Confounder-adjusted covariances of system outputs and applications to structural health monitoring

Publication date
2024-09-27
Document type
Forschungsartikel
Author
Mendler, Alexander
Organisational unit
DOI
URI
Publisher
Elsevier
Series or journal
Mechanical Systems and Signal Processing
ISSN
Periodical volume
224
Article ID
111983
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Conditional covariance
Supervised learning
Kernel method
Mahalanobis distance
Principal component analysis
Temperature removal
dtec.bw
Abstract
Automated damage detection is an integral component of each structural health monitoring (SHM) system. Typically, measurements from various sensors are collected and reduced to damage-sensitive features, and diagnostic values are generated by statistically evaluating the features. Since changes in data do not only result from damage, it is necessary to determine the confounding factors (environmental or operational variables) and to remove their effects from the measurements or features. Many existing methods for correcting confounding effects are based on different types of mean regression. This neglects potential changes in higher-order statistical moments, but in particular, the output covariances are essential for generating reliable diagnostics for damage detection. This article presents an approach to explicitly quantify the changes in the covariance, using conditional covariance matrices based on a non-parametric, kernel-based estimator. The method is applied to the Munich Test Bridge and the KW51 Railway Bridge in Leuven, covering both raw sensor measurements (acceleration, strain, inclination) and extracted damage-sensitive features (natural frequencies). The results show that covariances between different vibration or inclination sensors can significantly change due to temperature changes, and the same is true for natural frequencies. To highlight the advantages, it is explained how conditional covariances can be combined with standard approaches for damage detection, such as the Mahalanobis distance and principal component analysis. As a result, more reliable diagnostic values can be generated with fewer false alarms.
Description
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Version
Published version
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