Abstract
Pneumatic valve is an important component of the car and also widely used in the automation industry. The performance of the pneumatic valves can be measured by a number of testing parameters which mainly depend on manual testing in China until now. In order to lessen the labor intensity and improve the test efficiency, we propose a pneumatic valve performance test method based on LSSVM algorithm. This method has tested the parameters of the leakage and pressure of the pneumatic valve so that multi-class data can be divided into multiple regions according to different characteristics. The experimental result shows that the proposed method is more accurate than the manual testing in improving the efficiency.
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Agashe, S., Rege, P., Agashe, S.: Control valve fault detection. In: Proceedings of the International Instrumentation Symposium, vol. 474, pp. 297–305 (2008)
Chen, P., Yu, X., Liu, L.: Simulation and experimental study of electro-pneumatic valve used in air-powered engine. J. Zhejiang Univ.-Sci. A (Appl. Phys. Eng.) 10(3), 377–383 (2009)
Jia, M., Gouming, Z., Harold, S., Jim, W.: Adaptive control of a pneumatic valve actuator for an internal combustion engine. In: Proceedings of the 2007 American Control Conference, pp. 3678–3685 (2007)
Chuanhu, Z., Kun, L.: New calibration technology for safety valves of utility boiler. Electricity 4, 44–49 (2001)
Lin, C., Wang, S.: Training algorithm for fuzzy support vector machines with noisy data. Pattern Recogn. Lett. 25(14), 1647–1656 (2004)
You, M., Zhang, J., Sun, D., Gou, J.: Characteristics analysis and control study of a pneumatic proportional valve. In: Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, pp. 242–247 (2015)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1–3), 131–159 (2002)
Xue, X., Yang, X., Chen, X.: Application of a support vector machine for prediction of slope stability. Sci. China Technol. Sci. 57(12), 2379–2386 (2014)
Wang, Y.Q., Wang, S.Y., Laik, K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13(6), 820–831 (2005)
Zhang, Y., Chi, Z., Liu, X.-D., Wang, X.: A novel fuzzy compensation multi-class support vector machine. Appl. Intell. 27, 21–28 (2007)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machines classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
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Li, J., Sun, W. (2017). Application of LSSVM in Performance Test of Pneumatic Valves. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_28
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DOI: https://doi.org/10.1007/978-981-10-6373-2_28
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