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Anomaly detection with the Switching Kalman Filter for structural health monitoring

Luong Ha Nguyen and James Alexandre Goulet

Article (2018)

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Detecting changes in structural behaviour, i.e. anomalies over time is an important aspect in structural safety analysis. The amount of data collected from civil structures keeps expanding over years while there is a lack of data-interpretation methodology capable of reliably detecting anomalies without being adversely affected by false alarms. This paper proposes an anomaly detection method that combines the existing Bayesian Dynamic Linear Models framework with the Switching Kalman Filter theory. The potential of the new method is illustrated on the displacement data recorded on a dam in Canada. The results show that the approach succeeded in capturing the anomalies caused by refection work without triggering any false alarms. It also provided the specific information about the dam's health and conditions. This anomaly detection method offers an effective data-analysis tool for Structural Health Monitoring.

Uncontrolled Keywords

Anomaly Detection, Bayesian, Dynamic Linear Model, Switch Kalman Filter, Structural Health Monitoring, False Alarm, Dam

Subjects: 1000 Civil engineering > 1000 Civil engineering
1100 Structural engineering > 1104 Structural analysis
Department: Department of Civil, Geological and Mining Engineering
Grant number: RGPIN-2016-06405
PolyPublie URL: https://publications.polymtl.ca/2868/
Journal Title: Structural Control and Health Monitoring (vol. 25, no. 4)
Publisher: Wiley
DOI: 10.1002/stc.2136
Official URL: https://doi.org/10.1002/stc.2136
Date Deposited: 26 Mar 2018 12:18
Last Modified: 08 Jun 2023 11:37
Cite in APA 7: Nguyen, L. H., & Goulet, J. A. (2018). Anomaly detection with the Switching Kalman Filter for structural health monitoring. Structural Control and Health Monitoring, 25(4), 1-18. https://doi.org/10.1002/stc.2136


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