Name
Title | ||
|---|---|---|
Fault Diagnosis Of Rotating Machinery Based On Time-Frequency Image Feature Extraction |
Abstract | ||
|---|---|---|
Aiming at the characteristics of time-frequency analysis of unsteady vibration signals, this paper proposes a method based on time-frequency image feature extraction, which combines non-downsampling contour wave transform and local binary mode LBP (Local Binary Pattern) to extract the features of time-frequency image faults. SVM is used for classification and recognition. Finally, the method is verified by simulation data. The results show that the classification accuracy of the method reaches 98.33%, and the extracted texture features are relatively stable. Also, the method is compared with the other 3 feature extraction methods. The results also show that the classification effect of the method is better than that of the traditional feature extraction method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/JIFS-189004 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | DocType | Volume |
Time-frequency image, rotating machinery, fault diagnosis | Journal | 39 |
Issue | ISSN | Citations |
4 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiyi Zhang | 1 | 0 | 0.34 |
| Laigang Zhang | 2 | 0 | 0.34 |
| Teng Zhao | 3 | 0 | 0.34 |
| Mahmoud Mohamed Selim | 4 | 0 | 0.68 |