Pressure vessels are subject to deterioration processes, such as corrosion and fatigue, which can lead to failure. Inspections and repairs are performed to mitigate this risk. Large industrial facilities (e.g., oil and gas refineries) often have regularly scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired if necessary. This paper presents a decision analysis framework for the risk-based maintenance (RBM) planning of corroding pressure vessels. After a vessel has been inspected, this framework determines the optimal maintenance time of each defect, where the optimal time is the one that minimizes the total expected cost over the lifecycle of the vessel. The framework allows for multiple defects and two failure modes (leak and burst), and accounts for the dependent failure events. A stochastic gamma process is used to model the future deterioration growth to determine the probability of vessel failure. The novel growth model presents a simple method to predict both the depth and length of each corrosion defect to enable burst analysis. The decision analysis framework can aid decision makers in deciding when a repair or replacement should be performed. This method can be used to immediately inform the decision maker of the optimal decision postinspection. A numerical example of a corroding pressure vessel illustrates the method.
For engineering systems, decision analysis can be used to determine the optimal decision from a set of options via utility maximization. Applied to inspection and maintenance planning, decision analysis can determine the best inspection and maintenance plan to follow. Decision analysis is relatively straightforward for simple systems. However, for more complex systems with many components or defects, the set of all possible inspection and maintenance plans can be very large. This paper presents the use of a genetic algorithm to perform inspection and maintenance plan optimization for complex systems. The performance of the genetic algorithm is compared to optimization by exhaustive search. A numerical example of life cycle maintenance planning for a corroding pressure vessel is used to illustrate the method. Genetic algorithms are found to be an effective approach to reduce the computational demand of solving complex inspection and maintenance optimizations.
Pressure vessels are subject to deterioration processes, such as corrosion and fatigue. If left unchecked these deterioration processes can lead to failure; therefore, inspections and repairs are performed to mitigate this risk. Oil and gas facilities often have regular scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired or replaced if necessary.
The objective of this paper is to perform a decision analysis to determine the best course of action for an operator to follow after a pressure vessel is inspected during a shutdown period. If the pressure vessel is inspected and an unexpectedly deep corrosion defect is detected an operator has two options: schedule a repair for the next shutdown period, or perform an immediate unscheduled repair. A scheduled repair is the preferred option as it gives the decision maker lead time to accommodate the added labour and budgetary requirements. This preference is accounted for by a higher cost of immediate unscheduled repairs relative to the cost of a scheduled repair at the next shutdown. Depending on the severity of deterioration either option could present the optimal course of action. In this framework the decision that leads to the minimum expected cost is selected. A stochastic gamma process was used to model the future deterioration growth using the historical inspection data, considering the measurement error and uncertain initial wall thickness, to determine the probability of pressure vessel failure. The decision analysis framework can be used to aid decision makers in deciding when a repair or replacement action should be performed. This method can be used in real time decision making to inform the decision maker immediately post inspection. A numerical example of a corroding pressure vessel illustrates the method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.