Gaussian Process Time Series Model for Life Prognosis of Metallic Structures

Abstract

Al 2024-T351 fatigue specimens have been modeled using a kernel-based multi-variate Gaussian Process approach. The Gaussian Process model projects fatigue affecting input variables to output crack growth by probabilistically inferring the underlying nonlinear relationship between input and output. The Gaussian Process approach not only explicitly models the uncertainty due to scatter in material microstructure parameter but it also implicitly models the loading sequence effect due to variable loading. The loading sequence effect is modeled through the Gaussian Process optimal hyperparameters by using the crack length data observed over the entire domain of spectrum loading. The performance in the crack growth prediction is evaluated for two covariance functions, a radial basis-based, anisotropic, covariance function and a neural network-based isotropic covariance function. Furthermore, the performance of different types of scaling, used to scale the input—output data space, is tested. It is found that for the radial basis-based anisotropic covariance function with normalized scaling, the prediction error is consistently lower compared to other combinations. In addition, the Gaussian Process model allows determination of the collapse load condition, which is a desirable feature for the online health monitoring and prognosis.