信号分辨率增强的机械智能故障诊断方法研究

Intelligent fault diagnosis method for signal resolution enhancement

利用深度学习来增强数据集已成为各个领域的研究热点,即使用有限的数据集生成更多仿真的数据集。不同于目前主流的生成对抗网络算法及其变体算法,基于样本分辨率增强的思想,提出了一种简单有效的算法——高效亚像素全连接神经网络(ESPFCN)。ESPFCN的原理为:对原始输入样本进行全连接操作,经过隐层特征映射输出四通道的低分辨率特征;通过亚像素全连接层,将四通道的低分辨率特征进行周期性的排列,得到一组高分辨率特征,实现了样本分辨率的增强。设置了一组特殊的轴承实验来评估生成模型的性能,实验结果验证了ESPFCN框架的有效性,并通过可视化展示了ESPFCN的特征学习过程。

Using deep learning to enhance dataset has become a hot topic in various fields.That is,using limited dataset to generate more simulated dataset.Different from the current mainstream generative adversarial network(GAN)and its variant algorithms,based on the idea of sample resolution enhancement,this paper proposes a simple and effective algorithm—an efficient sub-pixel fully connected neural network(ESPFCN).The principle of ESPFCN is:it performs fully-connected operation on the raw input samples and the results of four-channel multi-feature maps are output;through the fully connected sub-pixel layer,the low resolution features of the four channels are arranged periodically,and a set of high resolution features is obtained,which enhances the sample resolution.Finally,a set of special bearing experiment is set up to evaluate the performance of the generated model.The experimental results verify the effectiveness of the ESPFCN framework,and the feature learning process of ESPFCN is visualized.