OnLine Monitoring for Instrument Calibration and Equipment Condition Assessment Regional Workshop on

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On-Line Monitoring for Instrument Calibration and
Equipment Condition AssessmentRegional
Workshop on Modernization Projects of NPP
Instrumentation and Control Systems Related to
Power Uprates and License Renewals
ProjectPortoroz, Slovenia14-18 April 2008

• Brandon Rasmussen
• brasmussen_at_kurztech.com
• Instrumentation Control Center
• operated by Kurz Technical Services, Inc.
• Harriman, TN 37748

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Applied On-Line MonitoringInstrument
Calibration Interval Extension

• The initial focus area for using empirical models
  for On-Line Monitoring (OLM) in the power
  industry was for calibration monitoring of
  safety-related transmitters in the nuclear power
  industry
• Safety related instrumentation is recalibrated at
  the end of every fuel cycle (18 months).
• Safety parameters are measured by redundant
  instrumentation, typically a group of 4
  instruments.
• Studies have shown that 90 of the
  instrumentation is still within calibration after
  18 months.
• Using an on-line monitoring model to monitor
  instrument calibration ensures that calibrations
  will only be performed where necessary, resulting
  in reduced radiation exposure, reduced outage
  workloads, and cost savings.

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Instrument Calibration Interval Extension -
Illustration
200 Instruments

• It is required that at least 1 transmitter within
  a redundant set be calibrated to prevent common
  mode failure (Assume groups of 4)

50 Mandatory Calibrations
150 Instruments will be assessed
60 Condition Based Calibrations

• Conservatively estimate that 40 of the
  instruments assessed will require calibration
• Assessment is made through on-line analysis of
  the sensor data

40
60
90 Instruments do not require calibration

• This conservative illustration indicates that
  only 55 (110) of the safety related instruments
  would require calibrations.
• Significant benefits are available from the
  reduced personnel radiation exposure and the
  reduced outage workload (indirect costs).
• Calibration costs are estimated between 1k and
  3k per instrument (direct costs).

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Applied Instrument Calibration Monitoring
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Applied Instrument Calibration Monitoring
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Reasons Utilities Give for OLM of Instrument
Calibration

• Manual calibrations only validate the correct
  operation of the instrumentation periodically
  therefore, faulty sensors may remain undetected
  for periods up to the calibration frequency.
• Studies have shown that less than 5 of the 50 to
  150 calibrated instrument channels were in a
  degraded condition that required maintenance.
  With the estimated cost of a typical manual
  calibration ranging between 900 and 2000, the
  cost of the potentially unnecessary work is
  significant.
• Performing maintenance on components that are
  operating correctly provides an opportunity for a
  fault to enter the system.
• It also is expected to provide
• Monitoring under normal operating conditions.
• Enhanced detection of infant mortality.
• Enhanced maintenance planning capability.
• OLM may detect other operational abnormalities.

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Expected Outcomes of OLM for Instrument
Calibration

• The proposed methods will continuously monitor
  the instrument channel condition and identify
  those that have degraded to an extent that
  warrants calibration.
• The identified instrument channels will be
  classified as either needing calibration at the
  next outage or as entirely inoperable based on
  the degree of degradation.
• It is expected that the on-line methods will
  reduce maintenance costs, reduce radiation
  exposure, reduce the potential for
  miscalibration, increase instrument reliability,
  and may reduce equipment downtime.

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Expected Secondary Benefits

• OLM can also detect and identify
• Equipment degradation
• System changes
• Operational changes
• It is expected that these secondary benefits
  could be the largest monetary driver for a
  utility.

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Documentation of British Energys Implementation

• Plant Application of On-Line Monitoring for
  Calibration Interval Extension of Safety Related
  Instruments Volumes 1 2, EPRI, Palo Alto, CA,
  and British Energy Group PLC, Suffolk, UK 2006.
  1013486.
• Updates were planned for 2007 and 2008.

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Implementation at British Energyhttp//mydocs.epr
i.com/docs/CorporateDocuments/EPRI_Journal/2007-Fa
ll/1016127_International.pdf

• On-Line Monitoring Improves Instrument
  Calibration at British Energy
• British Energys Sizewell B station became the
  first to apply the EPRI guidelines to extend the
  calibration interval of safety-related
  instruments.
• British Energy initially applied the OLM
  techniques to about 200 instruments at Sizewell
  B, focusing on the pressure, level, and flow
  transmitters in the plants primary and secondary
  protection systems.
• Overall, 80 of the transmitters evaluated
  during the first OLM cycle were found to be
  within calibration tolerance throughout the fuel
  cycle.
• During the first outage, most of the
  transmitters that were candidates for calibration
  interval extension (70 of the total transmitters
  evaluated) were, in fact, extended.

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Implementation at British Energyhttp//mydocs.epr
i.com/docs/CorporateDocuments/EPRI_Journal/2007-Fa
ll/1016127_International.pdf

• The additional 10 of transmitters that were
  within tolerance were nonetheless scheduled for
  calibration to maintain conservatism during the
  initial implementation.
• British Energy estimates that OLM, when fully
  deployed, will routinely reduce outage duration
  to 20 days from the 25 days normally required for
  transmitter calibration, saving 1.5 million per
  avoided outage day, or 7.5 million per operating
  cycle.
• Additional savings are expected from reductions
  in labor costs, radiation exposure, and
  calibration errors.
• British Energys goal is to expand the OLM
  application to nearly 2500 transmitters,
  including many in the secondary system (steam
  side) of the plant.
• The project methodology and application for the
  Sizewell project, together with a set of
  supporting analyses and results, are published in
  the EPRI report Plant Application of On-Line
  Monitoring for Calibration Interval Extension of
  Safety-Related Instruments Volumes 1 and 2,
  (1013486), 2006.

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Instrument Calibration Monitoring Applications

• Electricite de France has practiced the use of
  on-line monitoring to selectively calibrate
  safety instrumentation for over a decade.
• 2005 British Energy obtained regulatory
  approval to implement OLM technology to extend
  the calibration interval of their safety-related
  transmitters at Sizewell B (Westinghouse PWR)
• Software provided by Analysis and Measurements
  Services Corporation, Knoxville, TN
• EPRI / British Energy / AMS are collaborating to
  document this implementation from 2006-2008
• The implementation followed a graded approach
  over 3 fuel cycles

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New Research for NRC

• To prepare for expected plant specific license
  amendments from utilities involved in the OLM
  Implementation Project, in 2004 the NRC funded
  the research to gain additional insights into the
  potential challenges of OLM.
• Objective provide guidance to the regulatory
  review process of OLM.
• Deliverables
• NUREG/CR-6895 Technical Review of On-Line
  Monitoring Techniques for Performance Assessment
• Volume 1 State of the Art (published 2006)
• Overview and literature survey.
• Volume 2 Theoretical Issues (final draft 2007)
• Technical methods and derivations
• Volume 3 Limiting Case Studies (final draft
  2007)
• Assumptions and demonstrations.

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U.S. NPP License Amendment Request

• VC Summer NPP (South Carolina, USA) was one of
  the original test participants in the EPRI OLM
  Implementation Users Group.
• On March 9, 2005, VC Summer met with the NRC to
  discuss the submission of a plant specific
  license amendment for the application of OLM for
  calibration interval extension.
• On February 6, 2006, VC Summer submitted a 74
  page License Amendment Request LAR 05-0667
• Subsequently, VC Summer withdrew the request.

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Anomaly Detection

• OLM systems for monitoring instrument
  calibration, and on a larger scale equipment or
  processes, attempt to identify anomalies in the
  process data
• Properly trained empirical process models can
  detect early anomalies in the monitored
  instrument channels.
• An anomaly is defined as any change in an
  individual instrument channel or a more
  significant change in the process that is unusual
  with respect to the expected operation of the
  process.
• The expected operation of the process is defined
  by the training data set.

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Anomaly Detection

• Types of identifiable anomalies
• Gradual calibration drift in one or more channels
  (Calibration Monitoring)
• Abrupt changes in channels measurements, either a
  single point or a series of consecutive points.
• Abrupt changes in the channel variance.
• Changes between the channel inter-relationships
  (may indicate a process drift or equipment
  problem).
• Common mode failure if redundant and
  non-redundant instruments are included in the
  same model.

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Extensions to Equipment Monitoring Anomaly
Interpretation

• The detection of anomalies in the data signifies
  that a change has occurred in the learned normal
  physical relationships of a system component.
• The output of an On-Line Monitoring system is a
  set of identified anomalies. In many cases, an
  equipment degradation mechanism will manifest
  itself through changes in the instrument channel
  indications well before the eventual failure.
• Linking a set of anomalies to a given equipment
  degradation mechanism is referred to as anomaly
  interpretation and is the basis for the extension
  of empirical process modeling to equipment
  condition assessment.

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Empirical Equipment Monitoring

• Monitoring equipment using empirical models has
  the potential for the identification of early
  warning indicators of component degradation or
  failure.
• This is the largest financial driver for
  utilities to implement empirical OLM systems.
• One of the largest cost benefits can be derived
  from avoiding a lost-power event

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Summary of Lost Power Incidents at Selected U.S.
Nuclear Units 2000-2003
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Lost Power Incidents at Selected U.S. Nuclear
Units 2000-2003
1 Low Dollar Estimate 250,000/day Mean
Dollar Estimate 600,000/day High Dollar
Estimate 2,500,000/day
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Empirical Equipment Monitoring
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How are Assessments Made?

• Consider an Autoassociative models output

Assessments are made based on the behavior of the
residual values. In this case, the model
residuals indicate normal operation
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How are Assessments Made?
In this case, the model residuals indicate
abnormal operation. This information allows for
the most basic assessment that based on
historical operation, sensors A, B, C, D are
reporting data that is unexpectedly high.
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Anomaly Interpretation

• Assume that a failure mode or event (E) exists
  whereby signals A, B, C, D increase prior to
  the eventual failure.
• Further, assume that these increases are
  identifiable by an empirical model as anomalies,
  and this mode is differentiable from competing
  modes.
• Logic Rule Interpretation
• If RA gt t AND RB gt t AND RC gt t AND RD gt t
• Then event E will occur.
• Probability Based Interpretation
• P(E RA gt t) 0.2, P(E RB gt t) 0.2,
• P(E RA gt t RB gt t) 0.6,
• P(E RA gt t RB gt t RC gt t ) 0.9,
• P(E RA gt t RB gt t RC gt t RD gt t ) 0.97

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Event Notification
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Simple Anomaly Interpretation Scheme
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Anomaly Pattern Example
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Making Assessments

• Assessments require Interpretation, without
  interpretation the information is typically not
  useable
• In its most basic form it is the indication of an
  anomaly
• Assessment may requires some (or all) of the
  following
• System expertise to understand the information,
  including knowledge of the empirical model and
  knowledge of the monitored system
• Software based tools to identify the cause of an
  observed deviation
• Information from related systems to support the
  assessed condition
• Physical investigation at the equipment site
  additional diagnostic testing

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Typical Scheme of ECM
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Automated Assessments

• The ideal scenario is to devise an automated
  anomaly interpretation scheme
• Maps a set of observed anomalies to a specific
  degradation or assessment
• Requires extensive effort to link potential
  available indicators to a specific fault

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Automated Anomaly Interpretation
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Requirements for Accurate Reliable Early Warning

• Successful OLM models will provide early warning
  of impending failure or warning of sufficient
  degradation. Several requirements must be met in
  order for these capabilities to be available
• Differentiability The various failure modes or
  degradation mechanisms for a component or piece
  of equipment must be differentiable from one
  another so that the diagnosis is accurate and
  unambiguous.
• Anomalous Indications of the condition must be
  identifiable by an OLM model.
• Repeatability All occurrences of a given
  degradation mechanism or failure must
  consistently produce the same precursor
  indications.
• Timely Indications must occur and be properly
  identified with enough lead time to provide some
  advantage (e.g. event avoidance).

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Establishing a Failure Signature Database

• EVALUATE
• Failure Modes Degradation Modes
• consider probability of modes based on history or
  manufacturers data
• consider effect of failure with respect to
  personnel safety, loss of production, maintenance
  and related costs
• DEFINE
• Potential indicators of these modes (with respect
  to information contained in or produced by an OLM
  system)
• consider the requirements for accurate
  identification (DART)
• consider input from various modeling tools
  deployed
• construct interpretation logic to combine
  resultant information from multiple monitoring
  tools and other information streams to present a
  concise diagnosis (in some cases, prognosis)

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Other Industries Utilizing Empirical Equipment
Process Models

• Aircraft
• Chemical processing
• Petroleum
• Heavy machinery
• Power Fossil, Nuclear
• Manufacturing
• Locomotive
• Wind Turbines
• Security Monitoring
• Pulp and Paper
• Pipelines

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On-Line Monitoring Benefits

• Information Filtering
• Performance Monitoring
• Calibration Monitoring Reduction
• Early Warning of equipment degradation
• Provide system engineers and maintenance staff
  with necessary information to make informed,
  cost-effective operations and maintenance
  decisions based on the actual condition of the
  system/equipment
• Allow earlier mitigation or corrective actions
• Reduce the likelihood of unplanned plant trips or
  power reductions
• Reduce equipment damage
• Reduce likelihood of repetitive failures
• Knowledge Capture and retention through embedding
  expert knowledge into automatic anomaly
  interpretation
• Cost-savings associated with calibration
  reductions
• Cost-savings associated with planned and
  preventative maintenance rather than unplanned
  and reactive