Abstract
The system’s self-protection mechanism immediately stops the motor when motor system in the event of a malfunction, so it is difficult to collect the fault data when monitoring the motor status. Under the premise of only collecting motor’s health data, using SVDD algorithm to train health data and building non-health data sets based on practical experience in this paper. Based on BP neural network, a random self-adapting particle swarm optimization algorithm (RSAPSO) is used to substitute the original gradient descent method in BP network, training speed and accuracy of BP network training is improved. Three commonly used test functions were used to test the performance of the improved PSO, and the improved particle swarm optimization is compared with the standard particle swarm optimization, particle swarm optimization with compression factor and adaptive particle swarm optimization. In this paper, three asynchronous motor Y225S-4 output shaft vibration acceleration signal in healthy state as a case to test the effectiveness of the algorithm, results show that in the case of only health data, the new algorithm based on single classification has better performance and can effectively monitor the working state of the motor.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhong, B.L., Huang, R.: Introduction to Machine Fault Diagnosis. Mechanical Industry Press, Beijing (2006)
Zheng, L., Zhe, Z., Xiang, Y.: A summary of on-line condition monitoring and fault diagnostics for 3-phase induction motors. J. Wuhan Yejin Univ. Sci. Technol. 24(3), 285–289 (2001)
Park, Y.M., Kim, G.W., Sohn, J.M.: A logic based expert system (LBES) for fault diagnosis of power system. IEEE Trans. Power Syst. 12(1), 363–369 (1997). https://doi.org/10.1109/59.574960
Vazquez, E., Chacon, O.L., Altuve, H.J.: An on-line expert system for fault section diagnosis in power systems. IEEE Trans. Power Syst. 12(1), 357–362 (1997). https://doi.org/10.1109/59.574959
Wen, L., Li, X., Gao, L., Zhang, Y.: A new convolutional neural network based data-driven fault diagnosis method. IEEE Trans. Ind. Electron. 65(7), 5990–5998 (2017). https://doi.org/10.1109/tie.2017.2774777
Saucedo-Dorantes, J., Delgado-Prieto, M., Osornio-Rios, R., Romero-Troncoso, R.: Multi-fault diagnosis method applied to an electric machine based on high-dimensional feature reduction. IEEE Trans. Ind. Appl. PP(99), 1 (2016). https://doi.org/10.1109/tia.2016.2637307
Zhang, Y., Ding, X., Liu, Y., Griffin, P.J.: An artificial neural network approach to transformer fault diagnosis. IEEE Trans. Power Delivery 11(4), 1836–1841 (2002). https://doi.org/10.1109/61.544265
Shu, M.H., Cheng, C.H., Chang, J.R.: Using intuitionistic fuzzy sets for fault-tree analysis on printed circuit board assembly. Microelectron. Reliab. 46(12), 2139–2148 (2006). https://doi.org/10.1016/j.microrel.2006.01.007
Volkanovski, A., Čepin, M., Mavko, B.: Application of the fault tree analysis for assessment of power system reliability. Reliab. Eng. Syst. Saf. 94(6), 1116–1127 (2009). https://doi.org/10.1016/j.ress.2009.01.004
Gao, J., Hu, N., Jiang, L., Fu, J.: A new condition monitoring and fault diagnosis method of engine based on spectrometric oil analysis. Wear 110, 117–124 (2011). https://doi.org/10.1016/j.wear.2007.02.022
Huimin, L.I., Zhenlei, L.I., Rongjun, H.E., Yan, Y.: Rock burst risk evaluation based on particle swarm optimization and bp neural network. J. Min. Saf. Eng. 31(2), 203–207+231 (2014)
Han, X.H., Xiong, X., Duan, F.: A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping. Appl. Intell. 43(4), 855–873 (2015). https://doi.org/10.1007/s10489-015-0679-5
Niu, M., Sun, S., Wu, J., Zhang, Y.: Short-term wind speed hybrid forecasting model based on bias correcting study and its application. Math. Prob. Eng. 2015, 1–13 (2015). https://doi.org/10.1155/2015/351354
Liu, Y.M., Niu, B.: Theory and Practice of New Particle Swarm Optimization, Beijing (2013)
Jie, W.U., Shangguan, W.B., Jing, T., Song, Z.S., Huang, Z.L.: Robust analysis for decoupling layout of a powertrain mounting system. J. Vibr. Shock (2009)
Banerjee, A., Burlina, P., Meth, R.: Fast hyperspectral anomaly detection via SVDD. In: IEEE International Conference on Image Processing, vol. 4, pp. IV-101–IV-104. IEEE (2007). https://doi.org/10.1109/icip.2007.4379964
Luo, H., Jiang Cui, Y.W.: A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor. Expert Syst. Appl. 38(8), 10554–10561 (2011). https://doi.org/10.1016/j.eswa.2011.02.087
Yang, Z., Wang, S., Fu, X.: Pattern recognition-based chillers fault detection method using support vector data description (SVDD). Appl. Energy 112(4), 1041–1048 (2013). https://doi.org/10.1016/j.apenergy.2012.12.043
Liu, Y.H., Lin, S.H., Hsueh, Y.L., Lee, M.J.: Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble. Expert Syst. Appl. 36(2), 1978–1998 (2009). https://doi.org/10.1016/j.eswa.2007.12.015
Tao, X.M., Chen, W.H., Du, B.X., Xu, Y., Dong, H.G.: A novel model of one-class bearing fault detection using SVDD and genetic algorithm. In: IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, pp. 802–807. IEEE (2007). https://doi.org/10.1109/iciea.2007.4318518
Acknowledgments
This research is supported by National Program on Key Basic Research Project of China (973 Program) (2014CB046306) and National Natural Science Foundation of China under Grant 61573361.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Yang, J. et al. (2018). Application of SVDD Single Categorical Data Description in Motor Fault Identification Based on Health Redundant Data. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-93818-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93817-2
Online ISBN: 978-3-319-93818-9
eBook Packages: Computer ScienceComputer Science (R0)