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To ensure the proper operation of dam sluice gates, a large number of sensors are deployed for health monitoring. However, due to the harsh operating environment of the sluice gates, data collected by the sensors often experiences missing values, resulting in insufficient and unrepresentative sample data. The Attention-GAN algorithm is proposed for data augmentation of the raw data. Experimental results show that the similarity between the generated samples and the original samples in Attention-GAN is significantly improved compared to the original GAN, with the average cosine similarity on the sluice gate dataset increasing by 9.87%. This demonstrates that the proposed method effectively enhances the acceleration data of the small sample sluice gate.
[1] 罗光宇，杨光明.基于MIV分析的GA-BP神经网络闸门健康诊断 [J].水电能源科学，2021,39(11):203-206,194.
[2] 秦方，夏云秋，胡葆文，等.泄洪闸门在线监测数据挖掘应用的现状分析与讨论 [J].水电站机电技术，2023,46(2):98-100.
[3] REN P,CHEN X Y,SUN L J,et al.Incremental Bayesian matrix/tensor learning for structural monitoring data imputation and response forecasting [J].Mechanical systems and signal processing,2021,158:107734.
[4] ZHANG Y M,WANG H,BAI Y,et al.Bayesian dynamic regression for reconstructing missing data in structural health monitoring [J].Structural health monitoring-an international journal,2022,21(5):2097-2115.
[5] WAN H P,NI Y Q.Bayesian multi-task learning methodology for reconstruction of structural health monitoring data [J].Structural health monitoring,2019,18(4):1282-1309.
[6] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems Montreal,Canada,2014:2672-2680.
[7] 李洁，张震豪，董亚冰，等.基于生成对抗网络的电动汽车电池数据增强和故障诊断 [J].汽车技术，2023(8):1-6.
[8] 李瀚铭，苏金善.基于GAN的小样本数据增强在身份识别中的应用 [J].计算机仿真，2023,40(8):169-173.
[9] 甘岚，沈鸿飞，王瑶，等.基于改进DCGAN的数据增强方法 [J].计算机应用，2021,41(5):1305-1313.
[10] ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning,Sydney,2017:214-223.
[11] SNOW D.MTSS-GAN:Multivariate time series simulation generative adversarial networks[EB/OL].https://dx.doi.org/10.213/ssrn.3616557,2020-06-02.
[12] SAROJA T V,KALPANA Y.Hybrid missing data imputation and novel weight convolution neural network classifier for chronic kidney disease diagnosis [J].Measurement:Sensors,2023,27(S):100715.
Basic Information:
DOI：10.20040/j.cnki.1000-7709.2025.20240455
China Classification Code:TV663
Citation Information:
[1]HUANG Jian-zhang,ZHANG Yu-qi,TIE Ying ,et al.Attention-GAN Based Data Enhancement for Gate Structures[J].Water Resources and Power,2025,43(01):103-107.DOI:10.20040/j.cnki.1000-7709.2025.20240455.
Fund Information:
国家自然科学基金项目(52175153); 国家工信部智能制造综合标准化与新模式应用项目(2018037)
2024-03-12
2024
2024-03-28
2024
1
2024-11-19
2024-11-19
2024-11-19