1、Designation: D7669 15Standard Guide forPractical Lubricant Condition Data Trend Analysis1This standard is issued under the fixed designation D7669; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A number
2、 in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.INTRODUCTIONThis standard provides specific guidelines for trend analysis, as they are applied to conditionmonitoring of machinery. The main purpose of t
3、rend analysis is to learn how rapidly the machine andfluid are deteriorating. A significant change in trend is indicative of a developing failure. Interventionin the early stages of deterioration is much more cost effective than failure of the machine.Maximum reliability of in-service machine compon
4、ents and fluids requires a program of conditionmonitoring to provide timely indications of performance and remaining usable life. To achieve thesegoals, a condition monitoring program should monitor the rate of progression of the failure byincluding sufficient tests to determine the rate of degradat
5、ion, increase of contaminants, and quantityand identity of metal debris from corrosion or wear.The condition monitoring process determines the presence of oil-related failure modes, allowingremedial maintenance to take place before failure and subsequently expensive equipment damageoccurs. In order
6、to diagnose and predict machinery and fluid condition, the rate of change of machinecondition must be trended. Equipment maintainers expect conditionmonitoring information to clearlyand consistently indicate machinery condition, that is, the rate-of-change of component damage overtime and the risk o
7、f failure.Trending utilizes a comparison of a condition parameter with time. For example, plots of acondition-related parameter as a function of time is used to determine when the parameter is likely toexceed a given limit. Forecasting the expected breakdown of a machine well in advance enables theo
8、perator to minimize the machines downtime1. Scope*1.1 This guide covers practical techniques for condition datatrend analysis.1.2 The techniques may be utilized for all instrumentationthat provides numerical test results. This guide is writtenspecifically for data obtained from lubricant samples. Ot
9、herdata obtained and associated with the machine may also beused in determining the machine condition.1.3 This standard does not purport to address all of thesafety concerns, if any, associated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and he
10、alth practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2D4057 Practice for Manual Sampling of Petroleum andPetroleum ProductsD4177 Practice for Automatic Sampling of Petroleum andPetroleum ProductsD7720 Guide for Statistically
11、 Evaluating Measurand AlarmLimits when Using Oil Analysis to Monitor Equipmentand Oil for Fitness and ContaminationD7874 Guide for Applying Failure Mode and Effect Analy-sis (FMEA) to In-Service Lubricant TestingE2587 Practice for Use of Control Charts in StatisticalProcess Control3. Terminology3.1
12、Definitions of Terms Specific to This Standard:1This guide is under the jurisdiction of ASTM Committee D02 on PetroleumProducts, Liquid Fuels, and Lubricants and is the direct responsibility of Subcom-mittee D02.96.04 on Guidelines for In-Services Lubricants Analysis.Current edition approved April 1
13、, 2015. Published May 2015. Originallyapproved in 2011. Last previous edition approved in 2011 as D7669 11.DOI:10.1520/D7669-15.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandards volume informatio
14、n, refer to the standards Document Summary page onthe ASTM website.*A Summary of Changes section appears at the end of this standardCopyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States13.1.1 alarm, na means of alerting the operator that ap
15、articular condition exists.3.1.2 alarm limit, nset-point threshold used to determinethe status of the magnitude or trend of parametric conditiondata.3.1.2.1 DiscussionIn OEM provided alarm limits indi-vidual measurements are interpreted singly. Most fluid andmachine failure modes do not give rise to
16、 symptoms identifi-able by a single measurement parameter. Early positive iden-tification of a fault generally requires the combination ofmultiple condition measurements into a unique fault signature.See Guide D7874.3.1.2.2 DiscussionEstablishing proper alarm limits can bea valuable asset for interp
17、retation of test results to reflect theequipments operation. The level and trend alarms can assistthe equipment maintainer with reliability control and improve-ment. With the trending approach established, the machineoperators next objective is to establish guidelines for limits orextremes to which
18、the results may progress to before requiringmaintenance actions to be taken. The calculation of alarmlimits should initially be developed based on the ideal condi-tions and limitations from a sample population of conditiondata, although in reality, ideal conditions are not often met.3.1.3 condition
19、indicator, na condition indicator is avariable that is statistically associated with an equipment orlubricant failure modes whose value can be established byinclusion of one or more measurements. Development of acondition indicator involves considerable analysis of equip-ment test, maintenance and f
20、ailure histories. Most conditionmonitoring and analysis systems are centered on the gathering,storage and display of raw test data and trends. Data interpre-tation generally involves the evaluation of limit exceedenceand trend plots.3.1.3.1 DiscussionA condition indicator should be unam-biguous in i
21、ts indication of a problem. The minimum require-ment is that a combination of condition measurements andequipment usage provides a reliable indication of a specificmachine or lubricant problem without ambiguity. A conditionindicator should be statistically well behaved. It should staywithin defined
22、bounds given by the variability of machine-to-machine performance and instrument reproducibility. It shouldalso be sufficiently sensitive to trigger an early alarm and itshould be monotonic in its variation. Reliable warning andalarm limits should be established and maintained.3.1.4 condition tests,
23、 nthe requirement for an effectivecondition monitoring program is utilizing tests that indicatefailure modes and in sufficient time to prevent them.3.1.4.1 DiscussionAlthough the concept of measuringparameters to determine running condition of a system seemssimple, a great many additional variables
24、must be considered toensure reliable condition prediction. These include, but are notlimited to, machine type, machine configuration, operationalconsiderations, oil type, oil quantity, consumption rate, main-tenance history, etc.3.1.5 dead oil sampling, noil sample taken that is notrepresentative of
25、 the circulating or system oil due to one ofseveral reasons, including the fluid in the system is static, thesample is taken from a non-flowing zone, and the sample pointor tube within the oil was not flushed to remove the stagnant oilin the tube.3.1.5.1 DiscussionWithout a proper oil sample, oil an
26、aly-sis techniques are not useful. The most fundamental issue forany oil analysis program is sample quality. Oil samples must betaken using the appropriate procedure for the machinery inquestion. The sample must be taken from the most effectivelocation on the machine, whether it is via an on-line se
27、nsor ora bottled sample.3.1.5.2 DiscussionMaintenance, operational events, andsampling location are major factors affecting sample represen-tation and, thus, the test results. Sampling without regard tolocation or maintenance and operational activities causes ahigh level of data variability. High da
28、ta variability results inpoor data interpretation and loss of program benefits.3.1.6 lubricant condition monitoring, nfield of technicalactivity in which selected physical parameters associated withan operating machine are periodically or continuously sensed,measured, and recorded for the interim pu
29、rpose of reducing,analyzing, comparing, and displaying the data and informationso obtained and for the ultimate purpose of using interimresults to support decisions related to the operation andmaintenance of the machine.3.1.7 machinery health, nqualitative expression of theoperational status of a ma
30、chine subcomponent, component, orentire machine, used to communicate maintenance and opera-tional recommendations or requirements in order to continueoperation, schedule maintenance, or take immediate mainte-nance action.3.1.8 optimum sample interval, noptimum (standard)sample interval is derived fr
31、om failure profile data. It is afraction of the time between initiation of a critical failure modeand equipment failure. In general, sample intervals should beshort enough to provide at least two samples prior to failure.The interval is established for the shortest critical failure mode.3.1.8.1 Disc
32、ussionSampling, maintenance, and oil addi-tions may not be performed at the precisely specified intervals.The irregular intervals common to most equipment operationshave a profound effect on measurement data. In particular, theconcentration of wear metals, contaminants and additives isaffected great
33、ly by oil additions and machine usage.Consequently, both the level and rate-of-change of theseparameters must be considered for proper condition assess-ment. It is critical to establish an optimum sample interval. Theoptimum sample interval for a machine can be defined as aninterval short enough to
34、provide at least two samples during theperiod between the start of an abnormal condition and theinitiation of a critical failure mode. In practice, an engineershould determine or at least verify all sample intervals byanalyses of the equipment and historical data.3.1.9 prognostics, nforecast of the
35、condition or remainingusable life of a machine, fluid, or component part.3.1.10 remaining usable life, nsubjective estimate basedupon observations or average estimates of similar items,components, or systems, or a combination thereof, of thenumber of remaining time that an item, component, or system
36、D7669 152is estimated to be able to function in accordance with itsintended purpose before replacement.3.1.11 sample population, ngroup of samples organizedfor statistical analysis.3.1.12 statistical analysis, na structured trending andevaluation procedure in which statistics relate individual testr
37、esults to specific equipment failure mode and statistics is usedto define the interpretation criteria and alarm limits.3.1.13 statistical process control (SPC), nset of tech-niques for improving the quality of process output by reducingvariability through the use of one or more mechanisms, controlch
38、arts, for example. A corrective action strategy is used tobring the process back into a state of statistical control(Practice E2587).3.1.14 trend analysis, nmonitoring of the level and rate ofchange over operating time of measured parameters.3.2 Symbols:Avg = averageC = current sampleH = usage metri
39、c (for example, hours)OI = time on-oil intervalP = previous samplePP = predicted prior sampleSSI = standard sample intervalT = trend4. Summary of Guide4.1 This guide provides practical methods for the trendanalysis of condition data in the dynamic machinery operatingenvironment. Various trending tec
40、hniques and formulae arepresented with their associated benefits and limitations.5. Significance and Use5.1 This guide is intended to provide machinery mainte-nance and monitoring personnel with a guideline for perform-ing trend analysis to aid in the interpretation of machinerycondition data.6. Int
41、erferences6.1 Sampling, maintenance, filter, and oil changes are rarelyperformed at precise intervals. These irregular, opportunisticintervals have a profound effect on measurement data andinterfere with trending techniques.6.2 Machinery OperationOperational intensity can impacthow quickly a compone
42、nt wears and how rapidly a faultprogresses (1).3A relevant indicator of machine usage must beincluded in any calculations. The selected usage indicator mustreflect actual machine usage, that is, life consumed (forexample, stop/start cycles, megawatt hours, hours of use, orfuel consumption).6.3 Maint
43、enance EventsComponent, filter, and oilchanges impact the monitoring of machine performance, weardebris, contamination ingress and fluid condition. Maintenanceevents should always occur after a sample is taken (orcondition test is performed). All maintenance events should bedocumented and taken into
44、 account during condition datainterpretation. In all cases, maintenance events, if not reported,will reduce trending reliability.6.4 Sampling ProceduresImproper or poor sampling tech-niques can profoundly impact condition test data (see PracticesD4057 and D4177). Taking a good oil sample is a critic
45、al partof data trending. The following should be considered for aproper sampling procedure:6.4.1 Sample Quality:6.4.1.1 The most fundamental issue for any oil analysisprogram is sample representativeness. While poor analyticalpractices or insufficient data integrity checks generate data thatcannot b
46、e reliably interpreted, improper sampling practicesgenerate inaccurate data which is often meaningless withrespect to condition monitoring or fault diagnosis.6.4.1.2 Sample bottles can have a considerable influence ontest results, particularly on oil cleanliness results. In practice,only sample bott
47、les qualified for cleanliness should be used.When samples are to be taken from ultra clean machinery suchas industrial hydraulic systems, the sample bottle must be ratedas ultra clean. Exposing the new bottle or cap to the atmo-sphere negates any cleanliness certification.6.4.1.3 The primary objecti
48、ve of the oil sampling process isto acquire a representative sample, for example, one whoseproperties, contaminants, and wear metals accurately reflect thecondition of oil and machine. Theoretically, a representativesample means the concentration and size distribution of par-ticulates and chemical s
49、pecies in the sample bottle correlatewith those in the oil reservoir. Data variability may result fromsampling procedures, sampling locations, improper mainte-nance activities, operational events (for example, exposure tohigh stress or temperature variation), analytical testing, dataentry, and presence of one or more conflicting failure modes.6.4.2 A significant difference in the test data could trigger afalse trend alarm. Examples of poor sampling techniques are:6.4.2.1 Stagnant sampling,6.4.2.2 Sampling after component change out,6.4.2.3 Sampling after o
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