Abstract
Computer diagnosis systems grounded on hand-crafted decision trees are wide-spread in industrial practice. Since the complexity of technical system increases and innovation cycles are shortened, the need for systematic decision tree generation and maintenance arises. In this paper, the MAD system is introduced which generates decision trees based on qualitative device models. Existing resources such as design data and expert design know-how as well as decision trees and diagnosis knowledge can easily be reused and integrated into decision tree generation. Since decision tree generation is based on device models, applying MAD reduces average fault identification cost and facilitates quality management of diagnosis equipment. Furthermore, cost of diagnosis system generation, modification and maintenance is reduced. We have successfully evaluated the MAD system in cooperation with the german forklift manufacturer STILL GmbH Hamburg
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© 1999 Springer-Verlag Berlin Heidelberg
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Milde, H., Hotz, L., Kahl, J., Neumann, B., Wessel, S. (1999). MAD: A Real World Application of Qualitative Model-Based Decision Tree Generation for Diagnosis. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_28
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DOI: https://doi.org/10.1007/978-3-540-48765-4_28
Publisher Name: Springer, Berlin, Heidelberg
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