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Degradation Assessment of Shaft System of Hydropower Unit Based on Instantaneous Orbit Feature Image and Conditional Generative Adversarial Network
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Yi-guo WANG1, Hui CHANG2, Dong-jun CHEN3, Yi-fan WU4, Chao-shun LI4
Water Resources and Power | 2023, 41(6) : 166 - 170
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Water Resources and Power | 2023, 41(6): 166-170
• ELECTROMECHANICS AND CONTROL ENGINEERING •
Degradation Assessment of Shaft System of Hydropower Unit Based on Instantaneous Orbit Feature Image and Conditional Generative Adversarial Network
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Yi-guo WANG1, Hui CHANG2, Dong-jun CHEN3, Yi-fan WU4, Chao-shun LI4
Affiliations
  • 1.Guangdong Energy Group Co., Ltd., Guangzhou 510630, China
  • 2.Tianshengqiao First-class Hydropower Development Co., Ltd., Xingyi 562400, China
  • 3.Guangdong Yudean Changhu Power Generation Co., Ltd., Yingde 513021, China
  • 4.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2023-06-25 doi: 10.20040/j.cnki.1000-7709.2023.20221191
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The shaft system of hydropower unit has a significant impact on the stability of the unit. The degradation assessment of the shaft system can visually reflect the operating condition of the unit. This paper presents a method for assessing the degradation of the unit’s shaft system using instantaneous orbit feature image and conditional adversarial generative network (CGAN). Firstly, the vertical signals of each bearing were constructed as a complex signal, and the multivariate complex variational mode decomposition (MCVMD) method was used to process the signal and extract the instantaneous orbit features to construct the instantaneous orbit feature images. CGAN was used to construct the health model. The health model can fit the distribution of feature images in different operating conditions in healthy state and thus output health feature images. The healthy indicator was constructed using the differences between real and healthy images. The genetic algorithm was used to optimize the weights of multiple bearings in order to reduce the volatility of the comprehensive degradation curve in the healthy zone. The proposed method was tested on the unit's shaft system data and its validity has been proved.

hydropower unit  /  degradation assessment  /  instantaneous orbit feature  /  conditional generative adversarial network  /  genetic algorithm
Yi-guo WANG, Hui CHANG, Dong-jun CHEN, Yi-fan WU, Chao-shun LI. Degradation Assessment of Shaft System of Hydropower Unit Based on Instantaneous Orbit Feature Image and Conditional Generative Adversarial Network[J]. Water Resources and Power, 2023 , 41 (6) : 166 -170 . DOI: 10.20040/j.cnki.1000-7709.2023.20221191
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221191
  • Receive Date：2022-06-05
  • Online Date：2026-01-28
  • Published：2023-06-25
Article Data
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History
  • Received：2022-06-05
  • Revised：2022-08-29
Funding
Affiliations
    1.Guangdong Energy Group Co., Ltd., Guangzhou 510630, China
    2.Tianshengqiao First-class Hydropower Development Co., Ltd., Xingyi 562400, China
    3.Guangdong Yudean Changhu Power Generation Co., Ltd., Yingde 513021, China
    4.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221191
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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
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