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Sensor faults classification for SHM systems using deep learning-based method with Tsfresh features
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DOI：10.1088/1361-665X/ab85a6.png)
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Sensors have been installed on many civil infrastructures to monitor structural conditions. False alarm will be triggered, however, by the malfunctioned sensor even under normal conditions. Therefore, pre-processing is needed to identify and classify the sensor fault before structural damage detection and assessment. This paper proposes a deep learning-based method, namely, the Tsfresh Long Short-Term Memory networks (TLSTM), to address the sensor fault classification. The python package Tsfresh is used to extract features that are sensitive to sensor fault from measured signals. These features are further selected with the Benjamini-Yekutieli procedure. With the selected features, a long short-term memory (LSTM) network combining two fully-connected layers and a Softmax layer is constructed to differentiate sensor fault types. Experimental data with five types of sensor faults are obtained by mechanical and electrical simulation. The proposed method is shown able to successfully classify all these sensor fault types.
Keyword:
structural health monitoring
sensor fault
classification
Tsfresh
LSTM
Benjamini-Yekutieli
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