Model-Based Fault Detection and Isolation Using Locally Recurrent Neural Networks

Authors:
Piotr Przystałka
Affiliations:
Department of Fundamentals of Machinery Design, Silesian University of Technology, Gliwice, Poland 44-100
Venue:
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 5
Cited 0

Local feedback multilayered networks

Neural Computation
A simple chaotic neuron

Physica D
Fault Diagnosis: Models, Artificial Intelligence, Applications

Fault Diagnosis: Models, Artificial Intelligence, Applications
Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks

Neural Networks
A recurrent fuzzy-neural model for dynamic system identification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The increasing complexity of technological processes implemented in present industrial installations causes serious problems in the modern control system design and analysis. Chemical refineries, electrical furnaces, water treatments and other industrial plants are complex systems and in some cases cannot be precisely described by classical mathematical models. On the other hand, modern industrial systems are subject to faults in their components. Due to these facts, fault-tolerant control design using soft computing methods is gaining more and more attention in recent years. In this paper, the model-based approach to fault detection and isolation using locally recurrent neural networks is presented. The paper contains a numerical example that illustrates the performance of the proposed locally recurrent neural network with respect to other well-known neural structures.