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Soft Computing for diagnostics in equipment service

Published online by Cambridge University Press:  11 January 2002

PIERO BONISSONE  and
KAI GOEBEL
PIERO BONISSONE
Affiliation:
GE Corporate Research & Development, Information Systems Lab, Niskayuna, NY 12309, USA
KAI GOEBEL
Affiliation:
GE Corporate Research & Development, Information Systems Lab, Niskayuna, NY 12309, USA
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Abstract

We present methods and tools from the Soft Computing (SC) domain, which is used within the diagnostics and prognostics framework to accommodate imprecision of real systems. SC is an association of computing methodologies that includes as its principal members fuzzy, neural, evolutionary, and probabilistic computing. These methodologies enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. We outline the advantages and disadvantages of these methodologies and show how they can be combined to create synergistic hybrid SC systems. We conclude the paper with a description of successful SC case study applications to equipment diagnostics.

Keywords

DiagnosisDiagnosticsHybrid SystemsSoft Computing
Type
Research Article
Information
AI EDAM , Volume 15 , Issue 4 , September 2001 , pp. 267 - 279
Copyright
© 2001 Cambridge University Press

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