On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring
Simon Pfingstl,
Markus Zimmermann
Abstract:Gaussian process regression is a powerful method for predicting states based on given data. It has been successfully applied for probabilistic predictions of structural systems to quantify, for example, the crack growth in mechanical structures. Typically, predefined mean and covariance functions are employed to construct the Gaussian process model. Then, the model is updated using current data during operation while prior information based on previous data is ignored. However, predefined mean and covariance f… Show more
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