Name
Abstract | ||
|---|---|---|
A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S1469026809002631 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Wavelet packet transform, directed acyclic graph support vector machine, fault diagnosis, multi-class | Pattern recognition,Computer science,Support vector machine,Speech recognition,Bearing (mechanical),Artificial intelligence,Vibration,Directed acyclic graph support vector machines,Wavelet packet decomposition,Machine learning,Wavelet | Journal |
Volume | Issue | ISSN |
8 | 3 | 1469-0268 |
Citations | PageRank | References |
2 | 0.53 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang-ming Xian | 1 | 51 | 4.58 |
| Bi-qing Zeng | 2 | 18 | 4.83 |