Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks

We discuss a new type of macroarchitecture of neural networks called a HANN and how to train it for fault diagnosis given appropriate data patterns. The HANN divides a large number of patterns into many smaller subsets so the classification can be carried out more efficiently via an artificial neura...

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Bibliographic Details
Published in:AIChE journal 1994-05, Vol.40 (5), p.839-848
Main Authors: Watanabe, Kajiro, Hirota, Seiichi, Hou, Liya, Himmelblau, D. M.
Format: Article
Language:English
Subjects:
Applied sciences
Chemical engineering
Exact sciences and technology
Safety
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Summary:We discuss a new type of macroarchitecture of neural networks called a HANN and how to train it for fault diagnosis given appropriate data patterns. The HANN divides a large number of patterns into many smaller subsets so the classification can be carried out more efficiently via an artificial neural network. One of its advantages is that multiple faults can be detected in new data even if the network is trained with data representing single faults. The use of a HANN is illustrated in fault diagnosis of a chemical reactor.
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.690400510