Stochastic filter-based fatigue crack growth prediction for pipelines considering unknown model parameters and measurement uncertainty

Abstract

In this study a methodology is developed and implemented in Python for fatigue crack growth prediction in pipelines, by leveraging measurement data and fatigue growth model predictions. Specifically, Particle Filter (PF) algorithm, Paris law, and the stress intensity factor (SIF) model in API 579 are integrated into a tool to use noisy crack size measurements for estimating the current crack size and fatigue model parameters, also known as joint state-parameter estimation. For illustration purpose, pseudo-data set for crack size measurements is generated considering additive Gaussian white noise of two different noise levels, aiming to mimic crack size data obtained from In-line Inspection (ILI) tools. It is found that the crack state can be reliably estimated compared to noisy measurements and initial model predictions, and the true model parameters can be updated with good accuracy. As such, the current crack size estimated and model parameters updated can be used in the fatigue growth model (i.e., Paris law) to predict the future trajectory of the fatigue crack growth. As more measurement data becomes available, the developed tool more reliably estimates the future crack growth trajectory.