A stochastic model for RUL prediction of subsea pipeline subject to corrosion-fatigue degradation

Abstract

A robust model based on stochastic processes with Copula function is developed for remaining useful life (RUL) prediction of subsea pipeline subject to corrosion-fatigue degradation. Gamma-based stochastic degradation process is used to simulate the corrosion degradation of pipeline, and fatigue crack propagation is estimated with Wiener process. The Particle Filter (PF) and Kalman Filter (KF) are utilized to update the model parameters. Then, an improved joint distribution model incorporating marginal distributions and copula function is presented to capture the complex dependencies between corrosion and fatigue. By establishing an acceptable pipeline failure threshold, a comparison is conducted among three stochastic process models to assess the practicality and effectiveness of the developed robust model. The results indicate that copula-based model has superiority in predictive accuracy with a deviation of 0.5. Considering interaction of corrosion-fatigue has a substantial impact on improving the accuracy of RUL. The present model can significantly contribute to the proactive maintenance and management of subsea pipelines, thereby enhancing operational efficiency and reducing potential risks.