Anomaly detection with the Switching Kalman Filter for structural health monitoring

Anomaly detection with the Switching Kalman Filter for structural health monitoring The detection of changes in structural behaviour over time, that is, anomalies, is an important aspect in structural safety analysis. This paper proposes an anomaly detection method that combines the existing Bayesian Dynamic Linear Models framework with the Switching Kalman Filter theory. The key aspect of this method is its capacity to detect anomalies based on the prior probability of an anomaly, a generic anomaly model, as well as transition probabilities between a normal and an abnormal state. Moreover, the approach operates in a semisupervised setup where normal and abnormal state labels are not required to train the model. 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 identifying the anomaly caused by refection work, without triggering any false alarm. It also provided the specific information about the dam's health and conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Structural Control and Health Monitoring Wiley

Anomaly detection with the Switching Kalman Filter for structural health monitoring

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1545-2255
eISSN
1545-2263
D.O.I.
10.1002/stc.2136
Publisher site
See Article on Publisher Site

Abstract

The detection of changes in structural behaviour over time, that is, anomalies, is an important aspect in structural safety analysis. This paper proposes an anomaly detection method that combines the existing Bayesian Dynamic Linear Models framework with the Switching Kalman Filter theory. The key aspect of this method is its capacity to detect anomalies based on the prior probability of an anomaly, a generic anomaly model, as well as transition probabilities between a normal and an abnormal state. Moreover, the approach operates in a semisupervised setup where normal and abnormal state labels are not required to train the model. 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 identifying the anomaly caused by refection work, without triggering any false alarm. It also provided the specific information about the dam's health and conditions.

Journal

Structural Control and Health MonitoringWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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