Journal
2020 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL, AUTOMATION AND MECHANICAL ENGINEERING
Volume 1626, Issue -, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1742-6596/1626/1/012152
Keywords
WGAN; Water wall; Convolutional neural network; Defect detection; Thermal power generation
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Funding
- Science and Technology Project of China Energy Group [GJNY-1982]
- Fundamental Research Funds for the Central Universities [XK1802-4]
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This paper proposes an improved water wall defect detection method using Wasserstein generation adversarial network (WGAN). The method aims to improve the problems of poor safety and high level of maintenance personnel required by traditional inspection methods, and improve the accuracy and safety of water wall defect detection through automated inspection. The WGAN is used to expand the water wall defect data. Then the extended data set and the original data set are loaded into a convolutional neural network for training, respectively. The results show that the accuracy of the expanded data set is significantly improved, which can achieve the industrial requirement of the thermal power generation.
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