ASTM D7720-21
(Guide)Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination
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,...
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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.
Designation: D7720 − 21
Standard Guide for
Statistically Evaluating Measurand Alarm Limits when Using
Oil Analysis to Monitor Equipment and Oil for Fitness and
1
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 Thisguideprovidesspecificrequirementstostatistically
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, health, and environmental practices and deter-
or is eliminated. Outputs of this technique are specific mea- mine the applicability of regulatory limitations prior to use.
1.9 This international standard was developed in accor-
dance with internationally recognized principles on standard-
1
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-
Current edition approved Oct. 1
...
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 (Reapproved 2017) D7720 − 21
Standard Guide for
Statistically Evaluating Measurand Alarm Limits when Using
Oil Analysis to Monitor Equipment and Oil for Fitness and
1
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 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
1
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 May 1, 2017Oct. 1, 2021. Published July 2017October 2021. Originally approved in 2011. Last previous edition approved in 20112017 as
D7720 – 11.D7720 – 11 (2017). DOI:10.1520DOI:10.1520/D7720-21.⁄D7720-11R17.
*A Summary of Changes section appears at the end of this standard
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
1
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D7720 − 21
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 h
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