期刊
NONLINEAR DYNAMICS
卷 106, 期 3, 页码 2107-2130出版社
SPRINGER
DOI: 10.1007/s11071-021-06827-z
关键词
Fault diagnosis; Rotating machine; Multi-scale entropy; Isometric mapping; Support vector machine
资金
- National Natural Science Foundation of China [51775114, 51875105, 51275092]
- Fujian Provincial Industrial Robot Basic Components Technology Research and Development Center [2014H21010011]
- Natural Science Foundation of Anhui Province [1808085ME152]
The proposed method in this study utilizes nonlinear dynamics and manifold learning techniques to extract features for fault diagnosis of rotating machinery components, with the support vector machine for intelligent fault recognition. Experimental results show the method's accuracy in identifying various faults in bearings and gears, with higher recognition accuracy compared to other methods.
Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used rotating machinery fault diagnosis methods are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel nonlinear dynamics technique named the multivariate generalized refined composite multi-scale sample entropy was presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping was introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine was applied to carry out the intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various faults in both the bearings and gears. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it had higher recognition accuracy compared to other multi-channel or single-channel methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据