ASTM D7720-2011 9375 Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination《用油分析法监测设备.pdf

《ASTM D7720-2011 9375 Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination《用油分析法监测设备.pdf》由会员分享，可在线阅读，更多相关《ASTM D7720-2011 9375 Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination《用油分析法监测设备.pdf（14页珍藏版）》请在麦多课文档分享上搜索。

1、Designation: D7720 11Standard Guide forStatistically Evaluating Measurand Alarm Limits when UsingOil Analysis to Monitor Equipment and Oil for Fitness andContamination1This standard is issued under the fixed designation D7720; the number immediately following the designation indicates the year ofori

2、ginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This guide provides specific requirements to statisticallyeval

3、uate measurand alarm thresholds, which are called alarmlimits, as they are applied to data collected from in-service oilanalysis. These alarm limits are typically used for conditionmonitoring to produce severity indications relating to states ofmachinery wear, oil quality, and system contamination.

4、Alarmlimits distinguish or separate various levels of alarm. Fourlevels are common and will be used in this guide, though threelevels or five levels can also be used.1.2 A basic statistical process control technique describedherein is recommended to evaluate alarm limits when mea-surand data sets ma

5、y be characterized as both parametric and incontrol. A frequency distribution for this kind of parametricdata set fits a well-behaved two-tail normal distribution havinga “bell” curve appearance. Statistical control limits are calcu-lated using this technique. These control limits distinguish, at ac

6、hosen level of confidence, signal-to-noise ratio for an in-control data set from variation that has significant, assignablecauses. The operator can use them to objectively create,evaluate, and adjust alarm limits.1.3 A statistical cumulative distribution technique describedherein is also recommended

7、 to create, evaluate, and adjustalarm limits. This particular technique employs a percentcumulative distribution of sorted data set values. The techniqueis based on an actual data set distribution and therefore is notdependent on a presumed statistical profile. The technique maybe used when the data

8、 set is either parametric or nonparamet-ric, and it may be used if a frequency distribution appearsskewed or has only a single tail. Also, this technique may beused when the data set includes special cause variation inaddition to common cause variation, although the techniqueshould be repeated when

9、a special cause changes significantlyor is eliminated. Outputs of this technique are specific mea-surand values corresponding to selected percentage levels in acumulative distribution plot of the sorted data set. Thesepercent-based measurand values are used to create, evaluateand adjust alarm limits

10、.1.4 This guide may be applied to sample data from testingof in-service lubricating oil samples collected from machinery(for example, diesel, pumps, gas turbines, industrial turbines,hydraulics) whether from large fleets or individual industrialapplications.1.5 This guide may also be applied to samp

11、le data fromtesting in-service oil samples collected from other equipmentapplications where monitoring for wear, oil condition, orsystem contamination are important. For example, it may beapplied to data sets from oil filled transformer and circuitbreaker applications.1.6 Alarm limit evaluating tech

12、niques, which are not statis-tically based are not covered by this guide.Also, the techniquesof this standard may be inconsistent with the following alarmlimit selection techniques: “rate-of-change,” absolute alarm-ing, multi-parameter alarming, and empirically derived alarmlimits.1.7 The techniques

13、 in this guide deliver outputs that may becompared with other alarm limit selection techniques. Thetechniques in this guide do not preclude or supersede limits thathave been established and validated by an Original EquipmentManufacturer (OEM) or another responsible party.1.8 This standard does not p

14、urport 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 health practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:2

15、D445 Test Method for Kinematic Viscosity of Transparentand Opaque Liquids (and Calculation of Dynamic Viscos-ity)D664 Test Method for Acid Number of Petroleum Productsby Potentiometric Titration1This guide is under the jurisdiction of ASTM Committee D02 on PetroleumProducts and Lubricants and is the

16、 direct responsibility of Subcommittee D02.96.04on Guidelines for In-Services Lubricants Analysis.Current edition approved June 1, 2011. Published September 2011.DOI:10.1520/D772011.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.o

17、rg. For Annual Book of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.D974 Test Method for Acid and Base Number by Color-Indicator Tit

18、rationD2896 Test Method for Base Number of Petroleum Prod-ucts by Potentiometric Perchloric Acid TitrationD4378 Practice for In-Service Monitoring of Mineral Tur-bine Oils for Steam and Gas TurbinesD4928 Test Method for Water in Crude Oils by CoulometricKarl Fischer TitrationD5185 Test Method for De

19、termination of Additive Ele-ments, Wear Metals, and Contaminants in Used Lubricat-ing Oils and Determination of Selected Elements in BaseOils by Inductively Coupled Plasma Atomic EmissionSpectrometry (ICP-AES)D6224 Practice for In-Service Monitoring of LubricatingOil for Auxiliary Power Plant Equipm

20、entD6299 Practice for Applying Statistical Quality Assuranceand Control Charting Techniques to Evaluate AnalyticalMeasurement System PerformanceD6304 Test Method for Determination of Water in Petro-leum Products, Lubricating Oils, and Additives by Coulo-metric Karl Fischer TitrationD6439 Guide for C

21、leaning, Flushing, and Purification ofSteam, Gas, and Hydroelectric Turbine Lubrication Sys-temsD6595 Test Method for Determination of Wear Metals andContaminants in Used Lubricating Oils or Used HydraulicFluids by Rotating Disc Electrode Atomic Emission Spec-trometryD6786 Test Method for Particle C

22、ount in Mineral InsulatingOil Using Automatic Optical Particle CountersD7042 Test Method for Dynamic Viscosity and Density ofLiquids by Stabinger Viscometer (and the Calculation ofKinematic Viscosity)D7279 Test Method for Kinematic Viscosity of Transparentand Opaque Liquids by Automated Houillon Vis

23、cometerD7414 Test Method for Condition Monitoring of Oxidationin In-Service Petroleum and Hydrocarbon Based Lubri-cants by Trend Analysis Using Fourier Transform Infrared(FT-IR) SpectrometryD7416 Practice forAnalysis of In-Service Lubricants Usinga Particular Five-Part (Dielectric Permittivity, Time

24、-Resolved Dielectric Permittivity with Switching MagneticFields, Laser Particle Counter, Microscopic Debris Analy-sis, and Orbital Viscometer) IntegraD7483 Test Method for Determination of Dynamic Viscos-ity and Derived Kinematic Viscosity of Liquids by Oscil-lating Piston ViscometerD7484 Test Metho

25、d for Evaluation of Automotive EngineOils for Valve-Train Wear Performance in Cummins ISBMedium-Duty Diesel EngineD7596 Test Method for Automatic Particle Counting andParticle Shape Classification of Oils Using a Direct Imag-ing Integrated TesterD7647 Test Method for Automatic Particle Counting ofLu

26、bricating and Hydraulic Fluids Using Dilution Tech-niques to Eliminate the Contribution of Water and Inter-fering Soft Particles by Light ExtinctionD7670 Practice for Processing In-service Fluid Samples forParticulate Contamination Analysis Using Membrane Fil-tersD7684 Guide for Microscopic Characte

27、rization of Particlesfrom In-Service LubricantsD7685 Practice for In-Line, Full Flow, Inductive Sensor forFerromagnetic and Non-ferromagnetic Wear Debris Deter-mination and Diagnostics for Aero-Derivative and AircraftGas Turbine Engine BearingsD7690 Practice for Microscopic Characterization of Par-t

28、icles from In-Service Lubricants by Analytical Ferrogra-phyE2412 Practice for Condition Monitoring of In-ServiceLubricants by Trend Analysis Using Fourier TransformInfrared (FT-IR) Spectrometry3. Terminology3.1 Definitions:3.1.1 alarm, nmeans of alerting the operator that a par-ticular condition exi

29、sts.3.1.2 assignable cause, nfactor that contributes to varia-tion in a process or product output that is feasible to detect andidentify; also called special cause.3.1.3 boundary lubrication, ncondition in which the fric-tion and wear between two surfaces in relative motion aredetermined by the prop

30、erties of the surfaces and the propertiesof the contacting fluid, other than bulk viscosity.3.1.3.1 DiscussionMetal to metal contact occurs and thechemistry of the system is involved. Physically adsorbed orchemically reacted soft films (usually very thin) supportcontact loads. Consequently, some wea

31、r is inevitable.3.1.4 chance cause, nsource of inherent random variationin a process which is predictable within statistical limits; alsocalled common cause.3.1.5 characteristic, nproperty of items in a sample orpopulation which, when measured, counted or otherwise ob-served, helps to distinguish be

32、tween the items.3.1.6 data set, nlogical collection of data that supports auser function and could include one or more data tables, files,or sources.3.1.6.1 DiscussionHerein a data set is a population ofvalues for a measurand from within a particular measurand setand covering an equipment population

33、.3.1.7 distribution, nas used in statistics, a set of all thevarious values that individual observations may have and thefrequency of their occurrence in the sample or population.3.1.8 measurand, nparticular quantity subject to measure-ment.3.1.8.1 DiscussionIn industrial maintenance a measurandis s

34、ometimes called an analysis parameter.3.1.8.2 DiscussionEach measurand has a unit of measureand has a designation related to its characteristic measurement.3.1.9 nonparametric, nterm referring to a statistical tech-nique in which the probability distribution of the constituent inthe population is un

35、known or is not restricted to be of aspecified form.3.1.10 normal distribution, nfrequency distribution char-acterized by a bell shaped curve and defined by two param-eters: mean and standard deviation.D7720 1123.1.11 outlying observation, nobservation that appears todeviate markedly in value from o

36、ther members of the sampleset in which it appears, also called outlier.3.1.12 parametric, nterm referring to a statistical tech-nique that assumes the nature of the underlying frequencydistribution is known.3.1.13 population, nwell defined set (either finite orinfinite) of elements.Statistical Proce

37、ss Control Technique Terms3.1.14 statistical process control (SPC), nset of tech-niques for improving the quality of process output by reducingvariability through the use of one or more control charts and acorrective action strategy used to bring the process back into astate of statistical control.3

38、.1.15 state of statistical control, nprocess conditionwhen only common causes are operating on the process.3.1.16 center line, nline on a control chart depicting theaverage level of the statistic being monitored.3.1.17 control limits, nlimits on a control chart that areused as criteria for signaling

39、 the need for action or judgingwhether a set of data does or does not indicate a state ofstatistical control based on a prescribed degree of risk.3.1.17.1 DiscussionFor example, typical three-sigma lim-its carry a risk of 0.135 % of being out of control (on one sideof the center line) when the proce

40、ss is actually in control andthe statistic has a normal distribution.3.1.18 warning limits, nlimits on a control chart that aretwo standard errors below and above the center line.3.1.19 upper control limit, nmaximum value of the con-trol chart statistic that indicates statistical control.3.1.20 lowe

41、r control limit, nminimum value of the controlchart statistic that indicates statistical control.Cumulative Distribution Technique Terms3.1.21 cumulative distribution, nrepresentation of the to-tal fraction of the population, expressed as either mass-,volume-, area-, or number-based, that is greater

42、 than or lessthan discrete size values.3.2 Definitions of Terms Specific to This Standard:3.2.1 alarm limit, nalarm condition values that delineateone alarm level from another within a measurand set; alsocalled alarm threshold.3.2.1.1 DiscussionWhen several alarm levels are desig-nated, then a first

43、 alarm limit separates the normal level fromthe alert level, and a second alarm limit separates the alert levelfrom action level. In other words, measurand data valuesgreater than the first alarm limit and less-than-or-equal-to thesecond alarm limit are in the state of the second level alarm.3.2.1.2

44、 DiscussionAn alarm limit, “X”, may be single-sided such as “greater than X” or “less than X”; or it may bedouble-sided such as “greater than X and less than X”. Alarmlimit values may represent the same units and scale as thecorresponding measurand data set, or they may be representedas a proportion

45、 such as a percent. Alarm limit values may bezero-based, or they may be relative to a non-zero reference orother baseline value.3.2.1.3 DiscussionStatistical process control is used toevaluate alarm limits comparing a control limit value with analarm limit value. Statistical cumulative distribution

46、is used toevaluate alarm limits by identifying a cumulative percentvalues corresponding with each alarm limit value and compar-ing those results, for example, percentages of a data set in eachalarm level, with expected percentages of the data set typicallyassociated with each alarm level.3.2.2 alarm

47、 limit set, ncollection of all the alarm limits(alarm condition threshold values) that are needed for analarm-based analysis of measurands within a measurand set.3.2.3 critical equipment, ncategory for important produc-tion assets that are not redundant or high value or highlysensitivity or otherwis

48、e essential, also called critical assets orcritical machines.3.2.4 equipment population, nwell defined set of likeequipment operating under similar conditions, selected andgrouped for condition monitoring purposes; also called ma-chine population, asset population, and fleet.3.2.4.1 DiscussionLike e

49、quipment may refer to equip-ment of a particular type that may include make, model,lubricant in use, and lubrication system. Similar conditionsmay include environment, duty-cycle, loading conditions.D7720 1133.2.5 measurand set, nmeaningful assemblage of mea-surands collectively representing characteristic measurementsthat reveal modes and causes of failure within an equipmentpopulation.3.2.5.1 DiscussionIn industry, a measurand set is some-times called an analysis parameter set.3.2.6 noncritical equipment, ncategory for productionassets that