Machinery condition prediction based on support vector machine model with wavelet transform

被引：0

作者：

Liu, Shu-Jie ^{[1
]}

Lu, Hui-Tian ^{[2
]}

Li, Chao ^{[1
,4
]}

Hu, Ya-Wei ^{[1
]}

Zhang, Hong-Chao ^{[1
,3
]}

机构：

[1] School of Mechanical Engineering, Dalian University of Technology, Dalian, China

[2] Department of Construction & Operations Management, South Dakota State University, Brookings,SD, United States

[3] Department of Industrial Engineering, Texas Tech University, Lubbock,TX, United States

[4] Offshore Oil Engineering Co. Ltd., Tianjin, China

来源：

Journal of Donghua University (English Edition) | 2014年 / 31卷 / 06期

关键词：

Support vector machines - Forecasting - Machinery;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. The vibration signal was modeled from the double row bearing, and wavelet transform and support vector machine model (WT-SVM model) was constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships was applied to describing the degradation trend distribution and a 95% confidence level based on t-distribution was given. The single SVM model and neural network (NN) approach were also investigated as a comparison. Results indicate that the WT-SVM model outperforms the NN and single SVM models, and is feasible and effective in machinery condition prediction. Copyright © 2014 Editorial Department of Journal of Donghua University. All rights reserved.

引用

页码：831 / 834