Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling
(2022) In Structural Integrity 21. p.185-205- Abstract
Structural health monitoring techniques aim at providing an automated solution to the threat of unsurveilled aging of structures that can have tremendous consequences in terms of fatalities, environmental pollution, and economic loss. To assess the state of damage of a complex structure, this paper proposes to fully characterize its behavior under multiple environmental and operational scenarios and compare new sensor measurements with the baseline behavior. However, the repeated simulations of a nonlinear, time-dependent structural model with high-dimensional input parameters represent a severe computational bottleneck for large-scale engineering assets. This chapter presents how to use efficient reduced-order modeling techniques to... (More)
Structural health monitoring techniques aim at providing an automated solution to the threat of unsurveilled aging of structures that can have tremendous consequences in terms of fatalities, environmental pollution, and economic loss. To assess the state of damage of a complex structure, this paper proposes to fully characterize its behavior under multiple environmental and operational scenarios and compare new sensor measurements with the baseline behavior. However, the repeated simulations of a nonlinear, time-dependent structural model with high-dimensional input parameters represent a severe computational bottleneck for large-scale engineering assets. This chapter presents how to use efficient reduced-order modeling techniques to mitigate the computational effort of many-query simulations without jeopardizing the accuracy. To compare new sensor measurements with the natural behavior of synthetic solutions, the proposed methodology uses hierarchical semi-supervised learning algorithms on a small amount of extracted damage-sensitive features, thus allowing one to assess the state of damage in real time. Using the inexpensive simulations, one can also optimally place sensors to maximize the observability of discriminant features. The all-round methodology is validated on a numerical example.
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- author
- Bigoni, Caterina ; Guo, Mengwu LU and Hesthaven, Jan S.
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Digital twins, One-class classification, Reduced order models, Sensor placement, Simulation-based anomaly detection, Structural health monitoring
- host publication
- Structural Health Monitoring Based on Data Science Techniques
- series title
- Structural Integrity
- volume
- 21
- pages
- 21 pages
- publisher
- Springer
- external identifiers
-
- scopus:85117955283
- ISSN
- 2522-5618
- 2522-560X
- ISBN
- 978-3-030-81715-2
- 978-3-030-81716-9
- DOI
- 10.1007/978-3-030-81716-9_9
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- id
- 4b53736c-f5b3-4da4-800b-ad420d089ba9
- date added to LUP
- 2024-03-19 12:16:42
- date last changed
- 2024-06-25 18:05:39
@inbook{4b53736c-f5b3-4da4-800b-ad420d089ba9,
abstract = {{<p>Structural health monitoring techniques aim at providing an automated solution to the threat of unsurveilled aging of structures that can have tremendous consequences in terms of fatalities, environmental pollution, and economic loss. To assess the state of damage of a complex structure, this paper proposes to fully characterize its behavior under multiple environmental and operational scenarios and compare new sensor measurements with the baseline behavior. However, the repeated simulations of a nonlinear, time-dependent structural model with high-dimensional input parameters represent a severe computational bottleneck for large-scale engineering assets. This chapter presents how to use efficient reduced-order modeling techniques to mitigate the computational effort of many-query simulations without jeopardizing the accuracy. To compare new sensor measurements with the natural behavior of synthetic solutions, the proposed methodology uses hierarchical semi-supervised learning algorithms on a small amount of extracted damage-sensitive features, thus allowing one to assess the state of damage in real time. Using the inexpensive simulations, one can also optimally place sensors to maximize the observability of discriminant features. The all-round methodology is validated on a numerical example.</p>}},
author = {{Bigoni, Caterina and Guo, Mengwu and Hesthaven, Jan S.}},
booktitle = {{Structural Health Monitoring Based on Data Science Techniques}},
isbn = {{978-3-030-81715-2}},
issn = {{2522-5618}},
keywords = {{Digital twins; One-class classification; Reduced order models; Sensor placement; Simulation-based anomaly detection; Structural health monitoring}},
language = {{eng}},
pages = {{185--205}},
publisher = {{Springer}},
series = {{Structural Integrity}},
title = {{Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling}},
url = {{http://dx.doi.org/10.1007/978-3-030-81716-9_9}},
doi = {{10.1007/978-3-030-81716-9_9}},
volume = {{21}},
year = {{2022}},
}