Intelligent Small Sample Defect Detection of Water Walls in Power Plants Using Novel Deep Learning I

Thermal power generation is one of the main forms of electricity generation in the world, and the share of thermal power generation in total electricity generation has long been maintained at over 80% in 2018. However, power plants are often shut down due to boiler accidents, which are mostly caused by water wall damage. At present, the detection method for water wall defects is still in the stage of manual detection, which has a high risk coefficient, long time-frame, and low efficiency. In this article, a deep learning method integrating deep convolutional generating adversarial networks (DCGAN) and a seam carving algorithm to solve the problem of small sample defect detection is proposed. The proposed method uses the seam carving algorithm to solve the overfitting of the DCGAN, for which the DCGAN generates high-quality images. Then, the intelligent small sample defect detection model is built by convolutional neural networks. Finally, the proposed method is used in the defect detection of water walls in the actual thermal power generation plant. To evaluate the performance of our proposed method, we conduct comparison experiments among different GANs and different detection networks integrating different processes used and not used the proposed data expansion method. The experimental results demonstrate that the proposed method can achieve a detection accuracy of 98.43%, which is higher than other methods, and has the best generalization ability.