Premium
Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks
Author(s) -
Watanabe Kajiro,
Hirota Seiichi,
Hou Liya,
Himmelblau D. M.
Publication year - 1994
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690400510
Subject(s) - artificial neural network , fault (geology) , computer science , artificial intelligence , pattern recognition (psychology) , data mining , machine learning , geology , seismology
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.