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

翻译：在非接触式齿轮故障诊断问题中，缺乏高效的压缩模型仍是齿轮箱数据无线传输面临的挑战。本文提出一种带变换域层的信号自适应非对称自编码器用于压缩传感器信号。首先，引入新型离散余弦Stockwell变换层以替代多层自编码器中的线性层。通过利用卷积的乘法性质，在DCST域中实现可训练滤波器。应用可训练硬阈值层以减少DCST层中的冗余数据，从而实现特征图稀疏化。与线性层相比，DCST层减少了可训练参数数量并提升了数据重构精度。其次，采用稀疏化DCST层训练自编码器仅需少量数据集。在康涅狄格大学和东南大学齿轮箱数据集上，该方法优于其他基于自编码器的方法：在有限训练样本条件下，平均质量分数最低提升2.00%，最高提升32.35%。