基于深度学习的旋转机械故障诊断研究综述

Review on Fault Diagnosis of Rotating Machinery Based on Deep Learning

摘要:

在现代化生产中,旋转机械的精密性和重要性越来越高,朝着大型、高速和自动化方向发展,以至传统故障诊断方法不足以处理海量、多源、高维的测量数据,不能满足安全性和可靠性的要求；因此,首先简要介绍几种典型的深度学习模型,并结合深度学习强大的特征提取能力和聚类分析的优势,对其近些年来在转子系统、齿轮箱和滚动轴承故障诊断的应用情况进行了对比分析；最后总结深度学习模型的优缺点,并从工程实际出发对旋转机械的故障诊断方法进行总结与展望。

Abstract:

In modern production, the precision and the importance of rotating machinery is higher and higher, in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability; Therefore, several typical deep learning models are briefly introduced at first, and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized, and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.