@inproceedings{30ab90b9f5e14d8d84cd5de625c089a5,
title = "An Imbalanced Fault Diagnosis Method Based on Residual Wasserstein Generative Adversarial Network",
abstract = "In actual industrial processes, operations generally remain in a normal state for an extended period, with faults occurring less frequently. Since the amount of data representing faults is small, there is an imbalance between normal samples and fault samples, which affects the accuracy of fault diagnosis. To solve this problem, this paper proposes an imbalanced fault diagnosis method based on the Residual Wasserstein Generative Adversarial Network(ResWGAN). Firstly, shortcut connections are added to the hidden layers of both the generator and the discriminator to form a residual network, replacing the fully connected layers. Secondly, ResWGAN is trained to generate virtual fault samples and construct a balanced data set. Thirdly, a one-dimensional convolutional neural network (1D CNN) is used to build a classifier to achieve fault diagnosis. Finally, the experimental results on the Tennessee Eastman process show that the proposed method has a high diagnostic accuracy.",
keywords = "Fault Diagnosis, Imbalanced Data, Residual Network, Tennessee Eastman Process, Wasserstein Generative Adversarial Networks",
author = "Tang, \{Li Li\} and Yuan Xu and Wei Ke and Zhang, \{Ming Qing\} and Ji, \{Chong Xing\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025 ; Conference date: 09-05-2025 Through 11-05-2025",
year = "2025",
doi = "10.1109/DDCLS66240.2025.11065672",
language = "English",
series = "Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "80--85",
editor = "Mingxuan Sun and Ronghu Chi",
booktitle = "Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025",
address = "United States",
}