POWER February 2011 - 40

INSTRUMENTATION & CONTROL
New Tools for Diagnosing and
Troubleshooting Power Plant
Equipment Faults
The Electric Power Research Institute has developed a pair of diagnostic tools
that combine and integrate features from multiple sources of plant information.
The Diagnostic Advisor and the Asset Fault Signature Database
will improve diagnostics for and troubleshooting of equipment faults by
providing a holistic view of the condition of plant equipment.
By Aaron Hussey and Stephen Hesler, Electric Power Research Institute, and Randall Bickford, Expert Microsystems Inc.
I
n competitive environments, electric
power plants must operate under reduced
operation and maintenance (O&M) budgets
while maintaining high reliability and
availability. Early detection of equipment
faults and subsequent planning of maintenance
actions can help cut costs while maximizing
availability. However, the detection
of equipment faults and subsequent troubleshooting
often require information beyond
what traditional process instrumentation
provides. Improving the use of power plant
information sources for equipment fault
prediction and diagnosis can help electric
utilities meet plant availability goals and reduce
O&M costs.
In a new plant design, it is desirable to
install sensors based not only on process
control design but also on equipment fault
detection needs, as identified through a structured
failure modes effects analysis (FMEA)
and/or fault tree analysis. At existing plants,
however, information obtained manually
through predictive maintenance or operator
rounds can be used alongside process data for
detecting and diagnosing equipment faults.
The Electric Power Research Institute
(EPRI) has designed a new diagnostic analysis
software application and database that assists
electric power generation plant staff in
the early identification of equipment faults.
That early detection enables rapid incident response
and prevents failures of critical power
generation equipment. This article describes
EPRI's work in designing a pair of tools that
combine features from multiple sources of
plant information to assist with troubleshooting
and diagnostics of plant equipment.
Recent Trends in Anomaly Detection
Many of today's electric utilities store
years of historical process data in servers
40
known as data historians. This large quantity
of data contains information about
process and equipment history that, when
extracted, can indicate trends that provide
insight into equipment condition. However,
without data-mining tools, it is impractical
to monitor this large amount of
data continuously. As a result, most utilities
have used data historians mostly for
looking back at trends after a failure has
occurred, as part of a root-cause analysis.
New techniques in data mining, combined
with today's efficient computing capabilities,
have enabled continuous online monitoring of
plant process data. Advanced pattern recognition
(APR) techniques recently have been
applied to tens of thousands of data points
that often reside in multiple data historians
across a fleet of power plants. This technique
allows a utility engineer or technician to
compare historical data with current data for
hundreds of systems and components across
multiple operating units, thereby identifying
anomalous trends.
Although plant process data can be
monitored efficiently today with APR
techniques, traditional condition indications
from predictive maintenance and operator
rounds, for example, remain key to
troubleshooting equipment problems. Unlike
plant process data, however, this type
of information often is stored in standalone,
non-networked databases and can
be accessed only by the technician(s) who
collected the information. Web-based enterprise
asset management systems offer
an interface that can provide summaries
of predictive maintenance and operator
rounds results to staff across an entire
fleet of units. With this type of information,
staff tasked with monitoring and
diagnosing plant equipment can make a
www.powermag.com
complete assessment of equipment health
prior to maintenance planning.
Centralized Monitoring and
Diagnostics Strategy
Although a large quantity of information is
available for the purpose of monitoring and
diagnosing equipment, some key hurdles for
efficient troubleshooting remain:
■ Site technical staff are burdened fully with
everyday duties and cannot dedicate additional
time to trending data.
■ Equipment faults often progress to failure
quickly, and staff cannot react in a
timely fashion.
■ It is time-consuming to assemble all relevant
data for a given piece of equipment
when a fault condition is known.
■ Maintenance is performed on a periodic
basis, and the latest results may be out
of date.
Some of these problems have been addressed
by centralized monitoring and
diagnostic (M&D) facilities that are responsible
for detecting equipment faults
upon their earliest indication and working
with plant staff to plan the appropriate
maintenance in the most cost-effective
manner. These centralized facilities require
up-front investment including:
■ Multi-disciplinary staffing that includes
experienced operators, maintenance technicians,
and engineers.
■ Information integration, including the
connection of plant data historians and
enterprise asset management tools to the
central facility.
■ Brick-and-mortar facilities in a location
central to monitored units.
POWER | February 2011

http://www.powermag.com

POWER February 2011

Table of Contents for the Digital Edition of POWER February 2011