Neuro-fuzzy systems for diagnosis
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
Fuzzy Modeling for Control
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
On fuzzy logic applications for automatic control, supervision, and fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Set Membership identification of nonlinear systems
Automatica (Journal of IFAC)
Improving decision making in fault detection and isolation using model validity
Engineering Applications of Artificial Intelligence
In-vehicle network level fault diagnostics using fuzzy inference systems
Applied Soft Computing
Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS
Expert Systems with Applications: An International Journal
Passive robust fault detection using RBF neural modeling based on set membership identification
Engineering Applications of Artificial Intelligence
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The paper tackles the problem of robust fault detection using Takagi-Sugeno neuro-fuzzy (N-F) models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such an approach is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection, the adaptive threshold technique is used to deal with the problem. The paper focuses also on the N-F model design procedure. The bounded-error approach is applied to generate rules for the model using available data. The proposed algorithms are applied to fault detection in a valve that is a part of the technical installation at the Lublin sugar factory in Poland. Experimental results are presented in the final part of the paper to confirm the effectiveness of the method.