Picture child's feet next to pens, pencils and paper

Open Access research that is helping to improve educational outcomes for children

Strathprints makes available scholarly Open Access content by researchers in the School of Education, including those researching educational and social practices in curricular subjects. Research in this area seeks to understand the complex influences that increase curricula capacity and engagement by studying how curriculum practices relate to cultural, intellectual and social practices in and out of schools and nurseries.

Research at the School of Education also spans a number of other areas, including inclusive pedagogy, philosophy of education, health and wellbeing within health-related aspects of education (e.g. physical education and sport pedagogy, autism and technology, counselling education, and pedagogies for mental and emotional health), languages education, and other areas.

Explore Open Access education research. 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.