Can I read High-dimensional Data Analytics in Structural Health Monitoring and Non-destructive Evaluation: a Review Paper on EtoBox?

High-dimensional Data Analytics in Structural Health Monitoring and Non-destructive Evaluation: a Review Paper by Hamed Momeni; Arvin Ebrahimkhanlou is a Engineering article available to read on EtoBox.

What is High-dimensional Data Analytics in Structural Health Monitoring and Non-destructive Evaluation: a Review Paper about?

Abstract This paper aims to review high-dimensional data analytic (HDDA) methods for structural health monitoring (SHM) and non-destructive evaluation (NDE) applications. High-dimensional data is a type of data in which the number of features for each observation is much larger than the number of all observations. High-dimensional data may violate assumptions of the classic methods for statistical modeling and data analysis. Then, classic statistical modeling will no longer be applicable. HDDA methods were developed to overcome this challenge and analyze these types of data. In the field of SHM/NDE, there are several sources of high-dimensionality. Examples include a large number of data points in continuous waves/signals or high-resolution images/videos. HDDA methods are used as a dimension-reduction tool to preprocess data for further analysis, or they are directly implemented for damage detection and localization. This paper reviews six HDDA methods as well as existing and potential applications in SHM/NDE. Particularly, this paper discusses the vast range of implemented SHM/NDE applications from crack detection to missing data imputation. Furthermore, experimental and simulated

Who reads High-dimensional Data Analytics in Structural Health Monitoring and Non-destructive Evaluation: a Review Paper?

Author

Hamed Momeni; Arvin Ebrahimkhanlou

Publisher

IOP Publishing

Published

2022

Language

EN

Field

Engineering (Physical Sciences)