Name
Abstract | ||
|---|---|---|
Bearing fault detection with the aid of the vibration signals is presented. In this paper, time-domain features are extracted to indicate bearing fault, which collected from tri-axial vibration signal. Decision tree is chosen as an effective diagnostic tool to obtain bearing status. The paper also introduces principal component analysis (PCA) algorithm to reduce training data dimension and remove irrelevant data. Both original data and PCA-based data are used to train C4.5 decision tree models. Then, the result of PCA-based decision tree is compared with normal decision tree to get the best performance of classification process. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74205-0_99 | ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence |
Keywords | Field | DocType |
principal component analysis.,bearing diagnosis,pca-based decision tree,irrelevant data,fault detection,decision tree,original data,training data dimension,tri-axial vibration signal,decision tree model,vibration,pca-based data,time-domain features,normal decision tree,time domain,principal component analysis | Time domain,Training set,Decision tree,Data mining,Pattern recognition,Computer science,Bearing (mechanical),Bearing fault detection,Artificial intelligence,Vibration,Machine learning,Principal component analysis | Conference |
Volume | ISSN | Citations |
4682 | 0302-9743 | 3 |
PageRank | References | Authors |
0.87 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong-Hee Lee | 1 | 363 | 42.82 |
| Ngoc-Tu Nguyen | 2 | 4 | 1.23 |
| Jeong-Min Kwon | 3 | 3 | 0.87 |