Motor Fault Diagnosis Based on Improved Support Vector Machine

Author:

Guo Caixiang,Li Jin,Yang Chenxi

Publisher

Springer Nature Singapore

Reference11 articles.

1. Thomson, W.T., Fenger, M.: Current signature analysis to detect induction motor faults. IEEE Ind. Appl. Mag. 7(4), 26–34 (2001)

2. Li, B., Chow, M.Y., Tipsuwan, Y., et al.: Neural-network-based motor rolling bearing fault diagnosis. Ind. Electron. IEEE Trans. 47(5), 1060–1069 (2000)

3. Nandi, S., Toliyat, H.A.: Condition monitoring and fault diagnosis of electrical machines-a review. In: Industry Applications Conference, 1999. Conference Record of the Thirty-Fourth Ias Meeting, pp. 197–204, vol.1. IEEE (1999)

4. Ding, S., Qi, B., Tan, H.: Overview of support vector machine theory and algorithm research. J. Univ. Electron. Sci. Technol. 40(1), 2–10 (2011)

5. Wang, H., Zhang, X., Yu, J.: Fault diagnosis method based on support vector machine. J. East Chin. Univ. Sci. Technol. (Nat. Sci. Edn.) 30(2), 179–182 (2004)

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