POWER February 2011 - 41

INSTRUMENTATION & CONTROL
■ Executive support for establishing an implementation
plan and for communicating
the need and benefit across the fleet.
When implemented successfully, M&D
staff provide solutions to at least two of the key
hurdles: limited plant staff time for trending data
and the need to identify equipment faults early
enough that plant staff can respond in a timely
manner. Figure 1 shows a typical M&D center.
Diagnostic Advisor Summary
The role of the Diagnostic Advisor (DA),
which EPRI currently is developing, is to
1. The solutions squad. Centralized
monitoring and diagnostic staff provide solutions
to at least two key hurdles at power
plants: limited plant staff time for trending
data and the need to identify equipment faults
early enough that plant staff can respond in a
timely fashion. Courtesy: EPRI
infer the presence of specific fault conditions
by monitoring the fault effects in the
observable plant operating data. Using inference,
the DA matches current plant information
with a possible fault indication
that may lead to a known fault condition.
It is important to identify the unique fault
condition as soon as possible to enable
timely corrective action. Sometimes, the
observed data might not match a single
fault condition uniquely. In this situation,
the DA will identify and rank the most
likely fault conditions. In addition, the DA
will identify troubleshooting or corrective
action options to improve the diagnosis or
remedy the problem if that information is
available in a fault signature.
It is desirable that the DA provide broad
coverage for plant and enterprise assets. The
DA will operate with a user-specific or industry-wide
fault signature knowledge base.
EPRI's DA software system will facilitate
the conversion of observed plant data
to specific diagnoses, not just identified
anomalies. The companion database will
provide an extensible reasoning framework
that, over time, will be filled systematically
with knowledge specific to fossil plant
systems, equipment, and components (all
referred to collectively as " assets " in this
article). The software and database will be
flexible enough to integrate and process
data from several sources, including:
2. Cyber sleuths. The Diagnostic Advisor and Asset Fault Signature Database might be implemented
within the framework of an enhanced enterprise asset management system. These
tools will be used to diagnose and troubleshoot potential equipment failures. Courtesy: EPRI
Enhanced EAM tool
Tech exam database
Risk-informed
maintenance tool
Subject matter
expert
OLM/FWM/TPM
analysis tools
Diagnostic Advisor
■ Observed signatures of actual asset problems
(faults) contributed by users of fielded
online monitoring (OLM) and fleetwide
monitoring (FWM) systems.
■ Simulated asset fault signatures (AFS)
from plant simulators and/or physicsbased
models.
■ Asset fault-related health status information,
such as operator rounds results, predictive
maintenance data, and engineering
assessments.
■ Theoretical AFSs developed by subject
matter experts from FMEAs.
The DA will perform anomaly interpretation,
diagnosis, and condition assessment
more effectively than has previously been
possible. This will be accomplished by combining
the data types listed above to assemble
fault signatures for specific assets so that,
over time, theoretical FMEA-based AFSs
will be refined and enhanced using operational
experience across the industry.
The DA will consist of two primary elements:
■
An advisory software tool that provides
the user interface and reasoning capability
for performing anomaly interpretation,
diagnosis, and condition assessment.
■ An AFS Database for capturing and organizing
observed, simulated, and theoretical
AFS information used by the DA software
and by other applications, such as vendorprovided
OLM and FWM software.
Asset Fault Signature Database
Overview
EPRI's AFS Database archives experientially
derived fault signatures and designtheoretical-derived
fault signatures based
on FMEA/failure mode effects and criticality
analysis (FMECA) information. The
AFS Database allows for streamlined entry
of new fault signatures and for efficient
retrieval of fault signatures for diagnostic
purposes. This type of power industry
knowledge is not documented effectively
in any other database.
The fault signatures will be used in conOLM/FWM/TPM
analysis
tools
AFS Database
Plant staff
PM/FMEA
database
Design
Experience
Notes: AFS= asset fault signature, EAM = enterprise asset monitoring, FMEA = failure modes and effects analysis, FWM =
fleetwide monitoring, OLM = online monitoring, PM = preventive maintenance, TPM = thermal performance monitoring.
February 2011 | POWER
www.powermag.com
junction with EPRI's DA software to accomplish
the diagnostic process; however,
the AFS Database is not exclusive to the
DA and could be used in conjunction with
other diagnostic software, such as future
versions of vendor-provided online monitoring
software.
One of the main goals of the AFS Database
is to capture the fault signatures
in such a way that they can be used and
exchanged across the power industry.
This role of the AFS Database has very
high value, as it is unlikely that a single
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POWER February 2011

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