期刊
APPLIED SCIENCES-BASEL
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/app11104602
关键词
digital twin; Kalman filter; high-order variable structure technique; support vector algorithm; adaptive neural-fuzzy approach; bearing anomaly detection; crack size identification nonstationary; rotating machinery
类别
资金
- University of Ulsan
This study utilized an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification, achieving an average accuracy of 99.5% and 99.6%, respectively.
Featured Application Bearing anomaly recognition using an intelligent digital twin integrated with machine learning. In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.
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