Recognition of Crack-Rubbing Coupling Fault of Bearing under High Water Pressure Based on Polar Symmetry Mode Decomposition
| Autor: | Wen Hua, Shiming Dong, Tianzhou Xie, Yanchao Yao, Jiuzhou Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Physics and Astronomy (miscellaneous)
Computer science General Mathematics Feature extraction 02 engineering and technology Probabilistic neural network marine bearing 0203 mechanical engineering crack-rubbing coupling 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Time domain fault recognition lcsh:Mathematics Dimensionality reduction 020208 electrical & electronic engineering polar symmetry mode decomposition kinetic equations lcsh:QA1-939 Rubbing Nonlinear system 020303 mechanical engineering & transports Chemistry (miscellaneous) bearing crack identification Polar probabilistic neural network classification Algorithm Classifier (UML) |
| Zdroj: | Symmetry Volume 13 Issue 1 Symmetry, Vol 13, Iss 59, p 59 (2021) |
| ISSN: | 2073-8994 |
| DOI: | 10.3390/sym13010059 |
| Popis: | The precision of current research on fault recognition of marine bearing remains to be improved. Therefore, a recognition method of crack-rubbing coupling fault of bearing under high water pressure based on polar symmetry mode decomposition is proposed in this article. The structure of marine bearing was analyzed, and the system was divided into several subsystems. Then, the nonlinearity relationship among the subsystems was confirmed. One subsystem was used to represent other subsystems, which was imported into the kinetic equation to obtain the equation after dimensionality reduction. According to the results of dimensionality reduction, the features of signal were measured from time domain, energy, and entropy. Meanwhile, the interior features of signal were extracted. Based on the feature extraction, the classifier of probabilistic neural network was introduced. The signal was recognized, and the recognition results were output via the training of signal sample data. Experimental results show that the method has better dimensionality reduction effect and high recognition precision. The method is practical. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |