Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Engineering Applications of Artificial Intelligence
Fuzzy logic-based decision-making for fault diagnosis in a DC motor
Engineering Applications of Artificial Intelligence
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fault diagnosis of electronic systems using intelligent techniques: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Advances in Artificial Neural Systems
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An online fault detection and isolation (FDI) technique for nonlinear systems based on neurofuzzy networks (NFN) is proposed in this paper. Two NFNs are used. The first one trained by data obtained under normal operating condition models the system and the second one trained online models the residuals. Fuzzy rules that are activated under fault free and faulty conditions are extracted from the second NFN and stored in the symptom vectors using a binary code. A fault database is then formed from these symptom vectors. When applying the proposed FDI technique, the NFN that models the residuals is updated recursively online, from which the symptom vector is obtained. By comparing this symptom vector with those in the fault database, faults are isolated. Further, the fuzzy rules obtained from the symptom vector can also provide linguistic information to experienced operators for identifying the faults. The implementation and performance of the proposed FDI technique is illustrated by simulation examples involving a two-tank water level control system under faulty conditions.