Efficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer

Bearing data compression is vital to manage the large volumes of data generated during condition monitoring. In this paper, a novel asymmetrical autoencoder with a lifting wavelet transform (LWT) layer is developed to compress bearing sensor data. The encoder part of the network consists of a convol...

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Veröffentlicht in:	IEEE International Symposium on Circuits and Systems proceedings S. 1 - 5
Hauptverfasser:	Zhu, Xin, Cetin, Ahmet Enis
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 25.05.2025
Schlagworte:	Adaptation models adaptive hard-thresholding nonlinearity Asymmetrical autoencoder Autoencoders bearing sensor data compression Convolution Data compression Data models Decoding Filter banks lifting wavelet transform Training Wavelet coefficients Wavelet domain
ISSN:	2158-1525
Online-Zugang:	Volltext
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