A novel asymmetrical autoencoder with a sparsifying discrete cosine
  Stockwell transform layer for gearbox sensor data compression
Xin Zhu Daoguang Yang Hongyi Pan Hamid Reza Karimi Didem Ozevin Ahmet Enis Cetin
The lack of an efficient compression model remains a challenge for the
wireless transmission of gearbox data in non-contact gear fault diagnosis
problems. In this paper, we present a signal-adaptive asymmetrical autoencoder
with a transform domain layer to compress sensor signals. First, a new discrete
cosine Stockwell transform (DCST) layer is introduced to replace linear layers
in a multi-layer autoencoder. A trainable filter is implemented in the DCST
domain by utilizing the multiplication property of the convolution. A trainable
hard-thresholding layer is applied to reduce redundant data in the DCST layer
to make the feature map sparse. In comparison to the linear layer, the DCST
layer reduces the number of trainable parameters and improves the accuracy of
data reconstruction. Second, training the autoencoder with a sparsifying DCST
layer only requires a small number of datasets. The proposed method is superior
to other autoencoder-based methods on the University of Connecticut (UoC) and
Southeast University (SEU) gearbox datasets, as the average quality score is
improved by 2.00% at the lowest and 32.35% at the highest with a limited number
of training samples