Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder. Wang, Y., Yang, H., Yuan, X., Shardt, Y. A. W., Yang, C., & Gui, W. Journal of Process Control, 92:79–89, August, 2020. ![Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Stacked auto-encoder (SAE)-based deep learning has been introduced for fault classification in recent years, which has the potential to extract deep abstract features from the raw input data. However, SAE cannot ensure the relevance of deep features with the fault types due to its unsupervised self-reconstruction in the pretraining stage. To overcome this problem, a stacked supervised auto-encoder is proposed to pretrain the deep network and obtain deep fault-relevant features from raw input data. In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. By stacking multiple supervised auto-encoders hierarchically, high-level fault-relevant features are gradually learned from raw input data, which can improve the classification accuracy of the classifiers. The proposed SSAE is tested on the Tennessee–Eastman (TE) benchmark process and a real industrial hydrocracking process. The results show the effectiveness and flexibility of SSAE.
@article{wang_deep_2020,
title = {Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder},
volume = {92},
issn = {0959-1524},
url = {https://www.sciencedirect.com/science/article/pii/S0959152420302225},
doi = {10.1016/j.jprocont.2020.05.015},
abstract = {Stacked auto-encoder (SAE)-based deep learning has been introduced for fault classification in recent years, which has the potential to extract deep abstract features from the raw input data. However, SAE cannot ensure the relevance of deep features with the fault types due to its unsupervised self-reconstruction in the pretraining stage. To overcome this problem, a stacked supervised auto-encoder is proposed to pretrain the deep network and obtain deep fault-relevant features from raw input data. In each supervised auto-encoder, informative features are learned from the input data with the goal that they can largely distinguish different fault types. By stacking multiple supervised auto-encoders hierarchically, high-level fault-relevant features are gradually learned from raw input data, which can improve the classification accuracy of the classifiers. The proposed SSAE is tested on the Tennessee–Eastman (TE) benchmark process and a real industrial hydrocracking process. The results show the effectiveness and flexibility of SSAE.},
language = {en},
urldate = {2022-05-02},
journal = {Journal of Process Control},
author = {Wang, Yalin and Yang, Haibing and Yuan, Xiaofeng and Shardt, Yuri A. W. and Yang, Chunhua and Gui, Weihua},
month = aug,
year = {2020},
keywords = {Deep learning, Fault classification, Process monitoring, Stacked auto-encoder (SAE), Tennessee–Eastman process},
pages = {79--89},
}
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