Hierarchically Structured Inductive Learning For Fault Diagnosis
Price
Free (open access)
Volume
20
Pages
23
Published
1998
Size
151 kb
Paper DOI
10.2495/AI980411
Copyright
WIT Press
Author(s)
Michael G. Madden
Abstract
This paper presents a new methodology for fault diagnosis, based on the natural hierarchy of components and sub-components in electrical and/or mechanical systems. In the first section, the advantages of hierarchically decomposing learning tasks are discussed. In the second section, the author’s fault diagnosis system, DE/ IFT, is introduced. The underlying algorithm, the training cycle and the operation of DE/IFT are then discussed. In the third section, the hierarchical methodology for fault diagnosis is presented. In the section following, Hierarchical Condition Description files are introduced and the details of implementing hierarchical fault diagnosis within DE/IFT are explained. Next, an example application is discussed. Results of hierarchical fault diagnosis are presented. These are compared with eq
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