@inproceedings{c2933db647124326bf9a8e39561533bb,
title = "Fault diagnosis with feature representation based on stacked sparse auto encoder",
abstract = "A deep learning method for fault diagnosis is proposed in this paper. The stacked sparse auto encoder(SSAE) model with the theory of deep learning extracts deep feature representation from original fault data. Compared with traditional methods, SSAE is more efficient because of its deep architecture. The feature representation is used by a softmax classifier for fault detection and classification. The proposed method is experimented on Tennessee Eastman Process(TEP), a chemical industrial process benchmark, to demonstrate its practicality and effectiveness.",
keywords = "Deep learning, Fault diagnosis, Feature representation, Stacked sparse auto encoder",
author = "Zheng Zhang and Xuemei Ren and Hengxing Lv",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018 ; Conference date: 18-05-2018 Through 20-05-2018",
year = "2018",
month = jul,
day = "6",
doi = "10.1109/YAC.2018.8406476",
language = "English",
series = "Proceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "776--781",
booktitle = "Proceedings - 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2018",
address = "United States",
}