Abstract
Ship structures are subjected to repetitive wave loads that can lead to fatigue damage. Accumulation of fatigue damage may eventually result in structural failure. In order to mitigate fatigue hazard to ship structures, timely inspections and maintenance actions must be planned during their life-span. As delays in the process of decision-making could result in the increase of the risk of failure, decision-supportive frameworks working in a real-time fashion are extremely important. In this paper, a life-cycle management framework for fatigue-critical details is proposed on the basis of Dynamic Bayesian Network (DBN) and Influence Diagram (ID). The proposed framework, established based on a stochastic fatigue crack growth model, leverages exact inference and discretisation of continuous random variables to enable real-time Bayesian updating and utility-based decision-making. Compared with previous life-cycle management studies, the new framework is capable of making decisions regarding different inspection techniques and appropriate repair actions in a holistic manner. The relatively simple structure of DBN and ID also makes the proposed framework especially attractive to stakeholders and practitioners, who may have limited knowledge of statistics, Bayesian updating and utility theories.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.