Robust Reliability of Diagnostic Multi-Hypothesis Algorithms: Application to Rotating Machinery

  • Damage diagnosis based on a bank of Kalman filters, each one conditioned on a specific hypothesized system condition, is a well recognized and powerful diagnostic tool. This multi-hypothesis approach can be applied to a wide range of damage conditions. In this paper, we will focus on the diagnosis of cracks in rotating machinery. The question we address is: how to optimize the multi-hypothesis algorithm with respect to the uncertainty of the spatial form and location of cracks and their resulting dynamic effects. First, we formulate a measure of the reliability of the diagnostic algorithm, and then we discuss modifications of the diagnostic algorithm for the maximization of the reliability. The reliability of a diagnostic algorithm is measured by the amount of uncertainty consistent with no-failure of the diagnosis. Uncertainty is quantitatively represented with convex models.

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Metadaten
Author:Yakov Ben-Haim, Susanne Seibold
URN (permanent link):urn:nbn:de:hbz:386-kluedo-7107
Serie (Series number):Berichte des Fraunhofer-Instituts für Techno- und Wirtschaftsmathematik (ITWM Report) (3)
Document Type:Preprint
Language of publication:English
Year of Completion:1998
Year of Publication:1998
Publishing Institute:Fraunhofer-Institut für Techno- und Wirtschaftsmathematik
Tag:Kalman filtering ; Robust reliability ; convex models ; crack diagnosis; multi-hypothesis diagnosis ; rotating machinery
Faculties / Organisational entities:Fraunhofer (ITWM)
DDC-Cassification:510 Mathematik

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