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- W3086184207 abstract "The failure of thermal system will cause huge economic loss and waste of resources in a large-scale power plant, so the fault diagnosis of thermal system is of great significance. However, in practical applications, it is difficult to obtain sufficient labeled fault data to build diagnostic models under variable operating conditions. To utilize data obtained under different loading conditions to improve classification accuracy, this paper proposes a new convolutional domain adaptation network for fault diagnosis of thermal system in the power plant. In the proposed method, one-dimensional convolutional neural network is employed to extract discriminative features from raw operating data and Wasserstein distance is used to minimize the distribution gap in the feature space to learn domain-invariant features. Experiments are performed under different loading conditions on a 600-MW coal-fired power unit and the results validate the effectiveness of the proposed method." @default.
- W3086184207 created "2020-09-21" @default.
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- W3086184207 date "2020-07-01" @default.
- W3086184207 modified "2023-10-17" @default.
- W3086184207 title "Convolutional Domain Adaptation Network for Fault Diagnosis of Thermal System under Different Loading Conditions" @default.
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- W3086184207 doi "https://doi.org/10.23919/ccc50068.2020.9189493" @default.
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