Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring

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Sensors are now commonly employed for monitoring and controlling of engineering systems. Despite significant advances in sensor technologies and their reliability, sensor fault is inevitable. Sensor data reconstruction methods have been studied to recover the missing or faulty sensor data, as well as to enable sensor fault detection and identification. Most existing sensor data reconstruction methods use only the spatial correlations among the sensor data, but they rarely consider the temporal correlations among the data. Use of temporal correlations among the sensor data can potentially improve the accuracy for reconstructing the data. This paper presents a data-driven bidirectional recurrent neural network (BRNN) for sensor data reconstruction, taking into consideration the spatiotemporal correlations among the sensor data. The methodology is demonstrated using the sensor data collected from the Telegraph Road Bridge located along the 1-275 Corridor in Michigan. The results show that the BRNN-based method performs better than other current data-driven methods for accurately reconstructing the sensor data.
Publisher
ELSEVIER SCI LTD
Issue Date
2019-10
Language
English
Article Type
Article
Citation

ADVANCED ENGINEERING INFORMATICS, v.42

ISSN
1474-0346
DOI
10.1016/j.aei.2019.100991
URI
http://hdl.handle.net/10203/270831
Appears in Collection
CE-Journal Papers(저널논문)
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