Researching | Fault diagnosis of steam power system based on convolutional neural network

Chinese Journal of Ship Research, Volume. 17, Issue 6, 96(2022)

Fault diagnosis of steam power system based on convolutional neural network

Jian SU¹, Hanjiang SONG², Fuyuan SONG¹, and Guolei ZHANG¹

Author Affiliations

¹College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China

²The 92942 Unit of PLA, Beijing 100161, China

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References(10)

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[8] YANG D S, PANG Y H, ZHOU B W et al. Fault diagnosis for energy internet using correlation processing-based convolutional neural networks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49, 1739-1748(2019).

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[11] WANG H, PENG M J, YU Y et al. Fault identification and diagnosis based on KPCA and similarity cluster-ing for nuclear power plants[J]. Annals of Nuclear Energy, 150, 107786(2021).

[12] RÚA OROZCO D J, VENTURINI O J, ESCOBAR PALACIO J C et al. A new methodology of thermodynamic diagnosis, using the thermoeconomic method together with an artificial neural network (ANN): a case study of an externally fired gas turbine (EFGT)[J]. Energy, 123, 20-35(2017).

[15] KONG Z Q, TANG B P, DENG L et al. Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units[J]. Renewable Energy, 146, 760-768(2020).

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Jian SU, Hanjiang SONG, Fuyuan SONG, Guolei ZHANG. Fault diagnosis of steam power system based on convolutional neural network[J]. Chinese Journal of Ship Research, 2022, 17(6): 96

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Paper Information

Category: Intelligent Engine Room, Intelligent Energy Efficiency and Intelligent Platform

Received: Nov. 24, 2021

Accepted: --

Published Online: Mar. 26, 2025

The Author Email:

DOI:10.19693/j.issn.1673-3185.02616

Topics

laser devices and laser physics

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laser manufacturing

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