Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

• Autorzy: Manikandan P. , Geetha M.

Identyfikatory

DOI

10.2478/acsc-2014-0016

• Języki publikacji: EN

Abstrakty

EN

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

Słowa kluczowe

EN

extended Kalman filter; fault diagnosis; fuzzy logic; two-tank system

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2014
• Tom: Vol. 24, no. 3
• Strony: 271--287
• Opis fizyczny: Bibliogr. 18 poz., rys., wzory

Twórcy

autor

Manikandan P.

• Department of Instrumentation and Control systems Engineering PSG College of Technology, Coimbatore, India

autor

Geetha M.

• Department of Instrumentation and Control systems Engineering PSG College of Technology, Coimbatore, India

Bibliografia

• [1] A. Evsukoff and R. Schirru: Neuro-fuzzy systems for fault detection and isolation in nuclear reactors. The 10th IEEE Int. Conf. on Fuzzy Systems, (2001), 1460-1463.
• [2] R. Patton, P. P. Frank and R. Clark: Fault Diagnosis in Dynamic Systems, theory and Application, Prentice Hall, Britain, 1989.
• [3] F. Harrou, M. N. Nounou, H. N. Nounou and M. Madakyaru: Statistical fault detection using PCA-based GLR hypothesis testing. J. of Loss Prevention in the Process Industries, 26 (2013), 129-139.
• [4] A. S. Willsky: A survey of design methods for failure detection in dynamic systems. Automatica, 12 (1976), 601-611.
• [5] P. M. Frank: Fault diagnosis in dynamic systems using analytical and knowledge based redundancy - a survey and some new results. Automatica, 26 (1990), 459-474.
• [6] R. Isermann: Process fault detection and diagnosis methods. IFAC Symp. SAFEPROCESS, Finland, 2 (1994), 597-612.
• [7] M. Serpas, Y. Chu and J. Hahn: Fault detection approach for systems involving soft sensors. J. of Loss Prevention in the Process Industries, 26 (2013), 443-452.
• [8] J. J. Gertler: A survey of model based failure detection and isolation in complex plant. IEEE Control System Magazine, 8 (1988), 3-11.
• [9] O. M. Al-Jarrah and M. Al-Rousan: Fault detection and accommodation in dynamic systems using adaptive neuro fuzzy inference systems. IEE Proc. Control Theory and Applications, 148(4), (2001), 283-290.
• [10] D. Fussel and P. Balle: Combining neuro fuzzy and machine learning for fault diagnosis of a DC motor. Proc. American Control Conference, 1 (1997), 37-41.
• [11] F. J. Uppal, R. J. Patton: Fault diagnosis of an electro pneumatic valve actuator using neural network with fuzzy capabilities. Proc. of 10th European Symp. on Artificial Neural Networks, Belgium, (2002).
• [12] Y. Diao and M. Passino: Fault diagnosis for a turbine engine. Control Engineering Practice Magazine, 12(9), (2004), 1151-1165.
• [13] U. Sabura Banu and G. Uma: Anfis based sensor fault detection for continuous stirred tank reactor. Applied Soft Computing, 11 (2011), 2618-2624.
• [14] A. Srinivasan and C. Batur: Hopfield/ART-1 neural network based fault detection and isolation.IEEE Trans. on Neural Networks, 5(6), (1994), 890-899.
• [15] S. Naidu, E. Zafirou and T. J. Mcavoy: Use of neural networks for failure detection ina control system. IEEE Control Systems Magazine, 10 (1990), 49-55.
• [16] R. J. Patton, J. Chen and T. M. Siew: Fault diagnosis in nonlinear dynamic systems via neural networks. Proc. of Int. Conf. on Control, Coventry, UK, 2 (1994), 1346-1351.
• [17] A. Kumagai, T. Liu and P. Hozian: Control of shape memory alloy actuators with a neuro-fuzzy feed forward model element. J. of Intelligent Manufacturing, 1 (2006), 45-56.
• [18] M. Mordjaoui, M. Chabane, B. Boudjema and H. Zaier: Neuro-fuzzy modeling for dynamic ferromagnetic hysteresis. 4th Int. Conf. on Computer Integrated Manufacturing, (2007).