对抗制
计算机科学
断层(地质)
生成对抗网络
生成语法
方位(导航)
人工智能
模式识别(心理学)
数据挖掘
机器学习
地质学
深度学习
地震学
作者
Zhaohui Qin,Faguo Huang,Jiafang Pan,Junlin Niu,Haihua Qin
出处
期刊:Symmetry
[Multidisciplinary Digital Publishing Institute]
日期:2024-03-15
卷期号:16 (3): 358-358
被引量:2
摘要
Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for cases with a small number of data and data imbalances (S&I). We present an innovative solution to overcome this problem, which is composed of two components: data augmentation and fault diagnosis. In the data augmentation section, the S&I dataset is supplemented with a deep convolutional generative adversarial network based on a gradient penalty and Wasserstein distance (WDCGAN-GP), which solve the problems of the generative adversarial network (GAN) being prone to model collapse and the gradient vanishing during the training time. The addition of self-attention allows for a better identification and generation of sample features. Finally, the addition of spectral normalization can stabilize the training of the model. In the fault diagnosis section, fault diagnosis is performed through a convolutional neural network with coordinate attention (CNN-CA). Our experiments conducted on two bearing fault datasets for comparison demonstrate that the proposed method surpasses other comparative approaches in terms of the quality of data augmentation and the accuracy of fault diagnosis. It effectively addresses S&I fault diagnosis challenges.
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