计算机科学
判别式
规范化(社会学)
分类器(UML)
人工智能
模式识别(心理学)
对抗制
生成语法
生成对抗网络
数据挖掘
机器学习
深度学习
社会学
人类学
作者
Wenlong Fu,Yupeng Chen,Moxian Song,Xiaoyue Chen,Baojia Chen
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:23 (23): 29119-29130
被引量:1
标识
DOI:10.1109/jsen.2023.3322040
摘要
Mechanical equipment usually runs under normal condition (NC), making it prohibitively challenging to collect sufficient fault samples and the dataset is prone to imbalanced characteristics, which severely limits the performance of intelligent fault diagnosis methods. In view of this, a conditional Wasserstein generative adversarial network with switchable normalization (SN-CWGAN) is proposed. First, self-attention mechanism and dense convolutional network (DenseNet) are integrated into SN-CWGAN to enhance the transmission of key features, so as to obtain more discriminative feature information. Simultaneously, switchable normalization is performed within discriminators to increase the generalization capability of the SN-CWGAN model. Then, a two time-scale update rule (TTUR) is applied to improve the convergence speed and stability of the model during training. Accordingly, the SN-CWGAN model can generate high-quality fault samples to balance the dataset. Finally, the AlexNet classifier is trained on the balanced dataset to realize fault diagnosis. The effectiveness of the proposed method is validated by two case studies. The diagnostic results and comparative experiments indicate that the proposed method achieves significant improvements in diagnostic accuracy and stability.
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