Distributions of fatigue damage from data-driven strain prediction using Gaussian process regression
Samuel J. Gibson, Timothy J. Rogers, Elizabeth J. Cross
Abstract
Fatigue is a leading cause of structural failure; however, monitoring and prediction of damage accumulation remains an open problem, particularly in complex environments where maintaining sensing equipment is challenging. As a result, there is a growing interest in virtual loads monitoring, or inferential sensing, particularly for predicting strain in areas of interest using machine learning methods. This paper pursues a probabilistic approach, relying on a Gaussian process (GP) regression, to produce both strain predictions and a predictive distribution of the accumulated fatigue damage in a given time period. Here, the fatigue distribution is achieved via propagation of successive draws from the posterior GP through a rainflow count. The establishment of such a distribution crucially accounts for uncertainty in the predictive model and will form a valuable element in any probabilistic risk assessment. For demonstration of the method, distributions for predicted fatigue damage in an aircraft wing are produced across 84 flights. The distributions provide a robust measure of predicted damage accumulation and model uncertainty.