Covariate-adjusted functional data analysis for structural health monitoring
Publication date
2025-05-15
Document type
Forschungsartikel
Author
Organisational unit
Publisher
Cambridge University Press
Series or journal
Data-Centric Engineering
ISSN
Periodical volume
6
Article ID
e27
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.
Version
Published version
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