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Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors

: Kraljevski, Ivan; Duckhorn, Frank; Tschöpe, Constanze; Wolff, Matthias


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Sensors 2020. Conference Proceedings : Virtual Conference, October 25 - 28, 2020, Rotterdam, The Netherlands
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6801-2
ISBN: 978-1-7281-6802-9
4 S.
Sensors Conference <19, 2020, Online>
Fraunhofer IKTS ()
convolutional autoencoder; health indicator; piezoelectric sensors; microfluidic valves; valves; sensors; Hidden Markov Models; DV-Training; feature extraction; degradation; convolution

We present a method for health indicator extraction from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful time prediction.