计算机科学
卷积神经网络
生成对抗网络
人工智能
深度学习
对抗制
模式识别(心理学)
断层(地质)
生成语法
机器学习
地质学
地震学
作者
Yongyi Sun,Tingting Zhao,Zhihui Zou,Yinsheng Chen,Hongquan Zhang
摘要
The fault diagnosis of hydrogen sensors is of great significance. However, it is difficult to collect data samples for some modes of hydrogen sensor signals, so the data samples may be unbalanced, which can seriously affect the fault diagnosis results. In this paper, we present a novel convolutional neural network (CNN)-based deep convolutional generative adversarial network (DCG) method (DCG–CNN) for gas sensor fault diagnosis. First, we transform the 1D fault signals of the gas sensor into 2D gray images for end-to-end conversion with no signal data information loss. Second, we use the DCG to enrich the 2D gray images of small fault data samples, which results in balanced sensor fault datasets. Third, we use the CNN method to improve the accuracy of fault diagnosis. In order to understand the internal mechanism of the CNN, we further visualize the learned feature maps of fault data samples in each layer of the CNN and try to analyze the reasons for the method’s high performance. The fault diagnosis accuracy of the DCG–CNN is shown to be higher than that of other traditional methods.
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