Abstract
Quantitative and qualitative models and reasoning methods for diagnosis are able to cover a wide range of different properties of a system. Both groups of methods have advantages and drawbacks in respect to fault diagnosis. In this chapter we propose a framework which combines methods of both groups to a combined diagnosis engine in order to improve the overall quality of diagnosis. Moreover, we present the different methods based on a running example of an autonomous mobile robot. Furthermore, we discuss the problems and research topics which arise from such a fusion of diverse methods. Finally, we explain how actively gathered observation are able to further improve the quality of diagnosis of complex systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Friedrich G., Stumptner M., and Wotawa F., Model-based diagnosis of hardware designs. Artificial Intelligence, 111(2):3–39 (1999).
Hofbaur M., Köb J., Steinbauer G., and Wotawa F., Improving robustness of mobile robots using model-based reasoning. Journal of Intelligence and Robotic Systems, 48(1):37–54 (2007).
Steinbauer G. and Wotawa F., Detecting and locating faults in the control software of autonomous mobile robots. In 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp. 1742–1743 (2005).
Köb D. and Wotawa F., Introducing alias information into model-based debugging. In Ramon Lopez de Mantaras and Lorenza Saitta, editors, Proceedings of the 16th Eureopean Conference on Artificial Intelligence, ECAI’2004, pp. 833–837 (2004). IOS Press.
Struss P. and Price C., Model-based systems in the automotive industry. AI Magazine, 24(4):17–34 (2004).
Muscettola N., Nayak P., Pell B., and Williams B., Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence, 103(1–2):5–48 (1998).
Reiter R., A theory of diagnosis from first principles. Artificial Intelligence, 32(1):57–95 (1987).
Hamscher W., Console L., and de Kleer J., Readings in Model-Based Diagnosis. Morgan Kaufmann, San Mate. (1992).
Kalman R., A new approach to linear filtering and prediction problems. ASME Transactions, Journal of Basic Engineering, 82:35–50 (1960).
Anderson B. and Moore J., Optimal Filtering. Information and System Sciences Series. Prentice Hall, Englewood Cliffs (1979).
Isermann R., Supervision, fault-detection and fault-diagnosis methods - an introduction. Control Engineering Practice, 5(5):639–652 (1997).
Chen J. and Patton R., Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer, Dordrecht (1999).
Dearden R. and Clancy D., Particle filters for real-time fault detection in planetary rovers. In Proceedings of the 13th International Workshop on Principles of Diagnosis, pages 1 – 6 (2002).
Verma V., Gordon G., Simmons R., and Thrun S., Real-time fault diagnosis. IEEE Robotics & Automation Magazine, 11(2):56 – 66 (2004).
Roos N., ten Teije A., Bos A., and Witteveen C., Multi-agent diagnosis with spatially distributed knowledge. In Proceedings of the Belgium-Netherlands Artificial Intelligence Conference (BNAIC), pp 275–282 (2002).
de Kleer J., Getting the probabilities right for measurement selection. In 17th InternationalWorkshop on Principles of Diagnosis (DX-06), pp 141–146 (2006).
Hofbaur M., Hybrid Estimation of Complex Systems, volume 319 of Lecture Notes in Control and Information Sciences. Springer Verlag, New York (2005).
Drolet L., Michaud F., and Cote J., Adaptable sensor fusion using multiple kalman filters. In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (2000).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Steinbauer, G., Wotawa, F. (2009). Comining Quantitative and Qualitative Models with Active Observtions to Improve Diagnosis of Complex Systems. In: Martínez Madrid, N., Seepold, R.E. (eds) Intelligent Technical Systems. Lecture Notes in Electrical Engineering, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9823-9_15
Download citation
DOI: https://doi.org/10.1007/978-1-4020-9823-9_15
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-9822-2
Online ISBN: 978-1-4020-9823-9
eBook Packages: EngineeringEngineering (R0)