Abstract
Statistical data shows that rotor bar breakage and stator inter turn short circuit faults are the two most common faults in asynchronous motors. In order to achieve autonomous diagnosis of motor faults, this paper studies a motor fault diagnosis method based on least squares wavelet support vector machine (LS-WSVM). Using wavelet packets to extract fault feature components from stator current signals. An improved particle swarm optimization algorithm is proposed. The inertia weight and convergence factor are introduced to improve the particle swarm iteration formula, optimize the hyperparameter of LS-WSVM, and find the hyperparameter that optimizes the performance of the support vector machine through iteration. Compared with the unoptimized LS-WSVM, the diagnosis results show that the fault diagnosis of LS-WSVM based on particle swarm optimization has faster training time and classification time, and higer classification accuracy.
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References
Thomson, W.T., Fenger, M.: Current signature analysis to detect induction motor faults. IEEE Ind. Appl. Mag. 7(4), 26–34 (2001)
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)
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)
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)
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)
Ma, X., Huang, X., Chai, Y.: SVM based binary tree multi class classification algorithm and its application in fault diagnosis. Control Dec. Mak. 18(3), 272–276 (2003)
Rong, H., Zhang, G., Jin, W.: Research on support vector machine kernel function and its parameters in system identification. J. Syst. Simul. 18(11), 3204–3208 (2006)
Das, S.R., Panigrahi, P.K., Mishra, K.D.D.: Improving RBF kernel function of support vector machine using particle swarm optimization. Int. J. Adv. Comput. Res. 2(7) (2012)
Lei, G., Jin, C., Yi, Z., et al.: Application of wavelet support vector machine in fault diagnosis of rolling bearings. J. Shanghai Jiao Tong Univ. 4, 678–682 (2009)
Lu, Z., Sun, J., Butts, K.: Multiscale support vector learning with projection operator wavelet kernel for nonlinear dynamical system identification. IEEE Trans. Neural Netw. Learn. Syst. 1–13 (2016)
Chang, P., Li, S., Ge, Y., et al.: Computation of reservoir relative permeability curve based on multi-scale wavelet kernel extreme learning machine. In: Chinese Control Conference, pp. 7179–7184 (2016)
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Guo, C., Li, J., Yang, C. (2023). Motor Fault Diagnosis Based on Improved Support Vector Machine. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_20
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DOI: https://doi.org/10.1007/978-981-99-6882-4_20
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