Home Journals Articles Updates About
CN
image
收藏切换
Attention Based Mechanism and Multi-sensor Information Driven Fault Prediction System for Hydro-generating Units
收藏切换
PDF
Gang TAN1, Lei-hao DU1, Bian HU2, Zhi-cheng HE1
Water Resources and Power | 2023, 41(3) : 195 - 197
Less
收藏切换
Water Resources and Power | 2023, 41(3): 195-197
• ELECTROMECHANICS AND CONTROL ENGINEERING •
Attention Based Mechanism and Multi-sensor Information Driven Fault Prediction System for Hydro-generating Units
Full
Gang TAN1, Lei-hao DU1, Bian HU2, Zhi-cheng HE1
Affiliations
  • 1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
  • 2.Hunan Wuling Power Technology Co., Ltd., Changsha 410004, China
Published: 2023-03-25 doi: 10.20040/j.cnki.1000-7709.2023.20221136
Outline
收藏切换

To meet the fault prediction needs of hydro-generator units in the context of big data, combining the characteristics of the attention mechanism with good feature extraction ability and the advantages of model robustness for multi-sensor information driving, this paper proposed a fault prediction system of hydro-generating units based on the attention mechanism and multi-sensor information driving. The system was applied to on-line monitoring of unit #8 in August for a hydropower station in Hunan Province. The actual operation results show that the system can effectively predict the vibration trend of the hydro-generator set and realize the intelligent prediction of the hydro-generator.

hydro-generators  /  deep learning  /  multi-sensor drive  /  attention mechanism  /  fault prediction
Gang TAN, Lei-hao DU, Bian HU, Zhi-cheng HE. Attention Based Mechanism and Multi-sensor Information Driven Fault Prediction System for Hydro-generating Units[J]. Water Resources and Power, 2023 , 41 (3) : 195 -197 . DOI: 10.20040/j.cnki.1000-7709.2023.20221136
Appendix
Less
Year 2023 volume 41 Issue 3
PDF
126
36
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221136
  • Receive Date：2022-04-27
  • Online Date：2026-01-28
  • Published：2023-03-25
Article Data
Affiliations
History
  • Received：2022-04-27
  • Revised：2022-06-12
Funding
Affiliations
    1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
    2.Hunan Wuling Power Technology Co., Ltd., Changsha 410004, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221136
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House