# ASTM D7720-11 (Reapproved 2017)

Designation: D7720 − 11 (Reapproved 2017)Standard 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 oforiginal 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 statisticallyevaluate 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. 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 may 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 achosen 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 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 set is either parametric ornonparametric, and it may be used if a frequency distributionappears skewed or has only a single tail. Also, this techniquemay be used when the data set includes special cause variationin addition to common cause variation, although the techniqueshould be repeated when 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.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 sample 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 techniques, 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,” absolutealarming, multi-parameter alarming, and empirically derivedalarm limits.1.7 The techniques 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 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 health practices and determine the applica-bility of regulatory limitations prior to use.1.9 This international standard was developed in accor-dance with internationally recognized principles on standard-ization established in the Decision on Principles for theDevelopment of International Standards, Guides and Recom-mendations issued by the World Trade Organization TechnicalBarriers to Trade (TBT) Committee.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 May 1, 2017. Published July 2017. Originally approvedin 2011. Last previous edition approved in 2011 as D7720 – 11.DOI:10.1520 ⁄D7720-11R17.Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United StatesThis international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for theDevelopment of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.12. Referenced Documents2.1 ASTM Standards:2D445 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 TitrationD974 Test Method for Acid and Base Number by Color-Indicator TitrationD2896 Test Method for Base Number of Petroleum Productsby Potentiometric Perchloric Acid TitrationD4378 Practice for In-Service Monitoring of Mineral Tur-bine Oils for Steam, Gas, and Combined Cycle TurbinesD4928 Test Method for Water in Crude Oils by CoulometricKarl Fischer TitrationD5185 Test Method for Multielement Determination ofUsed and Unused Lubricating Oils and Base Oils byInductively Coupled Plasma Atomic Emission Spectrom-etry (ICP-AES)D6224 Practice for In-Service Monitoring of Lubricating Oilfor Auxiliary Power Plant EquipmentD6299 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 Cou-lometric Karl Fischer TitrationD6439 Guide for Cleaning, 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 Count 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 ViscometerD7414 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 for Analysis of In-Service Lubricants Usinga Particular Five-Part (Dielectric Permittivity, Time-Resolved Dielectric Permittivity with Switching MagneticFields, Laser Particle Counter, Microscopic DebrisAnalysis, and Orbital Viscometer) Integrated TesterD7483 Test Method for Determination of Dynamic Viscosityand Derived Kinematic Viscosity of Liquids by Oscillat-ing Piston ViscometerD7484 Test Method 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 DirectImaging Integrated TesterD7647 Test Method for Automatic Particle Counting ofLubricating 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 Characterization of Particlesfrom In-Service LubricantsD7685 Practice for In-Line, Full Flow, Inductive Sensor forFerromagnetic and Non-ferromagnetic Wear Debris De-termination and Diagnostics for Aero-Derivative and Air-craft Gas Turbine Engine BearingsD7690 Practice for Microscopic Characterization of Par-ticles from In-Service Lubricants by Analytical Ferrogra-phyE2412 Practice for Condition Monitoring of In-Service Lu-bricants by Trend Analysis Using Fourier TransformInfrared (FT-IR) Spectrometry3. Terminology3.1 Definitions:3.1.1 alarm, n—means of alerting the operator that a par-ticular condition exists.3.1.2 assignable cause, n—factor 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, n—condition in which the fric-tion and wear between two surfaces in relative motion aredetermined by the properties of the surfaces and the propertiesof the contacting fluid, other than bulk viscosity.3.1.3.1 Discussion—Metal to metal contact occurs and thechemistry of the system is involved. Physically adsorbed orchemically reacted soft films (usually very thin) supportcontact loads. Consequently, some wear is inevitable.3.1.4 chance cause, n—source of inherent random variationin a process which is predictable within statistical limits; alsocalled common cause.3.1.5 characteristic, n—property of items in a sample orpopulation which, when measured, counted or otherwiseobserved, helps to distinguish between the items.3.1.6 data set, n—logical collection of data that supports auser function and could include one or more data tables, files,or sources.3.1.6.1 Discussion—Herein a data set is a population ofvalues for a measurand from within a particular measurand setand covering an equipment population.3.1.7 distribution, n— as used in statistics, a set of all thevarious values that individual observations may have and thefrequency of their occurrence in the sample or population.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at service@astm.org. For Annual Book of ASTMStandards volume information, refer to the standard’s Document Summary page onthe ASTM website.D7720 − 11 (2017)23.1.8 measurand, n—particular quantity subject to measure-ment.3.1.8.1 Discussion—In industrial maintenance a measurandis sometimes called an analysis parameter.3.1.8.2 Discussion—Each measurand has a unit of measureand has a designation related to its characteristic measurement.3.1.9 nonparametric, n—term referring to a statistical tech-nique in which the probability distribution of the constituent inthe population is unknown or is not restricted to be of aspecified form.3.1.10 normal distribution, n—frequency distribution char-acterized by a bell shaped curve and defined by two param-eters: mean and standard deviation.3.1.11 outlying observation, n—observation that appears todeviate markedly in value from other members of the sampleset in which it appears, also called outlier.3.1.12 parametric, n—term referring to a statistical tech-nique that assumes the nature of the underlying frequencydistribution is known.3.1.13 population, n—well defined set (either finite or infi-nite) of elements.Statistical Process Control Technique Terms3.1.14 statistical process control (SPC), n—set 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.3.1.15 state of statistical control, n—process conditionwhen only common causes are operating on the process.3.1.16 center line, n—line on a control chart depicting theaverage level of the statistic being monitored.3.1.17 control limits, n—limits on a control chart that areused as criteria for signaling 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 Discussion—For 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 process is actually in control andthe statistic has a normal distribution.3.1.18 warning limits, n—limits on a control chart that aretwo standard errors below and above the center line.3.1.19 upper control limit, n—maximum value of the con-trol chart statistic that indicates statistical control.3.1.20 lower control limit, n—minimum value of the controlchart statistic that indicates statistical control.Cumulative Distribution Technique Terms3.1.21 cumulative distribution, n—representation of the to-tal fraction of the population, expressed as either mass-,volume-, area-, or number-based, that is greater than or lessthan discrete size values.3.2 Definitions of Terms Specific to This Standard:3.2.1 alarm limit, n—alarm condition values that delineateone alarm level from another within a measurand set; alsocalled alarm threshold.3.2.1.1 Discussion—When several alarm levels aredesignated, then a first alarm limit separates the normal levelfrom the alert level, and a second alarm limit separates the alertlevel from 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 Discussion—An 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 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 Discussion—Statistical process control is used toevaluate alarm limits comparing a control limit value with analarm limit value. Statistical cumulative distribution 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 limit set, n—collection 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, n—category for important produc-tion assets that are not redundant or high value or highlysensitivity or otherwise essential, also called critical assets orcritical machines.3.2.4 equipment population, n—well defined set of likeequipment operating under si