Tusongjiang. Kari1, Lin Du1, Aisikaer. Rouzi2, Xiaojing Ma1,*, Zhichao Liu1, Bo Li1
CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954
Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >
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