This thesis proposes the initial stages of the development of a NUI – Asset Integrity Case (Normally Unattended Installation). An NUI – Asset Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed. A key driver for improved asset integrity monitoring is centred on the level of accurate reporting of incidents. This stems from incidents to key offshore systems and areas. For example, gas turbine driven generators where 22% of fuel gas leaks were undetected with 60% of these 22% having been found to have ignited. Accordingly, there is a need for dynamic risk assessment and improved asset integrity monitoring. The immediate objective of this research is to investigate how a dynamic risk model can be developed for an offshore system. Subsequently, two dynamic risk assessment models were developed for an offshore gas turbine driven electrical power generation system. Bayesian Networks provided the base theory and algorithms to develop the models. The first model focuses on the consequences of one component failure. While the second model focuses on the consequences of a fuel gas release with escalated fire and explosion, based upon several initiating failures. This research also provides a Multiple Attribute Decision Analysis (MADA) to determine the most suitable Wireless Sensor Network (WSN) configuration for asset integrity monitoring. The WSN is applied to the same gas turbine system as in the dynamic risk assessment models. In the future, this work can be expanded to other systems and industries by applying the developed Asset Integrity Case framework and methodology. The framework outlines the steps to develop a dynamic risk assessment model along with MADA for the most suitable remote sensing and detection methods.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:733974 |
| Date | January 2018 |
| Creators | Loughney, S. J. |
| Contributors | Wang, J. ; Matellini, D. ; Nguyen, T. T. |
| Publisher | Liverpool John Moores University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchonline.ljmu.ac.uk/7990/ |
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