Abstract
This paper is concerned with the fault detection and diagnosis problem for the single motor servo systems. The continuous-time nonlinear servo system with disturbance, actuator fault and backlash is modeled. An observer based on radial basis function neural network is constructed to approximate the unknown backlash nonlinear, and a threshold is computed to detect the occurrence of fault. Then, another radial basis function neural network is provided to identify the fault information after a fault occurs. Finally, simulation results show the effectiveness and applicability of the proposed method.
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References
Yoo SJ. Output-feedback fault detection and accommodation of uncertain interconnected systems with time-delayed nonlinear faults. IEEE Trans Syst Man Cybern Syst. 2017;47(5):758–66.
Yi ZH, Etemadi AH. Fault detection for photovoltaic systems based on multi-resolution signal decomposition and fuzzy inference systems. IEEE Trans Smart Grid. 2017;8(3):1274–83.
Yang GH, Wang HM. Fault detection and isolation for a class of unvertain state-feedback fuzzy control systems. IEEE Trans Fuzzy Syst. 2015;23(1):139–51.
Davoodi M, Meskin N, Khorasani K. Event-triggered multiobjective control and fault diagnosis: a unified framework. IEEE Trans Ind Inform. 2017;13(1):298–311.
Wang YL, Shi P, Lim CC, Liu Y. Event-triggered fault detection filter design for a continuous-time networked control system. IEEE Trans Cybern. 2016;46(12):3414–26.
Han HG, Li Y, Qiao JF. A fuzzy neural network approach for online fault detection in waste water treatment process. Comput Electric Eng. 2014;40(7):2216–26.
Chai W, Qiao JF. Passive robust fault detection using RBF neural modeling based on set membership identification. Eng Appl Artif Intel. 2014;28(1):1–12.
Xiao YQ, He YG. A novel approach for analog fault diagnosis based on neural network and improved kernel PCA. Neurocomputing. 2011;74(7):1102–15.
Talebi HA, Khorasani K, Tafazoli S. A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite’s attitude control subsystem. IEEE Trans Neural Netw. 2009;20(1):45–60.
Shi ZG, Zuo ZY. Backsetpping control for gear transmission servo systems with backlash nonlinearity. IEEE Trans Autom Sci Eng. 2015;12(2):752–7.
Merzouki R, Davila JA, Fridman L, Cadiou JC. Backlash phenomenon observation and identification in electromechanical system. Control Eng Pract. 2007;15(4):447–57.
Tan YH, Zhou ZP, Dong RL, He H. Fault detection of mechanical systems with inherent backlash. In: IEEE International Conference on Networking, Sensing and Control. 2013, p. 77–82.
Merzouki R, Medjaher K, Djeziri MA, Ould-Bouamama B. Backlash fault detection in mechatronic system. Mechatronics. 2007;17(6):299–310.
Zhu RJ, Chai TY, Shao C. Robust nonlinear adaptive observer design using dynamic recurrent neural networks. In: Proceedings of the American Conference. 1997, p. 1096–100.
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grants 61433003, 61273150, 61321002.
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Guo, F., Ren, X. (2018). Fault Detection and Diagnosis for Servo Systems with Backlash. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_43
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DOI: https://doi.org/10.1007/978-981-10-6496-8_43
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