Risk and cost optimised condition monitoring system design for marine renewable energy
Item statusRestricted Access
Embargo end date03/07/2021
Kenny, Calum Jethro
Marine Renewable Energy (MRE) has significant potential to contribute to global energy security and sustainability. However, the high initial Levelised Cost Of Energy (LCOE) of MRE, as well as issues with survivability and reliability, present challenges for its commercialisation. Predictive maintenance using Condition Monitoring Systems (CMS) addresses these challenges by improving availability and reducing operational expenditure. This is achieved by monitoring critical subsystems for the detection and prediction of faults and allowing for maintenance personnel to take mitigating action prior to subsystem or component failure. However, the design and application of CMS in MRE is not standardised due to the relatively nascent operational experience in the sector, as well as the great variety of Wave Energy Converter (WEC) operating principles. Furthermore, the balance between risk monitoring and cost reduction in CMS design is not readily accounted for in current reliability methods. A portfolio approach to sensor selection and risk reduction is therefore proposed, incorporating a Failure Mode and Effects Analysis (FMEA) to identify and prioritise risks versus reward. This addresses the risk-reduction and cost-benefit trade-off in CMS design. This thesis presents two approaches to optimise risk-management and cost-benefit associated with a CMS sensor portfolio applied to MRE devices, using both an expert judgement, and a matrix-based optimisation for comparative purposes. In the former approach, sensor upgrades for an articulated WEC are divided into functional packages and expert judgement is used to determine sensor selection for a CMS portfolio based on the practical installation requirements of the sensors. Using the latter approach, sensor upgrades are made to the cooling system of an operational Tidal Energy Converter (TEC). Firstly, gathered upgrade data is used in conjunction with a physical model to observe deviation in cooling system performance. Secondly, changes in relationships between key cooling system variables are observed across a dataset spanning 6 months. The latter approach is found to effectively identify key application areas for sensor installation in a quantitative manner. The addition of sensors in a risk and cost optimised CMS portfolio offers a valuable practical compromise between: an expert’s best-guess qualitative approach (of limited comparative experience value in new applications) and extensive lifetime operational models (unavailable or unvalidated in MRE as data is too sparse). The risk and cost optimised CMS portfolio approach may be extended beyond MRE and applied to other industries, particularly benefitting practitioners in industries with relatively little experience in CMS design and operation.