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A rub fault recognition method based on generative adversarial nets
Journal of Mechanical Science and Technology ( IF 1.6 ) Pub Date : 2020-04-11 , DOI: 10.1007/s12206-020-0302-5
Wei Wang , Weidong Liu , Jing Li , Wei Peng

Faced with the problem of valid data shortage data in practical. There's not enough data to train classifiers which can be satisfied to detect impact-rubbing faults in rotary machine. Bedsides, the large number of noises in working enviroment make the useful signal contaminated. Based on this problem, this paper proposes a rubbing fault recognition method based on a generative adversarial nets named deep convolution generative adversarial nets (DCGAN), which is based on a deep convolutional network frame with generation and discrimination models. The acquired signal is processed by time frequency analysis further to get spectrogram. The DCGAN can perform feature conversion and map it to the potential feature subspace to obtain more robust features. The results illustrate that the proposed method can achieve a much more excellent recognition effect. Thus, the proposed DCGAN model is an effective way to recognize impact-rubbing fault in the practical.



中文翻译:

基于生成对抗网络的摩擦故障识别方法

在实际中面临有效数据短缺的问题。没有足够的数据来训练分类器,而这些分类器不能满足检测旋转机器中的碰磨故障的需要。在床头,工作环境中的大量噪音使有用信号受到污染。针对这一问题,本文提出了一种基于深度对抗卷积生成对抗模型的深度对抗卷积生成对抗网(DCGAN)的基于对抗对抗网的摩擦故障识别方法。所采集的信号通过时频分析进一步处理以获得频谱图。DCGAN可以执行特征转换并将其映射到潜在的特征子空间以获得更强大的特征。实验结果表明,该方法可以取得更好的识别效果。因此,所提出的DCGAN模型是实际识别冲击摩擦故障的有效方法。

更新日期:2020-04-11
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