ASTM D7720-11(2017)

Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination

SIGNIFICANCE AND USE
5.1 Alarm limits are used extensively for condition monitoring using data from in-service lubricant sample test results. There are many bases for initially choosing values for these alarm limits. There are many questions that should be addressed. These include:  
Are those limits right or wrong?
Are there too many false positive or false negative results?
Are they practical?  
5.2 This guide teaches statistical techniques for evaluating whether alarm limits are meaningful and if they are reasonable for flagging problems requiring immediate or future action.  
5.3 This guide is intended to increase the consistency, usefulness, and dependability of condition based action recommendations by providing machinery maintenance and monitoring personnel with a meaningful and practical way to evaluate alarm limits to aid the interpretation of monitoring machinery and oil condition as well as lubricant system contamination data.
SCOPE
1.1 This guide provides specific requirements to statistically evaluate measurand alarm thresholds, which are called alarm limits, as they are applied to data collected from in-service oil analysis. These alarm limits are typically used for condition monitoring to produce severity indications relating to states of machinery wear, oil quality, and system contamination. Alarm limits distinguish or separate various levels of alarm. Four levels are common and will be used in this guide, though three levels or five levels can also be used.  
1.2 A basic statistical process control technique described herein is recommended to evaluate alarm limits when measurand data sets may be characterized as both parametric and in control. A frequency distribution for this kind of parametric data set fits a well-behaved two-tail normal distribution having a “bell” curve appearance. Statistical control limits are calculated using this technique. These control limits distinguish, at a chosen level of confidence, signal-to-noise ratio for an in-control data set from variation that has significant, assignable causes. The operator can use them to objectively create, evaluate, and adjust alarm limits.  
1.3 A statistical cumulative distribution technique described herein is also recommended to create, evaluate, and adjust alarm limits. This particular technique employs a percent cumulative distribution of sorted data set values. The technique is based on an actual data set distribution and therefore is not dependent on a presumed statistical profile. The technique may be used when the data set is either parametric or nonparametric, and it may be used if a frequency distribution appears skewed or has only a single tail. Also, this technique may be used when the data set includes special cause variation in addition to common cause variation, although the technique should be repeated when a special cause changes significantly or is eliminated. Outputs of this technique are specific measurand values corresponding to selected percentage levels in a cumulative distribution plot of the sorted data set. These percent-based measurand values are used to create, evaluate and adjust alarm limits.  
1.4 This guide may be applied to sample data from testing of in-service lubricating oil samples collected from machinery (for example, diesel, pumps, gas turbines, industrial turbines, hydraulics) whether from large fleets or individual industrial applications.  
1.5 This guide may also be applied to sample data from testing in-service oil samples collected from other equipment applications where monitoring for wear, oil condition, or system contamination are important. For example, it may be applied to data sets from oil filled transformer and circuit breaker applications.  
1.6 Alarm limit evaluating techniques, which are not statistically based are not covered by this guide. Also, the techniques of this standard may be inconsistent with the following alarm limit selection techniques: “rate-of-change,...

General Information

• Status: Historical
• Publication Date: 30-Apr-2017
• ICS: 25.040.40 - Industrial process measurement and control
• Technical Committee: D02 - Petroleum Products, Liquid Fuels, and Lubricants
• Drafting Committee: D02.96.04 - Guidelines for In-Services Lubricants Analysis

Standards Content (Sample)

NOTICE: This standard has either been superseded and replaced by a new version or withdrawn.
Contact ASTM International (www.astm.org) for the latest information
Designation: D7720 − 11 (Reapproved 2017)
Standard Guide for
Statistically Evaluating Measurand Alarm Limits when Using
Oil Analysis to Monitor Equipment and Oil for Fitness and
Contamination
This standard is issued under the fixed designation D7720; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope surand values corresponding to selected percentage levels in a
cumulative distribution plot of the sorted data set. These
1.1 This guide provides specific requirements to statistically
percent-based measurand values are used to create, evaluate
evaluate measurand alarm thresholds, which are called alarm
and adjust alarm limits.
limits, as they are applied to data collected from in-service oil
analysis. These alarm limits are typically used for condition
1.4 This guide may be applied to sample data from testing
monitoring to produce severity indications relating to states of
of in-service lubricating oil samples collected from machinery
machinery wear, oil quality, and system contamination. Alarm
(for example, diesel, pumps, gas turbines, industrial turbines,
limits distinguish or separate various levels of alarm. Four
hydraulics) whether from large fleets or individual industrial
levels are common and will be used in this guide, though three
applications.
levels or five levels can also be used.
1.5 This guide may also be applied to sample data from
1.2 A basic statistical process control technique described
testing in-service oil samples collected from other equipment
herein is recommended to evaluate alarm limits when mea-
applications where monitoring for wear, oil condition, or
suranddatasetsmaybecharacterizedasbothparametricandin
control. A frequency distribution for this kind of parametric system contamination are important. For example, it may be
data set fits a well-behaved two-tail normal distribution having applied to data sets from oil filled transformer and circuit
a “bell” curve appearance. Statistical control limits are calcu-
breaker applications.
lated using this technique.These control limits distinguish, at a
1.6 Alarm limit evaluating techniques, which are not statis-
chosen level of confidence, signal-to-noise ratio for an in-
ticallybasedarenotcoveredbythisguide.Also,thetechniques
control data set from variation that has significant, assignable
of this standard may be inconsistent with the following alarm
causes. The operator can use them to objectively create,
limit selection techniques: “rate-of-change,” absolute
evaluate, and adjust alarm limits.
alarming, multi-parameter alarming, and empirically derived
1.3 Astatistical cumulative distribution technique described
alarm limits.
herein is also recommended to create, evaluate, and adjust
alarm limits. This particular technique employs a percent
1.7 The techniques in this guide deliver outputs that may be
cumulativedistributionofsorteddatasetvalues.Thetechnique
compared with other alarm limit selection techniques. The
is based on an actual data set distribution and therefore is not
techniquesinthisguidedonotprecludeorsupersedelimitsthat
dependent on a presumed statistical profile.The technique may
have been established and validated by an Original Equipment
be used when the data set is either parametric or
Manufacturer (OEM) or another responsible party.
nonparametric, and it may be used if a frequency distribution
1.8 This standard does not purport to address all of the
appears skewed or has only a single tail. Also, this technique
safety concerns, if any, associated with its use. It is the
may be used when the data set includes special cause variation
responsibility of the user of this standard to establish appro-
in addition to common cause variation, although the technique
should be repeated when a special cause changes significantly priate safety and health practices and determine the applica-
or is eliminated. Outputs of this technique are specific mea- bility of regulatory limitations prior to use.
1.9 This international standard was developed in accor-
dance with internationally recognized principles on standard-
This guide is under the jurisdiction of ASTM Committee D02 on Petroleum
Products, Liquid Fuels, and Lubricants and is the direct responsibility of Subcom- ization established in the Decision on Principles for the
mittee D02.96.04 on Guidelines for In-Services Lubricants Analysis.
Development of International Standards, Guides and Recom-
CurrenteditionapprovedMay1,2017.PublishedJuly2017.Originallyapproved
mendations issued by the World Trade Organization Technical
in 2011. Last previous edition approved in 2011 as D7720 – 11.
DOI:10.1520 ⁄D7720-11R17. Barriers to Trade (TBT) Committee.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D7720 − 11 (2017)
2. Referenced Documents D7484 Test Method for Evaluation of Automotive Engine
2 Oils for Valve-Train Wear Performance in Cummins ISB
2.1 ASTM Standards:
Medium-Duty Diesel Engine
D445 Test Method for Kinematic Viscosity of Transparent
D7596 Test Method for Automatic Particle Counting and
and Opaque Liquids (and Calculation of Dynamic Viscos-
Particle Shape Classification of Oils Using a Direct
ity)
Imaging Integrated Tester
D664 Test Method for Acid Number of Petroleum Products
D7647 Test Method for Automatic Particle Counting of
by Potentiometric Titration
Lubricating and Hydraulic Fluids Using Dilution Tech-
D974 Test Method for Acid and Base Number by Color-
niques to Eliminate the Contribution of Water and Inter-
Indicator Titration
fering Soft Particles by Light Extinction
D2896 Test Method for Base Number of Petroleum Products
D7670 Practice for Processing In-service Fluid Samples for
by Potentiometric Perchloric Acid Titration
Particulate ContaminationAnalysis Using Membrane Fil-
D4378 Practice for In-Service Monitoring of Mineral Tur-
ters
bine Oils for Steam, Gas, and Combined Cycle Turbines
D7684 Guide for Microscopic Characterization of Particles
D4928 Test Method for Water in Crude Oils by Coulometric
from In-Service Lubricants
Karl Fischer Titration
D7685 Practice for In-Line, Full Flow, Inductive Sensor for
D5185 Test Method for Multielement Determination of
Ferromagnetic and Non-ferromagnetic Wear Debris De-
Used and Unused Lubricating Oils and Base Oils by
termination and Diagnostics for Aero-Derivative and Air-
Inductively Coupled Plasma Atomic Emission Spectrom-
craft Gas Turbine Engine Bearings
etry (ICP-AES)
D7690 Practice for Microscopic Characterization of Par-
D6224 PracticeforIn-ServiceMonitoringofLubricatingOil
ticles from In-Service Lubricants by Analytical Ferrogra-
for Auxiliary Power Plant Equipment
phy
D6299 Practice for Applying Statistical Quality Assurance
E2412 Practice for Condition Monitoring of In-Service Lu-
and Control Charting Techniques to Evaluate Analytical
bricants by Trend Analysis Using Fourier Transform
Measurement System Performance
Infrared (FT-IR) Spectrometry
D6304 Test Method for Determination of Water in Petro-
leum Products, Lubricating Oils, and Additives by Cou-
3. Terminology
lometric Karl Fischer Titration
D6439 Guide for Cleaning, Flushing, and Purification of
3.1 Definitions:
Steam, Gas, and Hydroelectric Turbine Lubrication Sys-
3.1.1 alarm, n—means of alerting the operator that a par-
tems
ticular condition exists.
D6595 Test Method for Determination of Wear Metals and
3.1.2 assignable cause, n—factor that contributes to varia-
Contaminants in Used Lubricating Oils or Used Hydraulic
tion in a process or product output that is feasible to detect and
Fluids by Rotating Disc ElectrodeAtomic Emission Spec-
identify; also called special cause.
trometry
3.1.3 boundary lubrication, n—condition in which the fric-
D6786 Test Method for Particle Count in Mineral Insulating
tion and wear between two surfaces in relative motion are
Oil Using Automatic Optical Particle Counters
determined by the properties of the surfaces and the properties
D7042 Test Method for Dynamic Viscosity and Density of
of the contacting fluid, other than bulk viscosity.
Liquids by Stabinger Viscometer (and the Calculation of
Kinematic Viscosity) 3.1.3.1 Discussion—Metal to metal contact occurs and the
chemistry of the system is involved. Physically adsorbed or
D7279 Test Method for Kinematic Viscosity of Transparent
and Opaque Liquids by Automated Houillon Viscometer chemically reacted soft films (usually very thin) support
contact loads. Consequently, some wear is inevitable.
D7414 Test Method for Condition Monitoring of Oxidation
in In-Service Petroleum and Hydrocarbon Based Lubri-
3.1.4 chance cause, n—source of inherent random variation
cants byTrendAnalysis Using FourierTransform Infrared
in a process which is predictable within statistical limits; also
(FT-IR) Spectrometry
called common cause.
D7416 Practice for Analysis of In-Service Lubricants Using
3.1.5 characteristic, n—property of items in a sample or
a Particular Five-Part (Dielectric Permittivity, Time-
population which, when measured, counted or otherwise
Resolved Dielectric Permittivity with Switching Magnetic
observed, helps to distinguish between the items.
Fields, Laser Particle Counter, Microscopic Debris
Analysis, and Orbital Viscometer) Integrated Tester 3.1.6 data set, n—logical collection of data that supports a
D7483 TestMethodforDeterminationofDynamicViscosity user function and could include one or more data tables, files,
and Derived Kinematic Viscosity of Liquids by Oscillat- or sources.
ing Piston Viscometer
3.1.6.1 Discussion—Herein a data set is a population of
values for a measurand from within a particular measurand set
and covering an equipment population.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or
3.1.7 distribution, n— as used in statistics, a set of all the
contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
various values that individual observations may have and the
Standards volume information, refer to the standard’s Document Summary page on
the ASTM website. frequency of their occurrence in the sample or population.
D7720 − 11 (2017)
3.1.8 measurand, n—particular quantity subject to measure- 3.2.1 alarm limit, n—alarm condition values that delineate
ment. one alarm level from another within a measurand set; also
called alarm threshold.
3.1.8.1 Discussion—In industrial maintenance a measurand
is sometimes called an analysis parameter. 3.2.1.1 Discussion—When several alarm levels are
designated, then a first alarm limit separates the normal level
3.1.8.2 Discussion—Each measurand has a unit of measure
fromthealertlevel,andasecondalarmlimitseparatesthealert
and has a designation related to its characteristic measurement.
level from action level. In other words, measurand data values
3.1.9 nonparametric, n—term referring to a statistical tech-
greater than the first alarm limit and less-than-or-equal-to the
nique in which the probability distribution of the constituent in
second alarm limit are in the state of the second level alarm.
the population is unknown or is not restricted to be of a
3.2.1.2 Discussion—An alarm limit, “X”, may be single-
specified form.
sided such as “greater than X” or “less than –X”; or it may be
3.1.10 normal distribution, n—frequency distribution char-
double-sided such as “greater than X and less than –X”.Alarm
acterized by a bell shaped curve and defined by two param-
limit values may represent the same units and scale as the
eters: mean and standard deviation.
corresponding measurand data set, or they may be represented
as a proportion such as a percent. Alarm limit values may be
3.1.11 outlying observation, n—observation that appears to
zero-based, or they may be relative to a non-zero reference or
deviate markedly in value from other members of the sample
other baseline value.
set in which it appears, also called outlier.
3.2.1.3 Discussion—Statistical process control is used to
3.1.12 parametric, n—term referring to a statistical tech-
evaluate alarm limits comparing a control limit value with an
nique that assumes the nature of the underlying frequency
alarm limit value. Statistical cumulative distribution is used to
distribution is known.
evaluate alarm limits by identifying a cumulative percent
3.1.13 population, n—well defined set (either finite or infi-
values corresponding with each alarm limit value and compar-
nite) of elements.
ing those results, for example, percentages of a data set in each
alarm level, with expected percentages of the data set typically
Statistical Process Control Technique Terms
associated with each alarm level.
3.1.14 statistical process control (SPC), n—set of tech-
3.2.2 alarm limit set, n—collection of all the alarm limits
niques for improving the quality of process output by reducing
(alarm condition threshold values) that are needed for an
variability through the use of one or more control charts and a
alarm-based analysis of measurands within a measurand set.
corrective action strategy used to bring the process back into a
state of statistical control. 3.2.3 critical equipment, n—category for important produc-
tion assets that are not redundant or high value or highly
3.1.15 state of statistical control, n—process condition
sensitivity or otherwise essential, also called critical assets or
when only common causes are operating on the process.
critical machines.
3.1.16 center line, n—line on a control chart depicting the
3.2.4 equipment population, n—well defined set of like
average level of the statistic being monitored.
equipment operating under similar conditions, selected and
3.1.17 control limits, n—limits on a control chart that are
grouped for condition monitoring purposes; also called ma-
used as criteria for signaling the need for action or judging
chine population, asset population, and fleet.
whether a set of data does or does not indicate a state of
3.2.4.1 Discussion—Like equipment may refer to equip-
statistical control based on a prescribed degree of risk.
ment of a particular type that may include make, model,
3.1.17.1 Discussion—For example, typical three-sigma lim-
lubricant in use, and lubrication system. Similar conditions
its carry a risk of 0.135 % of being out of control (on one side
may include environment, duty-cycle, loading conditions.
of the center line) when the process is actually in control and
3.2.5 measurand set, n—meaningful assemblage of mea-
the statistic has a normal distribution.
surands collectively representing characteristic measur
...

This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
Designation: D7720 − 11 D7720 − 11 (Reapproved 2017)
Standard Guide for
Statistically Evaluating Measurand Alarm Limits when Using
Oil Analysis to Monitor Equipment and Oil for Fitness and
Contamination
This standard is issued under the fixed designation D7720; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope
1.1 This guide provides specific requirements to statistically evaluate measurand alarm thresholds, which are called alarm
limits, as they are applied to data collected from in-service oil analysis. These alarm limits are typically used for condition
monitoring to produce severity indications relating to states of machinery wear, oil quality, and system contamination. Alarm limits
distinguish or separate various levels of alarm. Four levels are common and will be used in this guide, though three levels or five
levels can also be used.
1.2 A basic statistical process control technique described herein is recommended to evaluate alarm limits when measurand data
sets may be characterized as both parametric and in control. A frequency distribution for this kind of parametric data set fits a
well-behaved two-tail normal distribution having a “bell” curve appearance. Statistical control limits are calculated using this
technique. These control limits distinguish, at a chosen level of confidence, signal-to-noise ratio for an in-control data set from
variation that has significant, assignable causes. The operator can use them to objectively create, evaluate, and adjust alarm limits.
1.3 A statistical cumulative distribution technique described herein is also recommended to create, evaluate, and adjust alarm
limits. This particular technique employs a percent cumulative distribution of sorted data set values. The technique is based on an
actual data set distribution and therefore is not dependent on a presumed statistical profile. The technique may be used when the
data set is either parametric or nonparametric, and it may be used if a frequency distribution appears skewed or has only a single
tail. Also, this technique may be used when the data set includes special cause variation in addition to common cause variation,
although the technique should be repeated when a special cause changes significantly or is eliminated. Outputs of this technique
are specific measurand values corresponding to selected percentage levels in a cumulative distribution plot of the sorted data set.
These percent-based measurand values are used to create, evaluate and adjust alarm limits.
1.4 This guide may be applied to sample data from testing of in-service lubricating oil samples collected from machinery (for
example, diesel, pumps, gas turbines, industrial turbines, hydraulics) whether from large fleets or individual industrial applications.
1.5 This guide may also be applied to sample data from testing in-service oil samples collected from other equipment
applications where monitoring for wear, oil condition, or system contamination are important. For example, it may be applied to
data sets from oil filled transformer and circuit breaker applications.
1.6 Alarm limit evaluating techniques, which are not statistically based are not covered by this guide. Also, the techniques of
this standard may be inconsistent with the following alarm limit selection techniques: “rate-of-change,” absolute alarming,
multi-parameter alarming, and empirically derived alarm limits.
1.7 The techniques in this guide deliver outputs that may be compared with other alarm limit selection techniques. The
techniques in this guide do not preclude or supersede limits that have been established and validated by an Original Equipment
Manufacturer (OEM) or another responsible party.
1.8 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory
limitations prior to use.
This guide is under the jurisdiction of ASTM Committee D02 on Petroleum Products, Liquid Fuels, and Lubricants and is the direct responsibility of Subcommittee
D02.96.04 on Guidelines for In-Services Lubricants Analysis.
Current edition approved June 1, 2011May 1, 2017. Published September 2011July 2017. DOI:10.1520/D7720–11.Originally approved in 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 States
D7720 − 11 (2017)
1.9 This international standard was developed in accordance with internationally recognized principles on standardization
established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued
by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
2. Referenced Documents
2.1 ASTM Standards:
D445 Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity)
D664 Test Method for Acid Number of Petroleum Products by Potentiometric Titration
D974 Test Method for Acid and Base Number by Color-Indicator Titration
D2896 Test Method for Base Number of Petroleum Products by Potentiometric Perchloric Acid Titration
D4378 Practice for In-Service Monitoring of Mineral Turbine Oils for Steam, Gas, and Combined Cycle Turbines
D4928 Test Method for Water in Crude Oils by Coulometric Karl Fischer Titration
D5185 Test Method for Multielement Determination of Used and Unused Lubricating Oils and Base Oils by Inductively
Coupled Plasma Atomic Emission Spectrometry (ICP-AES)
D6224 Practice for In-Service Monitoring of Lubricating Oil for Auxiliary Power Plant Equipment
D6299 Practice for Applying Statistical Quality Assurance and Control Charting Techniques to Evaluate Analytical Measure-
ment System Performance
D6304 Test Method for Determination of Water in Petroleum Products, Lubricating Oils, and Additives by Coulometric Karl
Fischer Titration
D6439 Guide for Cleaning, Flushing, and Purification of Steam, Gas, and Hydroelectric Turbine Lubrication Systems
D6595 Test Method for Determination of Wear Metals and Contaminants in Used Lubricating Oils or Used Hydraulic Fluids by
Rotating Disc Electrode Atomic Emission Spectrometry
D6786 Test Method for Particle Count in Mineral Insulating Oil Using Automatic Optical Particle Counters
D7042 Test Method for Dynamic Viscosity and Density of Liquids by Stabinger Viscometer (and the Calculation of Kinematic
Viscosity)
D7279 Test Method for Kinematic Viscosity of Transparent and Opaque Liquids by Automated Houillon Viscometer
D7414 Test Method for Condition Monitoring of Oxidation in In-Service Petroleum and Hydrocarbon Based Lubricants by
Trend Analysis Using Fourier Transform Infrared (FT-IR) Spectrometry
D7416 Practice for Analysis of In-Service Lubricants Using a Particular Five-Part (Dielectric Permittivity, Time-Resolved
Dielectric Permittivity with Switching Magnetic Fields, Laser Particle Counter, Microscopic Debris Analysis, and Orbital
Viscometer) Integrated Tester
D7483 Test Method for Determination of Dynamic Viscosity and Derived Kinematic Viscosity of Liquids by Oscillating Piston
Viscometer
D7484 Test Method for Evaluation of Automotive Engine Oils for Valve-Train Wear Performance in Cummins ISB
Medium-Duty Diesel Engine
D7596 Test Method for Automatic Particle Counting and Particle Shape Classification of Oils Using a Direct Imaging Integrated
Tester
D7647 Test Method for Automatic Particle Counting of Lubricating and Hydraulic Fluids Using Dilution Techniques to
Eliminate the Contribution of Water and Interfering Soft Particles by Light Extinction
D7670 Practice for Processing In-service Fluid Samples for Particulate Contamination Analysis Using Membrane Filters
D7684 Guide for Microscopic Characterization of Particles from In-Service Lubricants
D7685 Practice for In-Line, Full Flow, Inductive Sensor for Ferromagnetic and Non-ferromagnetic Wear Debris Determination
and Diagnostics for Aero-Derivative and Aircraft Gas Turbine Engine Bearings
D7690 Practice for Microscopic Characterization of Particles from In-Service Lubricants by Analytical Ferrography
E2412 Practice for Condition Monitoring of In-Service Lubricants by Trend Analysis Using Fourier Transform Infrared (FT-IR)
Spectrometry
3. Terminology
3.1 Definitions:
3.1.1 alarm, n—means of alerting the operator that a particular condition exists.
3.1.2 assignable cause, n—factor that contributes to variation in a process or product output that is feasible to detect and
identify; also called special cause.
3.1.3 boundary lubrication, n—condition in which the friction and wear between two surfaces in relative motion are determined
by the properties of the surfaces and the properties of the contacting fluid, other than bulk viscosity.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
D7720 − 11 (2017)
3.1.3.1 Discussion—
Metal to metal contact occurs and the chemistry of the system is involved. Physically adsorbed or chemically reacted soft films
(usually very thin) support contact loads. Consequently, some wear is inevitable.
3.1.4 chance cause, n—source of inherent random variation in a process which is predictable within statistical limits; also called
common cause.
3.1.5 characteristic, n—property of items in a sample or population which, when measured, counted or otherwise observed,
helps to distinguish between the items.
3.1.6 data set, n—logical collection of data that supports a user function and could include one or more data tables, files, or
sources.
3.1.6.1 Discussion—
Herein a data set is a population of values for a measurand from within a particular measurand set and covering an equipment
population.
3.1.7 distribution, n— as used in statistics, a set of all the various values that individual observations may have and the
frequency of their occurrence in the sample or population.
3.1.8 measurand, n—particular quantity subject to measurement.
3.1.8.1 Discussion—
In industrial maintenance a measurand is sometimes called an analysis parameter.
3.1.8.2 Discussion—
Each measurand has a unit of measure and has a designation related to its characteristic measurement.
3.1.9 nonparametric, n—term referring to a statistical technique in which the probability distribution of the constituent in the
population is unknown or is not restricted to be of a specified form.
3.1.10 normal distribution, n—frequency distribution characterized by a bell shaped curve and defined by two parameters: mean
and standard deviation.
3.1.11 outlying observation, n—observation that appears to deviate markedly in value from other members of the sample set in
which it appears, also called outlier.
3.1.12 parametric, n—term referring to a statistical technique that assumes the nature of the underlying frequency distribution
is known.
3.1.13 population, n—well defined set (either finite or infinite) of elements.
Statistical Process Control Technique Terms
3.1.14 statistical process control (SPC), n—set of techniques for improving the quality of process output by reducing variability
through the use of one or more control charts and a corrective action strategy used to bring the process back into a state of statistical
control.
3.1.15 state of statistical control, n—process condition when only common causes are operating on the process.
3.1.16 center line, n—line on a control chart depicting the average level of the statistic being monitored.
3.1.17 control limits, n—limits on a control chart that are used as criteria for signaling the need for action or judging whether
a set of data does or does not indicate a state of statistical control based on a prescribed degree of risk.
3.1.17.1 Discussion—
For example, typical three-sigma limits carry a risk of 0.135 % 0.135 % of being out of control (on one side of the center line)
when the process is actually in control and the statistic has a normal distribution.
3.1.18 warning limits, n—limits on a control chart that are two standard errors below and above the center line.
3.1.19 upper control limit, n—maximum value of the control chart statistic that indicates statistical control.
3.1.20 lower control limit, n—minimum value of the control chart statistic that indicates statistical control.
D7720 − 11 (2017)
Cumulative Distribution Technique Terms
3.1.21 cumulative distribution, n—representation of the total fraction of the population, expressed as either mass-, volume-,
area-, or number-based, that is greater than or less than discrete size values.
3.2 Definitions of Terms Specific to This Standard:
3.2.1 alarm limit, n—alarm condition values that delineate one alarm level from another within a measurand set; also called
alarm threshold.
3.2.1.1 Discussion—
When several alarm levels are designated, then a first alarm limit separates the normal level from the alert level, and a second alarm
limit separates the alert level from action level. In other words, measurand data values greater than the first alarm limit and
less-than-or-equal-to the second 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 be double-sided such as “greater
than X and less than –X”. Alarm limit values may represent the same units and scale as the corresponding measurand data set, or
they may be represented as a proportion such as a percent. Alarm limit values may be zero-based, or they may be relative to a
non-zero reference or other baseline value.
3.2.1.3 Discussion—
Statistical process control is used to evaluate alarm limits comparing a control limit value with an alarm limit value. Statistical
cumulative distribution is used to evaluate alarm limits by identifying a cumulative percent values corresponding with each alarm
limit value and comparing those results, for example, percen
...