{"id":9320,"date":"2026-07-25T19:40:59","date_gmt":"2026-07-25T11:40:59","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9320"},"modified":"2026-07-25T22:25:30","modified_gmt":"2026-07-25T14:25:30","slug":"jase-202610-33-055","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-055","title":{"rendered":"Power Equipment Fault Diagnosis in Small-Sample Scenarios Using GAN-CNNc"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Lei Ye<\/i><\/a>, Xuechao Zhang, Yang Liu, Chenyun Ji, and Guangchun Yin<\/p>\n\n\n\n

Chizhou Power Supply Company, State Grid Anhui Electric Power Co., Ltd, 247100, China<\/p>\n<\/div>\n\n\n\n

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Received: June 10, 2026
Accepted: July 2, 2026
Publication Date: July 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Overall workflow of the proposed GAN-CNN power  equipment fault  diagnosis  method <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.055<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This study proposes a small-sample fault diagnosis method integrating an optimized generative adversarial network (GAN) and lightweight convolutional neural network (CNN). The core innovations include: (1) A weighted adversarial loss function is designed to balance the training of real and synthetic fault samples, solving the data imbalance problem of rare faults; (2) A GAN-CNN hierarchical feature fusion framework is constructed, which uses GAN for highfidelity fault sample augmentation and CNN for adaptive spatial feature extraction of monitoring signals, overcoming the limitation of high-dimensional feature extraction in traditional GAN models. Three groups of simulation experiments and public benchmark dataset (CWRU, MFPT) validation are designed to verify the performance of the proposed method. Experimental results show that: for rare fault scenarios, the proposed method achieves 93.5% diagnostic accuracy, which is 8.5 percentage points higher than traditional methods; on the CWRU bearing fault benchmark dataset, the method achieves 99.12% overall accuracy under 10% small sample ratio, outperforming 5 state-of-the-art (SOTA) fault diagnosis methods; meanwhile, the method reduces the diagnostic inference latency to 180 ms , with significant improvements in anti-interference, precision, recall and F1-score. This method can be coupled with adaptive control and fault-tolerant control loops of power systems, providing technical support for condition-aware operation control and predictive maintenance of power equipment.<\/p>\n\n\n\n

Keywords: Generative adversarial network (GAN), Power equipment fault diagnosis, Small sample learning, Data augmentation, Fault-tolerant control<\/em><\/p>\n\n\n\n

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