# Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks

arXiv: https://arxiv.org/abs/2109.12513 · HTML: https://mlsanity.com/paper/2109.12513/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 26 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Remaining Useful Lifetime Prediction via Deep Domain Adaptation](https://mlsanity.com/paper/1907.07480/) (2019)
- [Discriminative feature generation for classification of imbalanced data](https://mlsanity.com/paper/2010.12888/) (2020)

## Built on by (most cited first, 2 of 2)

- [Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries](https://mlsanity.com/paper/2308.03664/) (2023)
- [Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks](https://mlsanity.com/paper/2310.18924/) (2023)

## Abstract (arXiv)

Bearing is a key component in industrial machinery and its failure may lead to unwanted downtime and economic loss. Hence, it is necessary to predict the remaining useful life (RUL) of bearings. Conventional data-driven approaches of RUL prediction require expert domain knowledge for manual feature extraction and may suffer from data distribution discrepancy between training and test data. In this study, we propose a novel generalized multiscale feature extraction method with generative adversarial networks. The adversarial training learns the distribution of training data from different bearings and is introduced for health stage division and RUL prediction. To capture the sequence feature from a one-dimensional vibration signal, we adapt a U-Net architecture that reconstructs features to process them with multiscale layers in the generator of the adversarial network. To validate the proposed method, comprehensive experiments on two rotating machinery datasets have been conducted to predict the RUL. The experimental results show that the proposed feature extraction method can effectively predict the RUL and outperforms the conventional RUL prediction approaches based on deep neural networks. The implementation code is available at https://github.com/opensuh/GMFE.