ASTM D7720-11

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

SIGNIFICANCE AND USE
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?
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.
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,” absolute alarming...

General Information

• Status: Historical
• Publication Date: 31-May-2011
• 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

Relations

• Referred By: ASTM D7917-14(2018) - Standard Practice for Inductive Wear Debris Sensors in Gearbox and Drivetrain Applications
• Effective Date: 01-Jun-2011
• Referred By: ASTM D7874-13(2022) - Standard Guide for Applying Failure Mode and Effect Analysis (FMEA) to In-Service Lubricant Testing
• Effective Date: 01-Jun-2011
• Referred By: ASTM D7898-14(2020) - Standard Practice for Lubrication and Hydraulic Filter Debris Analysis (FDA) for Condition Monitoring of Machinery
• Effective Date: 01-Jun-2011
• Referred By: ASTM D7889-21 - Standard Test Method for Field Determination of In-Service Fluid Properties Using IR Spectroscopy
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8120-17 - Standard Test Method for Ferrous Debris Quantification
• Effective Date: 01-Jun-2011
• Referred By: ASTM D6224-22 - Standard Practice for In-Service Monitoring of Lubricating Oil for Auxiliary Power Plant Equipment
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8506-23 - Standard Guide for Microbial Contamination and Biodeterioration in Turbine Oils and Turbine Oil Systems
• Effective Date: 01-Jun-2011
• Referred By: ASTM D7919-14(2021) - Standard Guide for Filter Debris Analysis (FDA) Using Manual or Automated Processes
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8182-18 - Standard Test Method for Alloy Classification of Wear Debris using Laser-Induced Breakdown Spectroscopy (LIBS)
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8004-23 - Standard Test Method for Fuel Dilution of In-Service Lubricants Using Surface Acoustic Wave Sensing
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8127-23 - Standard Test Method for Coupled Particulate and Elemental Analysis using X-ray Fluorescence (XRF) for In-Service Lubricants
• Effective Date: 01-Jun-2011
• Referred By: ASTM D4378-22 - Standard Practice for In-Service Monitoring of Mineral Turbine Oils for Steam, Gas, and Combined Cycle Turbines
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8323-20 - Standard Guide for Management of In-Service Phosphate Ester-based Fluids for Steam Turbine Electro-Hydraulic Control (EHC) Systems
• Effective Date: 01-Jun-2011
• Referred By: ASTM D8184-18e1 - Standard Test Method for Ferrous Wear Debris Monitoring in In-Service Fluids Using a Particle Quantifier Instrument
• Effective Date: 01-Jun-2011
• Referred By: ASTM D7669-20 - Standard Guide for Practical Lubricant Condition Data Trend Analysis
• Effective Date: 01-Jun-2011

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
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 cumulative distribution plot of the sorted data set. These
percent-based measurand values are used to create, evaluate
1.1 Thisguideprovidesspecificrequirementstostatistically
and adjust alarm limits.
evaluate measurand alarm thresholds, which are called alarm
limits, as they are applied to data collected from in-service oil 1.4 This guide may be applied to sample data from testing
analysis. These alarm limits are typically used for condition of in-service lubricating oil samples collected from machinery
monitoring to produce severity indications relating to states of (for example, diesel, pumps, gas turbines, industrial turbines,
machinery wear, oil quality, and system contamination. Alarm hydraulics) whether from large fleets or individual industrial
limits distinguish or separate various levels of alarm. Four applications.
levels are common and will be used in this guide, though three
1.5 This guide may also be applied to sample data from
levels or five levels can also be used.
testing in-service oil samples collected from other equipment
1.2 A basic statistical process control technique described applications where monitoring for wear, oil condition, or
herein is recommended to evaluate alarm limits when mea- system contamination are important. For example, it may be
suranddatasetsmaybecharacterizedasbothparametricandin applied to data sets from oil filled transformer and circuit
control. A frequency distribution for this kind of parametric breaker applications.
data set fits a well-behaved two-tail normal distribution having
1.6 Alarm limit evaluating techniques, which are not statis-
a “bell” curve appearance. Statistical control limits are calcu-
ticallybasedarenotcoveredbythisguide.Also,thetechniques
lated using this technique.These control limits distinguish, at a
of this standard may be inconsistent with the following alarm
chosen level of confidence, signal-to-noise ratio for an in-
limit selection techniques: “rate-of-change,” absolute
control data set from variation that has significant, assignable
alarming, multi-parameter alarming, and empirically derived
causes. The operator can use them to objectively create,
alarm limits.
evaluate, and adjust alarm limits.
1.7 The techniques in this guide deliver outputs that may be
1.3 Astatistical cumulative distribution technique described
compared with other alarm limit selection techniques. The
herein is also recommended to create, evaluate, and adjust
techniquesinthisguidedonotprecludeorsupersedelimitsthat
alarm limits. This particular technique employs a percent
have been established and validated by an Original Equipment
cumulativedistributionofsorteddatasetvalues.Thetechnique
Manufacturer (OEM) or another responsible party.
is based on an actual data set distribution and therefore is not
1.8 This standard does not purport to address all of the
dependent on a presumed statistical profile.The technique may
safety concerns, if any, associated with its use. It is the
be used when the data set is either parametric or
responsibility of the user of this standard to establish appro-
nonparametric, and it may be used if a frequency distribution
priate safety and health practices and determine the applica-
appears skewed or has only a single tail. Also, this technique
bility of regulatory limitations prior to use.
may be used when the data set includes special cause variation
in addition to common cause variation, although the technique
2. Referenced Documents
should be repeated when a special cause changes significantly
2
2.1 ASTM Standards:
or is eliminated. Outputs of this technique are specific mea-
D445 Test Method for Kinematic Viscosity of Transparent
surand values corresponding to selected percentage levels in a
1
This guide is under the jurisdiction of ASTM Committee D02 on Petroleum
2
Products, Liquid Fuels, and Lubricants and is the direct responsibility of Subcom- For referenced ASTM standards, visit the ASTM website, www.astm.org, or
mittee D02.96.04 on Guidelines for In-Services Lubricants Analysis. contact ASTM Customer Service at [e-mail]. For Annual Book of ASTM
Current edition approved June 1, 2011. Published September 2011. Standards volume information, refer to the standard’s Document Summary page on
DOI:10.1520/D7720–11. the ASTM website.
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