Abstract
For estimating sensor faults for the plant with min-disturbance, a novel fault diagnosis method based on support vector interval regression is proposed. This method can reduce the influence of min-disturbance. When faults occur in this plant, the interval of regression model can not contain outputs of the plant and the faults can be detected. Experiments are given to demonstrate the efficiency.
This work was supported by natural science foundation of Ningbo(No. 2009A610074) A Project Supported by Scientific Research Fund of Zhejiang Provincial Education Department (Y200803444); Advance Research Fund of College of Science and Technology (003-21020901); Education Research Fund of Ningbo University (007-200903).
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References
Isermann, R.: Process fault detection based on modeling and estimation methods: a survey. Automatic 20(2), 387–404 (1984)
Wang, H., Daley, S.: Actuator fault disgnosis: An adaptive observer based technique. IEEE Trans. Autom. Control 41(2), 1073–1078 (1996)
Tyler, M.L., Asano, K., Morari, M.: Application of Moving Horizon Estimation Based Fault Detection to Cold Tandem Steel Mill. International Journal of Control (2000)
Zhang, X., Polycarpou, M., Parisini, T.: A robust detection and isolation scheme for abrupt and incipient faults in nonliear systems. IEEE Trans. Autom. Control 47(4), 576–593 (2002)
Isermann, R.: Model-based fault-detection and diagnosis-status and application. Ann. Rev. Control 29(2), 81–85 (2005)
Tao, G., Chen, S.H., Joshi, S.M.: An adaptive control scheme for systems with unknown actuator failures. Automatic 38(6), 1027–1034 (2002)
Demetriou, M.: Robust adaptive technique for sensor fault detection and diagnosis. In: Proc. IEEE Conf. Dec. Control, pp. 1143–1148 (1998)
Tao, G., Tang, X.D., Chen, S.H., Fei, J.T., Joshi, S.M.: Adaptive failure compensation of two-state actuators for a morphing aircraft lateral model. IEEE Trans. Control Syst. Technol 14(1), 157–164 (2006)
Zhang, L.-J., He, Z.-S., Zheng-Jia: Fault diagnosis approach based on hidden markov model and support vector machine. Chinese Journal of Mechanical Engineering 8(3) (2003)
Tian, H., Wang, A.: A novel fault diagnosis system for blast furnace based on support vector machine ensemble. ISIJ International 5(20) (2010)
Tanaka, H., Lee, H.: Interval regression analysis by quadratic programming approach. IEEE Transactions on Fuzzy Systems 6(4), 473–481 (1998)
Hwang, C., Dug, H.H., Kyung, H.S.: Support vector interval regression machine for crisp input and output data. Fuzzy Sets and Systems (157), 1114–1125 (2006)
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Chen, Y., Yang, X. (2012). A Novel Fault Diagnosis Method for the Plant with Min-Disturbance. In: Deng, W. (eds) Future Control and Automation. Lecture Notes in Electrical Engineering, vol 173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31003-4_34
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DOI: https://doi.org/10.1007/978-3-642-31003-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31002-7
Online ISBN: 978-3-642-31003-4
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