# A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression

arXiv: https://arxiv.org/abs/2310.02862 · HTML: https://mlsanity.com/paper/2310.02862/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study](https://mlsanity.com/paper/2003.03315/) (2020)
- [A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer](https://mlsanity.com/paper/2305.17510/) (2023)
- [DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer](https://mlsanity.com/paper/2211.08577/) (2022)

## Built on by (most cited first, 3 of 3)

- [MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection](https://mlsanity.com/paper/2410.09103/) (2024)
- [The Blind Normalized Stein Variational Gradient Descent-Based Detection for Intelligent Random Access in Cellular IoT](https://mlsanity.com/paper/2403.18846/) (2024)
- [MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection](https://mlsanity.com/paper/2505.23870/) (2025)

## Abstract (arXiv)

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