Incorporation of Unit 1 Inspection Results on the STP Reactor Vessel Head Nuclear Asset Management Analysis

2007 ◽  
Author(s):  
Ernie Kee ◽  
Vera Moiseytseva ◽  
Alexander Galenko ◽  
David Morton ◽  
Elmira Popova
Keyword(s):  
Decision Making ◽  
Asset Management ◽  
Reactor Vessel ◽  
Management Decision ◽  
Optimal Timing ◽  
Inspection Method ◽  
Head Penetration ◽  
Failure Data ◽  
Timing Model ◽  
Local Experience

Including local experience information with generic data can maximize operational flexibility and help make better informed nuclear asset management decisions. Experience data are particularly helpful in risk-informed asset management decision-making. In 2004, South Texas Project (STP) used a risk-informed study to help decide the proper timing for reactor vessel head replacement. The decision to replace the reactor vessel heads was based on a previous study. Up to the time of decision-making inspections had not been performed at STP and therefore, the likelihood of finding a J-Groove failure in an inspection was based on generic industry information in both of the studies. In the Unit 1 fall outage, a full volumetric reactor vessel head penetration inspection was performed. The inspection method is briefly described. The algorithm for optimal timing of the reactor vessel head is presented and solved for the STP case. The generic industry J-Groove failure data is updated with the recent results from the Unit 1 outage inspection and the new failure projections are summarized. The optimal timing model is again run using the updated data and compared with the previous results.

scholarly journals Risks and Uncertainties in the Planning Phase of Offshore Wind Projects

2017 ◽  
pp. 334-357 ◽  
Author(s):  
Jannes van der Wal ◽  
Peter Eecen ◽  
Jasper Veldman
Keyword(s):  
Decision Making ◽  
Asset Management ◽  
Planning Process ◽  
Offshore Wind ◽  
Management Decision ◽  
Planning Phase ◽  
Complex Projects ◽  
Management Decision Making ◽  
The Government ◽  
Project Structure

Megaprojects are large and complex projects that entail multi-actor management, non-standard technology and processes. This chapter aims to explore offshore wind projects (OWPs) as megaprojects, particular in the planning phase. Based on interviews with 26 experts from a variety of backgrounds in the offshore wind industry in The Netherlands, the risks and uncertainties in the planning phase of OWPs and key factors in the decision making process are explored. A framework is presented that depicts the planning phase of an OWP, as well as ten risks and seven uncertainties that are most common in an OWP. The role of the government and the project structure are further highlighted. The findings of this research allow practitioners to gain a better overview of the planning process of an OWP and can help to improve asset management decision making.

A framework for considering externalities in urban water asset management

2011 ◽  
Vol 64 (11) ◽  
pp. 2199-2206 ◽  
Author(s):  
David Marlow ◽  
Leonie Pearson ◽  
Darla Hatton MacDonald ◽  
Stuart Whitten ◽  
Stewart Burn
Keyword(s):  
Decision Making ◽  
Urban Communities ◽  
Asset Management ◽  
Capital Investment ◽  
Management Decision ◽  
Water Utility ◽  
Societal Issues ◽  
Management Perspective ◽  
Management Decision Making ◽  
Decision Making Processes

Urban communities rely on a complex network of infrastructure assets to connect them to water resources. There is considerable capital investment required to maintain, upgrade and extend this infrastructure. As the remit of a water utility is broader than just financial considerations, infrastructure investment decisions must be made in light of environmental and societal issues. One way of facilitating this is to integrate consideration of externalities into decision making processes. This paper considers the concept of externalities from an asset management perspective. A case study is provided to show the practical implications to a water utility and asset managers. A framework for the inclusion of externalities in asset management decision making is also presented. The potential for application of the framework is highlighted through a brief consideration of its key elements.

scholarly journals Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

2021 ◽  
Author(s):  
Frederick Appoh ◽  
Akilu Yunusa-Kaltungo ◽  
Jyoti Kumar Sinha ◽  
Moray Kidd
Keyword(s):  
Decision Making ◽  
Hybrid Model ◽  
Asset Management ◽  
Fault Tree Analysis ◽  
Pairwise Comparison ◽  
Rolling Stock ◽  
Management Decision ◽  
Service Reliability ◽  
Original Target ◽  
Criticality Analysis

AbstractRailway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.

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