Picture offshore wind farm

Open Access research that is improving renewable energy technology...

Strathprints makes available scholarly Open Access content by researchers across the departments of Mechanical & Aerospace Engineering (MAE), Electronic & Electrical Engineering (EEE), and Naval Architecture, Ocean & Marine Engineering (NAOME), all of which are leading research into aspects of wind energy, the control of wind turbines and wind farms.

Researchers at EEE are examining the dynamic analysis of turbines, their modelling and simulation, control system design and their optimisation, along with resource assessment and condition monitoring issues. The Energy Systems Research Unit (ESRU) within MAE is producing research to achieve significant levels of energy efficiency using new and renewable energy systems. Meanwhile, researchers at NAOME are supporting the development of offshore wind, wave and tidal-current energy to assist in the provision of diverse energy sources and economic growth in the renewable energy sector.

Explore Open Access research by EEE, MAE and NAOME on renewable energy technologies. Or explore all of Strathclyde's Open Access research...

Dynamic risk and reliability assessment for ship machinery decision making

Dikis, K. and Lazakis, I. and Michala, A. L. and Raptodimos, Y. and Theotokatos, G. (2016) Dynamic risk and reliability assessment for ship machinery decision making. In: Risk, Reliability and Safety. CRC/Taylor & Francis Group, London, pp. 685-692. ISBN 9781315374987

[img]
Preview
Text (Dikis-etal-ESREL2016-Dynamic-risk-and-reliability-assessment-for-ship-machinery-decision-making)
Dikis_etal_ESREL2016_Dynamic_risk_and_reliability_assessment_for_ship_machinery_decision_making.pdf
Accepted Author Manuscript

Download (678kB) | Preview

Abstract

The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm.