基于平移不变性稀疏编码的滚动轴承故障诊断

摘要

Rolling bearing plays a vital role in industrial systems, in which unexpected mechanical faults during operation can lead to severe consequences. For prevention, in this paper, we introduce a new feature extraction technique named Shift-invariant sparse coding for machinery fault diagnosis, and use Shift-invariant sparse coding analyzes the vibration signal. A classiifer is used to verify the extracted sparse features whether it is good enough. Experiments show that the total classiifcation accuracy can be improved, and Shift-invariant sparse coding is an effective feature extraction method for machinery fault diagnosis.%滚动轴承在工业领域中扮演着重要的角色，为了预防故障发生，文章引入了平移不变性稀疏编码作为一种特征提取技术用于滚动轴承故障诊断，利用平移不变性稀疏编码对振动信号进行分析，采用分类器用于测试所提取的稀疏特征。实验表明，该方案达到了较高的分类精度，是针对滚动轴承故障诊断的一种有效的特征提取方法。