Identifiability of parametric models
Identifiability of parametric models
Self-organization of nets of active neurons
Systems Analysis Modelling Simulation
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bounding Approaches to System Identification
Bounding Approaches to System Identification
New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing (Studies in Fuzziness and Soft Computing, V. 143)
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches
Fault Tolerant Control Design For Polytopic LPV Systems
International Journal of Applied Mathematics and Computer Science
Comparing different approaches to model error modeling in robust identification
Automatica (Journal of IFAC)
Set Membership identification of nonlinear systems
Automatica (Journal of IFAC)
Stability Analysis and the Stabilization of a Class of Discrete-Time Dynamic Neural Networks
IEEE Transactions on Neural Networks
Local stability conditions for discrete-time cascade locally recurrent neural networks
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Efficient plant supervision strategy using NN based techniques
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Supervision strategy of a solar volumetric receiver using NN and rule based techniques
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Robust sensor and actuator fault diagnosis with GMDH neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.