A fault detection, diagnosis, and reconfiguration method via support vector machines
A fault detection, diagnosis, and reconfiguration method via support vector machines
Abstract: This paper presents a fault detection, diagnosis, and reconfiguration method based on support vector machi- nes. This method is appropriate for certain or predetermined faults and involves a fault detection and diagnosis unit and an online controller selection type reconfiguration mechanism. In this method, when a fault is detected and diagnosed by the fault detection and diagnosis unit, a suitable controller, which has been determined via an optimization algorithm in an off-line fashion, is activated to maintain proper closed-loop performance of the system in an on-line manner. In the detection, diagnosis, and reconfiguration stages of the method, support vector classification and regression machines are used and the performance is tested on a simulation model of a two-tank level control system for various fault scenarios.
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- [1] Blanke MS, Marcel M, Wu EN. Concept and methods in fault tolerant control. In: Proceedings of the American Control Conference; Arlington, VA, USA; 2001. pp. 26062620.
- [2] Patton RJ. Fault-tolerant control: the 1997 situation. In: 3rd IFAC Symposium SAFEPROCESS97; Hull, UK; 1997. pp. 10331055.
- [3] Puig V, Quevedo J. Fault-tolerant PID controllers using a passive robust fault diagnosis approach. Control Eng Pract 2001; 9: 12211234.
- [4] Isermann R. Model-based fault-detection and diagnosis - status and applications. Annu Rev Control 2005; 29: 7185.
- [5] Isermann R. Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Berlin, Germany: Springer, 2006.
- [6] Zhong M, Fang H, Ye H. Fault diagnosis of networked control systems. Annu Rev Control 2007; 31: 5568.
- [7] Patton RJ, Frank PM, Clark RN. Issues of Fault Diagnosis for Dynamic Systems. London, UK: Springer, 2000.
- [8] Desobry F, Davy M. Support vector-based online detection of abrupt changes. In: IEEE ICASSP; Hong Kong; 2003. pp. 872875.
- [9] Oblak S, Skrjanc I, Blazic S. On applying interval fuzzy model to fault detection and isolation for nonlinear input- output systems with uncertain parameters. In: IEEE Conference on Control Applications; Toronto, Canada; 2005. pp. 465470.
- [10] Tarassenko L, Nairac A, Townsend NW, Buxton I, Cowley P. Novelty detection for the identification of abnormal- ities. Int J Syst Sci 2000; 31: 14271439.
- [11] Liang YW, Liaw DC, Lee TC. Reliable control of nonlinear systems. IEEE T Automat Contr 2000; 45: 706710.
- [12] Zhang YM, Jiang J. Issues on integration of fault diagnosis and reconfigurable control in active fault tolerant control systems. In: 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes; Beijing, China; 2006. pp. 14371448.
- [13] Zhang YM, Jiang J. Bibliographical review on reconfigurable fault-tolerant control systems. Annu Rev Control 2008; 32: 229252.
- [14] Mahmoud M, Jiang J, Zhang Y. Active Fault Tolerant Control Systems: Stochastic Analysis and Synthesis. Lecture Notes in Control and Information Science. Berlin, Germany: Springer, 2003.
- [15] Saludes S, Fuente MJ. Support vector based novelty detection for fault tolerant control. In: Proceedings of the 44th European Control Conference and CDC; Seville, Spain; 2005. pp. 58205825.
- [16] Saludes S, Fuente MJ. Fault tolerance in the framework of support vector machines based model predictive control. Eng Appl Artif Intel 2010; 23: 11271139.
- [17] Vapnik VN. The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag, 1995.
- [18] Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge University Press, 2000.
- [19] Iplik ̧ ̇ ci S. A support vector machine based control application to the experimental three-tank system. ISA T 2010; 49: 376386.
- [20] Vapnik VN. Statistical Learning Theory. New York, NY, USA: John Wiley and Sons, 1998.
- [21] Smola AJ, Schoelkopf B. A Tutorial on Support Vector Regression. NeuroCOLT2 Technical Report NC-TR-98-030, Royal Holloway College. London, UK: University of London, 1998.
- [22] Ge M, Zhang G, Du R, Xu Y. Application of support vector machine based fault diagnosis. In: Proceedings of the 15th Triennial IFAC World Congress; Barcelona, Spain; 2002. p. 766.
- [23] Oblak S, Skrjanc I, Blazic S. Fault detection for nonlinear systems with uncertain parameters based on the interval fuzzy model. Eng Appl Artif Intel 2007; 20: 503510.
- [24] Zhang X, Parisini T, Polycarpou MM. Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach. IEEE T Automat Contr 2004; 49: 12591274.