oil analysis lab. Studieshave also revealed that the degree of particle contam-ination can be correlated directly with the lifeexpectancy of a component. Table 1 shows data froma study conducted by the British HydrodynamicResearch Association. In this study, 117 hydraulicsystems from different types of equipment werecompared and the average number of hours betweenmechanical breakdown correlated with fluidcleanliness.

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date iron/ppm sediment mg/L

97/01/01 3 20 97/02/03 2 30 97/03/02 20 20 97/04/01 164 120 97/05/02 52 340 97/06/04 40 20

Table 1: British Hydrodynamic Research Association(BHRA) study of fluid cleanliness on average

breakdown hours for 117 hydraulic systems (Ref:BHRA/Noria Corp.)

Based on this study, it stands to reason that if yourcurrent machine cleanliness is 18/15, improving thecleanliness by even one ISO range code can have asignificant impact on equipment life and reliability.Table 2 provides a means of assessing the theoreticallife extension possible by improved fluid cleanlinessand allows specific targets or goal based limits to beset. For example, as a maintenance engineer, yourmandate may be to improve the reliability and lifeexpectancy of a particular piece of hydraulicequipment by a factor of two. If your current averageISO cleanliness rating is 18/15, based on Table 2, yourgoal based limit for improved cleanliness should beset at 15/12. Of course setting the limit and doingparticle counting doesn't change the cleanliness ofyour system, to achieve this improvement a lifestylechange such as an upgrade of air or oil filtration maybe required, but the goal based limit provides a targetby which the success of this lifestyle change can bemeasured.

Table 2: Life Extension table for improved fluidcleanliness (Ref: Noria Corp.)

When setting goal based limits, it is important to berealistic. For example, according to Table 2, byimproving fluid cleanliness in the above examplefrom 18/15 to 11/8, the life expectancy of thishydraulic system should go up be a factor of 7.However, the question which must be asked is “howrealistic is it that I can keep my oil as clean as 11/8”.As with all aspects of oil analysis, theory should betempered with real life common sense.

Goal based limits should be considered “movingtargets”. Lets assume using the above example, thatby upgrading the breathers on this hydraulic unit, thefluid cleanliness improves from 18/15 to 15/12,resulting in the expected two fold increase inreliability. At this point, it may be possible, throughupgraded oil filtration to improve the oil cleanlinessto 14/11. From Table 2, this would results in a 50%increase in reliability. If the added expense ofimproving oil filtration to achieve another 50%increase in reliability makes economic sense, why notmove the target cleanliness code lower? Goal basedimprovements should be considered a iterativeprocess; continual changes and improvements should

be made with lower and lowerlimits set until no further financialgains can be achieved.

Water contamination

In many industries, watercontamination is the number onesource of component failure. Inrolling element bearings inparticular, water contaminationcan have a serious impact oncomponent life and equipmentreliability. Figure 5 shows datafrom a study by Timken BearingCo. illustrating the effects ofwater in oil on rated bearing life.As can be seen from Figure 5,keeping water contamination at100 ppm or lower can result in asignificant increase in componentlife. While this may not always bepractical, the general rule ofthumb in setting limits for watercontamination is to keep it as low

as reasonably possible, but certainly below thesaturation point of the lubricant in use to prevent freewater from being present.

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ISO code 24/21 23/20 22/19 21/18 20/17 19/16 18/15 17/14 16/13 15/12 14/11 13/10 12/9 average hours between breakdown

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Figure 5: The effects of water in oil on bearing life(Ref: Timken Bearing Co.)

Just like particulate contamination, goal based limitsfor water contamination can be set as a target forimproved equipment reliability. Table 3 illustrates theeffects of reduced water contamination on componentlife. Again, by setting a goal based limit using Table 3and making a lifestyle change such as improving sealsor deploying vacuum dehydration a proactiveapproach to equipment reliability can be made.

Table 3: Life Extension table for water contamination

(Ref: SKF/OSU/Noria Corp.)

ConclusionsOil analysis limits provide us a way of measuring thesuccess of our oil analysis program. By getting awayfrom the premise that “the equipment OEM orlubricant supplier knows best” and using our ownknowledge and historical data to determine our ownlimits, maximum benefit can be gained by using oilanalysis as a predictive tool for condition monitoring.Going one stage further and setting limits and targetssuch as particle contamination or water concentrationbased on specific maintenance goals can provide theend user with a proactive means of significantlyimproving equipment reliability and productionuptime.

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