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Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks (1994)

Watanabe, Kajiro ; Hirota, Seiichi ; Hou, Liya ; [et al.]

Hoboken, NJ : Wiley-Blackwell

AIChE Journal 40 (1994), S. 839-848

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ISSN: 0001-1541

Keywords: Chemistry ; Chemical Engineering

Source: Wiley InterScience Backfile Collection 1832-2000

Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology

Notes: 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.

Additional Material: 3 Ill.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1002/aic.690400510

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