An ANFIS-based data-driven method for fault accommodation

• Autorzy: Khosravi A. , Lu J.
• Języki publikacji: EN

Abstrakty

EN

Since occurrence of faults in different parts of a system as a complex abnormality is inevitable and could cause a total failure, Fault Detection and Accommodation (FDA) is finding ever widening attention for both industrial practitioners as well as academic researchers. In the large majority of real implementation of FDA, analytical model of the system, if known, may exert an impact on the performance of an FDA method. However, in some cases, such analytical model cannot be obtained in advance. Under unavailability assumption of the analytical model, in this paper we develop a data-driven method to identify and model three kinds of faults in nonlinear systems. Two Adaptive Neural-Fuzzy Inference Systems (ANFISs) are employed in this method, i.e. the first one is used for building a model of the faultless plant using the historical data, and the second one for modeling the occurred faults. Parameters of the second ANFIS are adjusted in an indirect way based on minimization of difference between actual and model outputs. Simulation results for a nonlinear system are also presented to demonstrate the potentiality of the proposed method for fault identification.

Słowa kluczowe

EN

fault modeling; Fault Detection and Accommodation (FDA); Adaptive Neural-Fuzzy Inference System (ANFIS); nonlinear systems

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2006
• Tom: Vol. 32, no 4
• Strony: 45--54
• Opis fizyczny: Bibliogr. 19 poz., wykr.

Twórcy

autor

Khosravi A.

autor

Lu J.

• Department of Electronics, Informatics and Automatics, University of Girona, Spain,

Bibliografia

• [1] Altug S., Chow M.Y., Trussell H.J., Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis, IEEE Trans. Industrial Electronics, 46, 1999, pp. 1069-1079.
• [2] Alwi H., Edwards C., Robust sensor fault estimation for tolerant control of a civil aircraft using sliding modes, American Control Conference, 2006. pp. 5704-5709.
• [3] Chen W., Saif M., Actuator fault isolation and estimation for uncertain nonlinear systems, IEEE Int. Conf. Systems, Man and Cybernetics, 2005, pp. 2560-2565.
• [4] Gao Z., Ho D.W.C., Slate/noise estimator for descriptor systems with application to sensor fault diagnosis, IEEE Trans. Signal Processing, 54, 2006, pp. 1316-1326.
• [5] Jang J.-S.R., ANFIS: adaptive-network-based fuzzy inference systems, IEEE Trans. Systems, Man, and Cybernetics, 23, 1993, pp. 665-685.
• [6] Khosravi A.. Talebi H.A., Karrari M., Fault detection and isolation for unknown nonlinear systems using expert methods, IEEE Conf. Control Applications, 2005, pp. 1486-1490.
• [7] Park T.-G., Ryu J.-S.. Lee K.-S., Actuator fault estimation with disturbance decoupling, IEE Proc. Control Theory and Applications, 2000. 147, pp. 501-508.
• [8] Patton R., Frank P., Clark R., Fault diagnosis in dynamic systems, theory and application. Prentice Hall, Britain, 1989.
• [9] Sneider H., Frank P.M., Observer-based supervision and fault detection in robots using nonlinear and fuzzv logic residual evaluation, IEEE Trans. Control System Technology, 4, 1996, pp. 274-282.
• [10] Tan C.P., Edwards C., Reconstruction of sensor faults using a secondary sliding mode observer, IEEE Conf. Decision and Control, 1, 2001, pp. 579-584.
• [11] Uppal F.J., Patton R.J., Fault diagnosis of an electro-pneumatic valve actuator using neural network with fuzzy capabilities, Proc. European Symposium on ANN., 2002, pp. 283-290.
• [12] Vemuri T., Sensor bias fault diagnosis in a class of nonlinear systems, IEEE Trans. Automatic Control, 46, 2001, pp. 949-954.
• [13] Vemuri T., Polycarpou M.M., On the use of on-line approximators for sensor fault diagnosis, American Control Conference, 5, 1998, pp. 2857-2861.
• [14] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A review of process fault detection and diagnosis. Part I: Quantitative model-based methods. Journal of Computers and Chemical Engineering, 27, 2003, pp. 293-311.
• [15] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A review of process fault detection and diagnosis, Part II: Qualitative models and search strategies. Journal of Computers and Chemical Engineering, 27, 2003, pp. 313-326.
• [16] Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N., A review of process fault detection and diagnosis. Part III: Process history based methods, Journal of Computers and Chemical Engineering, 27, 2003, pp. 327-346.
• [17] Wang H., Keerthipala W.W.L., Fuzzy-neuro approach to fault classification for transmission line protection, IEEE Trans. Power Delivery, 13, 1998, pp. 1093-1104.
• [18] Ye D., Yang G.-H., Adaptive fault-tolerant tracking control against actuator faults, American Control Conference, 2005, pp. 5505-5510.
• [19] Zhang X., Parisini T., Polycarpou M.M., Sensor bias fault isolation in a class of nonlinear systems, IEEE Trans. Automatic Control, 50, 2005, pp. 370-376.