Download our summary brochure - Department of Mechanical

Research and development funding:
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National Science Foundation
South Texas Project Nuclear Operating
Company
Electric Power Research Institute
The University of Texas at Austin
Operations Research and Industrial Engineering
People and organizations involved in RIAM
From the University of Texas at
Austin:
Risk Informed
Asset Management
(RIAM)
A joint research and development program between
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The University of Texas at Austin
Operations Research and Industrial
Engineering, ETC 5.120
1 University Station, C2200
Austin, TX 78712, USA
Phone: 512-471-3078
Fax: 512-232-1489
[email protected]
www.me.utexas.edu/~popova/riam.html
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ORIE at the University of
Texas at Austin
McCombs School of Business at the University of
Texas at Austin
Risk Management at
STPNOC
Nuclear Asset Management Program at EPRI
Elmira Popova, Ph.D., Associate Professor, Department of Mechanical Engineering, [email protected]
This research program is a joint effort
between the Operations Research and
Industrial Engineering Graduate Program and the McCombs School of
Business at the University of Texas at
Austin,
David Morton, Ph.D., Associate Professor, Department of Mechanical Engineering, [email protected]
Paul Damien, Ph.D., Professor, Department of Information, Risk, and
Operations Management, McCombs
School of Business,
[email protected]
Alexander Galenko, Ph.D. candidate,
Department of Mechanical Engineering, [email protected]
From the Risk Management group
at South Texas Project Nuclear Operating Company:
Ernie Kee, [email protected]
Alice Sun, [email protected]
Rick Grantom, [email protected]
Drew Richards,
[email protected]
http://www.me.utexas.edu/~orie
http://www.mccombs.utexas.edu
the Risk Management Group at South
Texas Project Nuclear Operating
Company (STPNOC),
http://www.stpnoc.com
and the Nuclear Asset Management
Program at the Electric Power Research Institute (EPRI).
From EPRI:
Stephen Hess, Program Manager,
[email protected]
http://www.epri.com
Risk Informed Asset Management (RIAM)
The main objective of the RIAM program is: to
make optimal risk-informed decisions at both
operational and executive management levels
by taking into account budget, internal project
dependencies, outage duration, and regulatory
safety constraints; to appropriately model and
include the uncertainty related to rates of return on investments, energy prices, failure
mechanisms, and costs for replacement and
spare parts; and to provide decision-makers
quantified feedback on decision-making performance.
We strive to advance the state of knowledge,
through basic research, on decision making,
uncertainty, optimization, simulation, and statistics. We apply these novel methodologies to
optimal decision-making at operational and
executive level in electric power generation.
The main areas of research are:
Operational (decision-making) level:
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Single item maintenance policies
Portfolio of maintenance strategies
Proper modeling of failure distributions,
maintenance costs, and life-cycle management
Executive (decision-making) level
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Project valuation
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Risk metrics for project valuation and
analysis
Optimal allocation of funds among projects
Major Accomplishments
Basic Factors to Forecast Maintenance Cost
and Failure Processes for Nuclear Power
Plants: In this project we found the most important factors to predict the cost for planned and
unplanned maintenance. The cost incurred includes labor cost, cost for new parts, and emergency order of expensive items. At the plant management level there is a budgeted amount of
money to be spent every year for such operations.
It is important to have a good forecast for this cost
since unexpected events can trigger it to a very
high level. In this research we present a statistical
factor model to forecast the maintenance cost for
the coming month. One of the factors is the expected number of unplanned (due to failure) maintenance interventions. We introduce a Bayesian
model for the failure rate of the equipment, which
is input to the cost forecasting model. The importance of equipment reliability and prediction in the
commercial nuclear power plant is presented
along with applicable governmental and industry
organization requirements. A detailed statistical
analysis is performed on a set of maintenance cost
and failure data gathered at STPNOC. The paper
will appear in the Nuclear Engineering and Design journal in 2006.
Operational Level Models and Methods for
Risk Informed Nuclear Asset Management: In
this research, we model and solve the problem of
choosing a maintenance policy for a single item,
from a particular class of policies, that minimizes
total expected cost over a finite horizon. We optimize the replacement time for a system that fails
according to an increasing failure-rate distribution.
Our work is applied to a system at STPNOC. We
developed a computational algorithm that solves
the problem of finding the optimal preventive
maintenance time and implemented it at STPNOC
by creating an Excel-based add-in. The paper
appeared in the ANS, PSA 2005 Proceedings.
Optimal Preventive Maintenance Under
Decision Dependent Uncertainty: We analyze a system of N components with dependent
failure times. The goal is to obtain the optimal
block replacement interval (different for each
component) over a finite horizon that minimizes
the expected total maintenance cost. Our model
captures the change in the future joint failure time
distribution due to selecting a particular preventative maintenance action. The paper appeared in
ICONE14 2006 Proceedings.
Ongoing and Future Projects
Bayesian Nonparametric Modeling of Failure Times and Cost Parameters: Typically,
the mathematical modeling of failure times are
based on parametric models. These models fail to
capture the true underlying relationships in the
data; indeed, under a parametric assumption, the
hazard rates are treated as unimodal and skewed,
which, we show, is incorrect. Importantly, assuming an increasing failure rate, is way off the mark
in the present context. Since hazard and cost estimates are important from a risk management perspective, potentially gross errors, resulting from
purely parametric models, can be obviated.
Project Prioritization via Optimization with
Applications to Electric Power Generation:
Managers of electric power generation plants are
routinely faced with the problem of allocating limited
resources over a collection of potential plant projects.
One can consider each project as an “asset” and the
plant manager as a “portfolio manager.” When selecting a portfolio of projects, a fundamental question is
whether one should optimize or prioritize? In this
project, research will be conducted to develop an
optimal priority list. The approach requires specifying
budget scenarios with weights estimating the likelihood of the respective scenarios. The research will
develop an optimal prioritization approach and apply
it to a collection of candidate projects at STPNOC, as
defined in the STPNOC plant long-range plan. We
will demonstrate the benefit the prioritization approach provided by accounting for multiple budget
scenarios.
Failure Probability From Fault Tree Cut
Sets: To properly capture the effect of an equipment
failure, we not only have to understand the maintenance cost but also we have to understand the effect
of the failure on production revenue. To do this requires the probability that the equipment failure will
result in production loss as well as the magnitude of
the loss. STPNOC uses fault trees to represent failure
logic leading to production loss at the plant level on
an average basis. We are now developing the required
technology to reduce the fault tree solutions to get the
probability (and magnitude) of production loss for
each failure represented in the STPNOC fault tree
model.