On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
This work addresses the problem of improving model reliability for engineers in SHM, though it is incremental as it applies existing SLT methods to a specific domain.
The paper tackles model selection in Structural Health Monitoring (SHM) by applying Statistical Learning Theory (SLT) to estimate generalization bounds, showing that incorporating domain knowledge into kernel smoother regression for linear oscillator impulse responses yields a lower guaranteed risk and enhances generalization.
Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although statistical model validation in this field is often performed heuristically, it is possible to estimate generalisation more rigorously using the bounds provided by Statistical Learning Theory (SLT). Therefore, this paper explores the selection process of a kernel smoother for modelling the impulse response of a linear oscillator from the perspective of SLT. It is demonstrated that incorporating domain knowledge into the regression problem yields a lower guaranteed risk, thereby enhancing generalisation.