Abstract
In response to the problem of low fault identification rate of induction motor, An induction motor fault diagnosis method based on the combination of fast overall average empirical modal decomposition (FEEMD) and support vector machine (SSA-SVM) optimized by sparrow search algorithm is proposed. First, the stator current is decomposed into intrinsic modal components (IMFs) of sequentially decreasing frequency by FEEMD, and then the IMF components with larger correlation coefficients are selected by correlation coefficient method and the energy entropy and sample entropy are calculated as the eigenvectors, which are then inputted into the SSA-SVM model in order to receive the diagnosis results. The results show that the fault diagnosis accuracy of SSA-SVM model reaches 96.7%, which has higher accuracy and shorter time compared with the two models of Grey Wolf Algorithm (GWO) optimized SVM and Particle Swarm Algorithm (PSO) optimized SVM, which verifies that the method is a reliable method for fault diagnosis of induction motors.
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References
Li, R.Y., Liu, F., Liang, L., et al.: Fault identification of broken rotor bars the variable frequency AC motor based on parameter optimized variational mode decomposition. Trans. China Electrotechnology Soc. 36(18), 3922–3933 (2021). (in Chinese)
Chen, P., Yuan, L., He, Y., et al.: An improced SVM classifier based on double chains quantum geneticalgorithm and its application in analogue circuit diagnosis. Neurocomputing 211, 202–211 (2016)
Shi, L.P., Wang, P.P., Hu, Y.J., et al.: Broken rotor fault diagnosis of induction motor based on bare-bone particle swarm optimization and support vector machine. Trans. China Electrotechnology Soc. 29(01), 147–155 (2014). (in Chinese)
Huang, Y.F., Shi, X.F., He, S.Z.: A wind power gearbox fault diagnosis method based on principal component analysis and support vector machine. Therm. Power Eng. 37(10), 175–181 (2022). (in Chinese)
Agasthian, A., Pamula, R., Kumaraswamidhas, L.A.: Fault classificationand detection in wind turbine using Cuckoo-optimized support vector machine. Neural Comput. Appl. 31(5), 1503–1511 (2019)
Wu, Z.H., Bai, H.J., Yan, H., et al.: Gearbox fault diagnosis based on variational state decomposition and gray wolf optimization support vector machine. Sci. Technol. Eng. 23(16), 6881–6888 (2023). (in Chinese)
Qiu, H.F., Su, N., Tian, S.L.: Research on the application of improved support vector machine in power transformer fault diagnions. Electr. Measure. Instrum. 59(11), 48–53 (2022). (in Chinese)
Wang, B.J., Zhang, X.L., Sun, C., et al.: A quantitative intelligent diagnosis method for early weak fault of aviation high-speed bearings. ISA Trans. 93, 370–383 (2019)
Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. 8(1), 22–34 (2020)
Wang, Y.H., Yeh, C.H., Young, H.W.V., et al.: On the computational complexity of the empirical mode decomposition algorithm. Phys. Stat. Mech. Appl. 400(2), 159–167 (2014)
Yang, Z.S., Kong, C.R., Rong, X., et al.: Fault diagnosis of mining asynchronous motor based on EEMD energy entropy and ANN. Micro Motor. 54(08), 23–27+61 (2021). (in Chinese)
Huang, X.H., Tian, K.C., Rong, X., et al.: Fault diagnosis method of asynchronous motor under variable frequency environment. Mach. Tools Hydraulics 50(18), 165–171 (2022). (in Chinese)
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This work was supported by the National Natural Science Foundation of China, grant number 62203196.
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Liu, M., Wu, J. (2024). Induction Motor Fault Diagnosis Based on SSA-SVM. In: Yang, Q., Li, Z., Luo, A. (eds) The Proceedings of the 18th Annual Conference of China Electrotechnical Society. ACCES 2023. Lecture Notes in Electrical Engineering, vol 1167. Springer, Singapore. https://doi.org/10.1007/978-981-97-1064-5_44
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DOI: https://doi.org/10.1007/978-981-97-1064-5_44
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