This paper focuses on the creation of a dynamic probabilistic model which simulates deterioration trends of a marine engine lubricationsystem. The approach is based on risk and the implementation is achieved through a...This paper focuses on the creation of a dynamic probabilistic model which simulates deterioration trends of a marine engine lubricationsystem. The approach is based on risk and the implementation is achieved through a dynamic Bayesian network (dBN).Risk can be useful for decision making, while dBNs are a powerful tool for risk modelling and prediction models. The model takesinto account deterioration of engine components, oil degradation and the off-line condition monitoring technique of oil analysis, inthe context of predictive maintenance. The paper aims to efficiently predict probability evolution for main engine lubrication failureand to decide upon the most beneficial schemes from a variety of lubrication oil analysis interval schemes by introducing monetarycosts and producing the risk model. Real data and respective analysis, along with expert elicitation, are utilized for achieving modelquantification, while themodel is materialized through a code in the Matlab environment. Results from the probabilistic model showa realistic simulation for the system and indicate the obvious, that with more frequent oil analyses and respective maintenance orrepairs, the probability of failure drops significantly. However, the results from the risk model highlight that the costs can redefinescheme suggestions, as they can correspond to low probabilities of failure but also to higher costs. A two-month interval scheme issuggested, in contrast to the most preferred practice among shipping companies of a three-month interval. The developed model isin general identified as a failure prediction tool focusing on marine engine lubrication failure.展开更多
文摘This paper focuses on the creation of a dynamic probabilistic model which simulates deterioration trends of a marine engine lubricationsystem. The approach is based on risk and the implementation is achieved through a dynamic Bayesian network (dBN).Risk can be useful for decision making, while dBNs are a powerful tool for risk modelling and prediction models. The model takesinto account deterioration of engine components, oil degradation and the off-line condition monitoring technique of oil analysis, inthe context of predictive maintenance. The paper aims to efficiently predict probability evolution for main engine lubrication failureand to decide upon the most beneficial schemes from a variety of lubrication oil analysis interval schemes by introducing monetarycosts and producing the risk model. Real data and respective analysis, along with expert elicitation, are utilized for achieving modelquantification, while themodel is materialized through a code in the Matlab environment. Results from the probabilistic model showa realistic simulation for the system and indicate the obvious, that with more frequent oil analyses and respective maintenance orrepairs, the probability of failure drops significantly. However, the results from the risk model highlight that the costs can redefinescheme suggestions, as they can correspond to low probabilities of failure but also to higher costs. A two-month interval scheme issuggested, in contrast to the most preferred practice among shipping companies of a three-month interval. The developed model isin general identified as a failure prediction tool focusing on marine engine lubrication failure.
