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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE International Symposium on Circuits and Systems proceedings s. 1 - 5
Hlavní autori: Zhu, Xin, Cetin, Ahmet Enis
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 25.05.2025
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 convolutional layer followed by a wavelet filterbank layer. Specifically, a dual-channel convolutional block with diverse convolutional kernel sizes and varying processing depths is integrated into the wavelet filterbank layer to enable comprehensive feature extraction from the wavelet domain. Additionally, the adaptive hard-thresholding nonlinearity is applied to remove redundant components while denoising the primary wavelet coefficients. On the decoder side, inverse LWT, along with multiple linear layers and activation functions, is employed to reconstruct the original signals. Furthermore, to enhance compression efficiency, a sparsity constraint is introduced during training to impose sparsity on the latent representations. The experimental results demonstrate that the proposed approach achieves superior data compression performance compared to state-of-the-art methods.
ISSN:2158-1525
DOI:10.1109/ISCAS56072.2025.11043949