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Articles:2016,Vol:21,Issue(4):231-240 |
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Citation: |
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SUN Yebei, ZHAO Rongzhen, TANG Xiaobin. Fault Pattern Recognition based on Kernel Method and Fuzzy C-means[J]. International Journal of Plant Engineering and Management, 2016, 21(4): 231-240 |
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| Fault Pattern Recognition based on Kernel Method and Fuzzy C-means |
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| SUN Yebei, ZHAO Rongzhen, TANG Xiaobin |
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| School of Mechanical & Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050, P. R. China |
| Abstract: |
| A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means (FCM) is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery. |
| Key words:
Kernel method
fuzzy C-means FCM
pattern recognition
clustering
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| Received: 2016-09-26
Revised:
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| DOI: 10.13434/j.cnki.1007-4546.2016.0404 |
| Corresponding author:
Email： |
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References: |
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