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Journals >Chinese Journal of Ship Research >Volume 17 >Issue 6 >Page 96 > Article

Chinese Journal of Ship Research
Vol. 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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DOI: 10.19693/j.issn.1673-3185.02616 Cite this Article

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 Copy Citation Text

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References

[4] Z H ZHANG, T JIANG, S H LI et al. Automated feature learning for nonlinear process monitoring – an approach using stacked denoising autoencoder and k-nearest neighbor rule. Journal of Process Control, 64, 49-61(2018).

[5] C HE, D C GE, M H YANG et al. A data-driven adaptive fault diagnosis methodology for nuclear power sys- tems based on NSGAII-CNN. Annals of Nuclear Energy, 159, 108326(2021).

[6] W K YU, C H ZHAO. Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability. IEEE Transactions on Industrial Electronics, 67, 5081-5091(2020).

[7] K ZHANG, B P TANG, L DENG et al. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. Measurement, 179, 109491(2021).

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

[10] R P NIKULA, M RUUSUNEN, K LEIVISKÄ. Data-driven framework for boiler performance monitoring. Applied Energy, 183, 1374-1388(2016).

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

[12] OROZCO D J RÚA, O J VENTURINI, PALACIO J C ESCOBAR 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). Energy, 123, 20-35(2017).

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

[16] S M CHEN, J B YU, S J WANG. One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes. Journal of Process Control, 87, 54-67(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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