Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems
International Journal of Applied Mathematics and Computer Science, Tome 9 (1999) no. 3, pp. 519-546.

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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
Mots-clés : wykrywanie błędu, sieć neuronowa dynamiczna, modelowanie nieliniowe, algorytm inteligentny
@article{IJAMCS_1999_9_3_a1,
     author = {Korbicz, J. and Patan, K. and Obuchowicz, A.},
     title = {Dynamic {Neural} {Networks} for {Process} {Modelling} in {Fault} {Detection} and {Isolation} {Systems}},
     journal = {International Journal of Applied Mathematics and Computer Science},
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     language = {en},
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Korbicz, J.; Patan, K.; Obuchowicz, A. Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems. International Journal of Applied Mathematics and Computer Science, Tome 9 (1999) no. 3, pp. 519-546. http://geodesic.mathdoc.fr/item/IJAMCS_1999_9_3_a1/