Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform

• URL: http://arxiv.org/abs/2009.00470v2
• Date: Mon, 18 Oct 2021 19:50:12 GMT
• Title: Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform
• Authors: Monica Arul and Ahsan Kareem
• Abstract summary: A number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. This paper proposes the use of a relatively new time series representation named Shapelet Transform to autonomously identify anomalies in SHM data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The continuous monitoring provides valuable information about the structure that can help in providing a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing the use of a relatively new time series representation named Shapelet Transform in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation that is solely based on the shape of the time series data. In consideration of the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithm on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from a SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

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