Monday, December 7, 2009
AUTONOMOUS FAULT DIAGNOSIS: STATE OF THE ART AND AERONAUTICAL BENCHMARK
This article briefly reviews the state of the art in Fault Detection and Isolation (FDI), and give some description (describes) a test-case for in-flight fault diagnosis. The main concepts are recalled, the links between the approaches are indicated and the most promising methods are highlighted. The nonlinear models of the system, its sensors and actuators are described in the second part of the article, along with fault scenarios. Constraints restrict the approaches that are applicable on the test-case and suggest quantitative indicates that can be used to evaluate fault diagnosis strategies in an aeronautical context. This is the abstract of the article that I found it on the internet. I think this article is so useful to study and to know much more about how to write a nice article to International journal and to learn about Distance Learning in Business and Management, Distance Learning Management and Distance Learning Indonesia. This article has a conclusion like I write it below:
A survey of the main fault diagnosis approaches has been conducted in this paper. A classification according to the type of knowledge available has been proposed, and the links between apparently dissimilar techniques coming from different communities have been emphasized. We defined a test-case for in-flight FDI, based on an interceptor missile with a predetermined set of sensors and actuators with no hardware redundancy. The nonlinear models of the aircraft, its components and the faults affecting them were described in detail. Finally, methods to be further investigated along with the performance criteria to compare them were set forth.
These are a few references that used on this article:
 G. Ducard and H.P. Geering. Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles. Journal of Guidance Control and Dynamics, 31(1):225, 2008.
 R.J. Patton. Fault detection and diagnosis in aerospace systems using analytical redundancy. Computing & Control Engineering Journal, 2(3):127–136, 1991.
 R. Isermann and P. Balle. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5):709–719, 1997.
 C. Angeli and A. Chatzinikolaou. On-line fault detection techniques for technical system: A survey. International Journal of Computer Science & Applications, 1(1):12–30, 2004.
 V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, and K. Yin. A review of process fault detection and diagnosis Part III: Process history based methods. Computers and Chemical Engineering, 27(3):327–346, 2003.
 B. Dubuisson. Diagnostic et reconnaissance des formes. Hermes Paris, 1990.
 R.J. Patton, C.J. Lopez-Toribio, and F.J. Uppal. Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems. International Journal of Applied Mathematics and Computer Science, 9(3):471–518, 1999.
 H.J. Shin, D.H. Eom, and S.S. Kim. One-class support vector machines: an application in machine fault detection and classification. Comput. Ind.Eng., 48(2):395–408, 2005.
 R.J. Patton, J. Chen, and T.M. Siew. Fault diagnosis in nonlinear dynamic systems via neural networks. In International Conference on Control, Coventry, volume 2, pages 1346–1351, 1994.
 L. Györfi. Principles of Nonparametric Learning. Springer Verlag Wien New York, 2002.
 V.N. Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5):988–999, 1999.
 C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. Springer-Verlag New York, 2006.
 W. Li, H.H. Yue, S. Valle-Cervantes, and S.J. Qin. Recursive PCA for adaptive process monitoring. Journal of Process Control, 10(5):471–486, 2000.
 J.M. Lee, C.K. Yoo, S.W. Choi, P.A. Vanrolleghem, and I.B. Lee. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1):223–234, 2004.
 L. Chittaro and R. Ranon. Hierarchical model-based diagnosis based on structural abstraction. Artificial Intelligence, 155(1):147–182, 2004.
 J. Montmain and S. Gentil. Dynamic causal model diagnostic reasoning for online technical process supervision. Automatica, 36(8):1137–1152, 2000.
 C. Combastel, S. Lesecq, S. Petropol, and S. Gentil. Model-based and wavelet approaches to induction motor on-line fault detection. Control Engineering Practice, 10(5):493–509, 2002.
 S.M. Castillo, E.R. Gelso, and J. Armengol. Constraint satisfaction techniques under uncertain conditions for fault diagnosis in nonlinear dynamic systems. In 16th IEEE Mediterranean Conference on Control and Automation,, pages 1216–1221, 2008.
 M. Basseville and I.V. Nikiforov. Detection of Abrupt Changes: Theory and Application. Prentice Hall Englewood Cliffs, NJ, 1993.
 M. Witczak. Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches. Springer-Verlag, Berlin-Heidelberg, 2007