WIT Press

Fault Diagnosis In Complex Chemical Processes Using Hierarchical Neural Networks

Price

Free (open access)

Paper DOI

10.2495/AI950431

Volume

10

Pages

8

Published

1995

Size

854 kb

Author(s)

I.E. Ozyurt, M.C. Camurdan & A.K. Sunol

Abstract

BackPropagation Neural Networks (BPN) with linear activation functions have some shortcomings including long training time, neuron size determination problems with hidden layers, extrapolation problems which reduces their applicability for real time fault diagnosis of complex processes. Two hierarchical diagnostic approaches that are based on hierarchically ordered BPNs and elliptical neural networks are developed to overcome some of these limitations. Their applicability and reliability are tested and compared using a hydrocarbon chlorination plant troubleshooting simulator. 1 Introduction The fault diagnosis problem in chemical process industries (CPI) is addressed through v

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