鉴别器
规范化(社会学)
计算机科学
分类器(UML)
人工智能
模式识别(心理学)
剪裁(形态学)
断层(地质)
模式(计算机接口)
算法
探测器
社会学
人类学
电信
语言学
哲学
地震学
地质学
操作系统
作者
Wei Li,Xiang Zhong,Haidong Shao,Baoping Cai,Xingkai Yang
标识
DOI:10.1016/j.aei.2022.101552
摘要
As one of the representative unsupervised data augmentation methods, generative adversarial networks (GANs) have the potential to solve the problem of insufficient samples in fault diagnosis of rotating machinery. However, the existing unsupervised GANs are usually incapable of simultaneously generating multi-mode fault samples and have some shortcomings such as mode collapse and gradient vanishing. To overcome these deficiencies, a supervised model called modified auxiliary classifier GAN (MACGAN) designed with new framework is proposed in this paper. Firstly, a new ACGAN framework is developed by adding an independent classifier to improve the compatibility between the classification and discrimination. Secondly, the Wasserstein distance is introduced in the new loss functions to overcome mode collapse and gradient vanishing. Finally, to achieve stable training, a spectral normalization is used to replace the weight clipping to constrain the weight parameters of discriminator. The proposed method is applied to fault diagnosis of bearing and gear. Compared with the existing GANs, the proposed method can more efficiently generate multi-mode fault samples with higher qualities, which can be used to assist the training of deep learning-based fault diagnosis models with high accuracy and good stability.
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