自编码器及其改进算法在滚动轴承故障诊断的应用

Application of Auto-Encoder and Its Improvement in Rolling Bearing Fault Diagnosis

自编码器作为神经网络中典型的无监督学习模型,在数据降噪和数据可视化降维方面具有明显的优势,且在各应用领域都引起了普遍重视,在滚动轴承故障诊断中的应用也日渐增加。为了及时了解并掌握自编码器及其改进算法在滚动轴承方面的应用,对近年具有代表性的自编码器相关算法进行了分类和总结。首先,阐述了自编码器的原理和几种基于其改进的自编码器方法的理论简述,并分析了这些算法的改进目的与改进方式。然后,列举了上述算法在滚动轴承故障诊断领域的应用。最后,总结了当前自编码器及其改进算法存在的问题,分析了解决问题的思路。

As a typical unsupervised learning model in neural networks,the self-encoder has attracted widespread attention in various areas,and its application in rolling bearing fault diagnosis is increasing with obvious advantages in data noise reduction and data visualization dimension reduction.In order to timely understand and master the application of auto-encoder and its improved algorithm in rolling bearing,this paper classifies and summarizes the representative auto-encoder related algorithms in recent years.Firstly,the principle of self-encoder and the theoretical sketch of several self-encoder methods based on its improvement are described,and the improvement purpose and improvement of these algorithms are analyzed.Then,the applications of these algorithms in the field of rolling bearing fault diagnosis are listed.Finally,the problems of present-day self-encoders and their improved algorithms are summarized,and the ideas for solving them are analyzed.