Charles R. Farrar

TL;DR: In this article, machine learning algorithms based on Artificial Neural Networks (ANNs) including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs.

TL;DR: In this paper, structural health monitoring in the context of a Micon 65/13 horizontal axis wind turbine was described as a process in statistical pattern recognition, and it was shown that vertical wind shear and turbulent winds lead to different modal contributions in the operational response of the turbine suggesting that the sensitivity of operational data to damage depends on the wind loads.

TL;DR: The vibration-based damage detection process in the context of a problem in statistical pattern recognition is posed, and the application of this statistical paradigm to two different real world structures is studied focusing on the issues of data normalization and feature extraction.

TL;DR: In this article, the authors provide an overview of methods that examine changes in measured vibration response to detect, locate, and characterize damage in structural and mechanical systems, and discuss critical issues for future research in the area of modal-based damage identification.

TL;DR: Four techniques based on Akaike information criterion, partial autocorrelation function, root mean squared error, and singular value decomposition are presented and found that these four techniques do not converge to a unique solution, rather all require somewhat qualitative interpretation to define the optimal model order.