Unsupervised anomaly detection in non-linear mechanical systems for structural health monitoring
Publication date
2026-05-01
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Springer
Series or journal
Acta Mechanica Sinica
ISSN
Periodical volume
42
Periodical issue
8
Article ID
725585
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Nonlinear dynamic systems
Operational modal analysis
Structural health monitoring
Variational autoencoders
dtec.bw
Abstract
This work investigates the application of operational modal analysis (OMA) and unsupervised variational autoencoders (VAEs) for damage and anomaly detection in structural health monitoring. The underlying structural model is a multi-degree-of-freedom (MDOF) Bouc-Wen-Baber-Noori hysteretic system, which captures the highly nonlinear behavior typical of degrading and pinching effects in real-world structures. By simulating and analyzing the responses of this nonlinear MDOF system under stochastic excitation, we assess the effectiveness of data-driven approaches, including VAEs trained on healthy states, for detecting changes in system dynamics. The example system was specifically chosen to represent anomalies that manifest predominantly in the nonlinear components of the system, while leaving the linear part unchanged. Special attention is given to the sensitivity, interpretability, and ease of obtaining extracted features from the different methods. The results show that VAEs offer advantages over the well established COVariance-driven stochastic subspace identification OMA approach when applied to markedly nonlinear data.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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