Off-Line and On-Line Fatigue Crack Growth Prediction Using Multivariate Gaussian Process

Off-line and On-line fatigue crack growth prediction of Aluminum 2024 compact-tension (CT) specimens under variable loading has been modeled, using multivariate Gaussian Process technique. The Gaussian Process model projects the input space to an output space by probabilistically inferring the underlying non-linear function relating input and output. For the off-line prediction the input space of the model is trained with parameters that affect fatigue crack growth, such as number of fatigue cycles, minimum load, maximum load, and load ratio. For the case of online prediction, the model input space is trained using features found from piezoelectric sensor signals rather than training the input space with loading parameters, which are difficult to measure in a real time scenario. Two different algorithms such as Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) are used to extract the principal features from sensor signals. In both the off-line and on-line case the output space is trained with known associated crack lengths. Once the Gaussian process model is trained, a new output space for which the corresponding crack length or damage state is not known is predicted using the trained Gaussian process model. Concepts are validated through several numerical examples.