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.; Walls, Lesley and Revie, Matthew and Bedford, Tim, eds. (2016) Dynamic risk and reliability assessment for ship machinery decision making. In: Risk, Reliability and Safety. CRC/Taylor & Francis Group, GBR, pp. 685-692. ISBN 9781315374987

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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.


Dikis, K., Lazakis, I. ORCID logoORCID: https://orcid.org/0000-0002-6130-9410, Michala, A. L. ORCID logoORCID: https://orcid.org/0000-0001-7821-1279, Raptodimos, Y. ORCID logoORCID: https://orcid.org/0000-0002-7508-5956 and Theotokatos, G. ORCID logoORCID: https://orcid.org/0000-0003-3547-8867; Walls, Lesley, Revie, Matthew and Bedford, Tim