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Paper details
Number 3 - September 1999
Volume 9 - 1999
Dynamic neural networks for process modelling in fault detection and isolation systems
Józef Korbicz, Krzysztof Patan, Andrzej Obuchowicz
Abstract
A fault diagnosis scheme for unknown nonlinear dynamic systems with modules of residual generation and residual evaluation is considered. Main emphasis is placed upon designing a bank of neural networks with dynamic neurons that
model a system diagnosed at normal and faulty operating points. To improve the quality of neural modelling, two optimization problems are included in the construction of such dynamic networks: searching for an optimal network architecture and the network training algorithm. To find a good solution, the effective well-known cascade-correlation algorithm is adapted here. The residuals generated by a bank of neural models are then evaluated by means of pattern
classification. To illustrate the effectiveness of our approach, two applications are presented: a neural model of Narendra's system and a fault detection and identification system for the two-tank process.
Keywords
fault detection, dynamic neural networks, non-linear modelling, learning algorithms, FL-classifier, two-tank system