期刊
MEASUREMENT
卷 159, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2020.107768
关键词
Fault diagnosis; Rotating machinery; Wavelet transform; Generative adversarial nets; Convolutional neural net
资金
- Foundation of the National Key Research and Development Plan of China [2018YFB1701402]
The fault detection of rotating machinery systems especially its typical components such as bearings and gears is of special importance for maintaining machine systems working normally and safely. However, due to the change of working conditions, the disturbance of environment noise, the weakness of early features and various unseen compound failure modes, it is quite hard to achieve high-accuracy intelligent failure monitoring task of rotating machinery using existing intelligent fault diagnosis approaches in real industrial applications. In the paper, a novel and high-accuracy fault detection approach named WT-GAN-CNN for rotating machinery is presented based on Wavelet Transform (WT), Generative Adversarial Nets (GANs) and convolutional neural network (CNN). The proposed WT-GAN-CNN approach includes three parts. To begin with, WT is employed for extracting time-frequency image features from one-dimension raw time domain signals. Secondly, GANs are used to generate more training image samples. Finally, the built CNN model is used to accomplish the fault detection of rotating machinery by the original training time-frequency images and the generated fake training time-frequency images. Two experiment studies are implemented to assess the effectiveness of our proposed approach and the results demonstrate it is higher in testing accuracy than other intelligent failure detection approaches in the literatures even in the interference of strong environment noise or when working conditions are changed. Furthermore, its result in the stability of testing accuracy is also quite excellent. (C) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据