Fault detection via factorization approach
Systems & Control Letters
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Fuzzy Modeling for Control
Foundations of Fuzzy Systems
Decision tree search methods in fuzzy modeling and classification
International Journal of Approximate Reasoning
Model-based fault diagnosis using fuzzy matching
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On fuzzy logic applications for automatic control, supervision, and fault diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Process fault detection based on modeling and estimation methods-A survey
Automatica (Journal of IFAC)
Dynamic theorem proving algorithm for consistency-based diagnosis
Expert Systems with Applications: An International Journal
A novel Artificial Immune System for fault behavior detection
Expert Systems with Applications: An International Journal
On-line adaptive clustering for process monitoring and fault detection
Expert Systems with Applications: An International Journal
Fault tolerant control using a fuzzy predictive approach
Expert Systems with Applications: An International Journal
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
Model-based fault detection and isolation (FDI) is an approach with increasing attention in the academic and industrial fields, due to economical and safety related matters. In FDI, the discrepancies between system outputs and model outputs are called residuals, and are used to detect and isolate faults. This paper proposes a model-based architecture for fault detection and isolation based on fuzzy methods. Fuzzy modeling is used to derive nonlinear models for the process running in normal operation and for each fault. When a fault occurs, fault detection is performed using the residuals. Then, the faulty fuzzy models are used to isolate a fault. The FDI architecture proposed in this paper uses a fuzzy decision making approach to isolate faults, which is based on the analysis of the residuals. Fuzzy decision factors are derived to isolate faults. An industrial valve simulator is used to obtain several abrupt and incipient faults, which are some of the possible faults in the real system. The proposed fuzzy FDI architecture was able to detect and isolate the simulated abrupt and incipient faults.