The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data
Abstract
Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions, the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changing environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, which can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration , a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linear combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residual created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmental and operational conditions.
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“This task is linked to the inherent variability uncertainty class of our taxonomy, which typically aims to effectively capture the influence of longterm trends underlying the induced dynamics [80,106].”
From On the Consistent Classification and Treatment of Uncertainties in Structural Health Monitoring Applications (2025) · cites this paper as [106]
Citations
Cites
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Johansen, S. (1995) · doi:10.1093/0198774508.001.0001 · Cited by 5,409
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Research works printed but not linked: 1
- Manson G., 2002, Identifying damage sensitive, environment insensitive features for damage detection, In Proc. of the IES Conf.
Cited by
Worden, K., & Cross, E. J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.04.022 · Cited by 135
Self-citationKromanis, R., & Kripakaran, P. (2016) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-016-0161-z · Cited by 52
He, H., Wang, W., & Zhang, X. (2019) · Structural Health Monitoring · doi:10.1177/1475921718755573 · Cited by 35
Cadini, F., Lomazzi, L., Ferrater Roca, M., Sbarufatti, C., & Giglio, M. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108048 · Cited by 25
Kamariotis, A., Vlachas, K., Ntertimanis, V., Koune, I., Cicirello, A., & Chatzi, E. (2025) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering · doi:10.1115/1.4067140 · Cited by 15
Confounder-adjusted Covariances of System Outputs and Applications to Structural Health Monitoring
Neumann, L., Wittenberg, P., Mendler, A., & Gertheiss, J. (2024) · arXiv · doi:10.48550/arxiv.2409.17735 · Cited by 25
Metadata
- Authors
- Cross, E. J., Worden, K., & Chen, Q.
- Affiliations
- Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK
- Institute of Vibration Engineering Research, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
- Venue
- Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2011-04-27
- Volume
- 467
- Issue
- 2133
- Pages
- 2712-2732
- ISSN
- 1364-5021
- 1471-2946
- Cited by
- 173
- References
- 30
- Language
- en
- Licence
- royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)
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