  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.
