Dynamic Reliability and Resilience Assessment of Jack-up Production Platform Legs Based on Dynamic Bayesian Networks

Abstract

This paper proposes a dynamic reliability and system resilience assessment method for the leg structures of a mobile offshore production unit (MOPU) that is subjected to long-term service in complex marine environments. A fault tree of the leg system is first constructed and mapped to a Bayesian network (BN). Time slices are then introduced to expand the network into a dynamic Bayesian network (DBN). To capture history-dependent cumulative effects, a non-homogeneous state transition mechanism driven by physical damage laws is embedded within the DBN framework, within which the Melchers power-law corrosion model and the Palmgren-Miner fatigue model are dynamically modelled. A restoration sub-network comprising diagnosis capability, resource accessibility and maintenance capability is built, and a semi-Markov process is adopted to describe the transition probabilities of degradation and recovery. A quantitative resilience indicator is defined based on the “resilience triangle” theory. Numerical simulations are carried out for four typical scenarios: normal operation, frequent jacking operations, fishing-vessel collision, and blowout. The results show that fatigue degradation is the dominant factor affecting reliability; welded joints are the most sensitive nodes, and under collision/blowout conditions the sensitivity of stress-concentration nodes rises to the second highest; bottom-side collision combined with blowout.