CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity-Reference-Cited by-同舟云学术

CSiamese: a novel semi-supervised anomaly detection framework for gas turbines via reconstruction similarity

Published:2023-04-28 Issue:22 Volume:35 Page:16403-16427
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Liu Dan,Zhong Shisheng^ORCID,Lin Lin,Zhao Minghang,Fu Xuyun,Liu Xueyun

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08507-y.pdf

Reference50 articles.

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2. Yan W, Yu L (2019) On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach. arXiv preprint arXiv:1908.09238

3. Yazdani S, Montazeri-Gh M (2020) A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic. Comput Ind 115:103–131

4. Mousavi M, Chaibakhsh A, Jamali A, Kordestani M, Saif M (2022) A new fault diagnosis approach for heavy-duty gas turbines. IEEE/ASME Trans Mechatron

5. Asgari H, Chen X, Sainudiin R (2013) Modelling and simulation of gas turbines. Int J Model Ident Control 20(3):253–270

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