Identification and application of bounded-parameter models
Automatica (Journal of IFAC)
Reformulation of parameter identification with unknown-but-bounded errors
Mathematics and Computers in Simulation
Model-based causal reasoning for process supervision
Automatica (Journal of IFAC)
Consistency Techniques in Ordinary Differential Equations
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Normality and faults in logic-based diagnosis
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic causal model diagnostic reasoning for online technical process supervision
Automatica (Journal of IFAC)
Engineering Applications of Artificial Intelligence
Improving decision making in fault detection and isolation using model validity
Engineering Applications of Artificial Intelligence
SQualTrack: a tool for robust fault detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief paper: Interval observer design for consistency checks of nonlinear continuous-time systems
Automatica (Journal of IFAC)
International Journal of Applied Mathematics and Computer Science
Relaxed fault detection and isolation: An application to a nonlinear case study
Automatica (Journal of IFAC)
Progress in root cause and fault propagation analysis of large-scale industrial processes
Journal of Control Science and Engineering
Passive robust fault detection using RBF neural modeling based on set membership identification
Engineering Applications of Artificial Intelligence
Hi-index | 22.15 |
This paper presents a diagnostic methodology relying on a set-membership approach for fault detection and on a causal model for fault isolation. Set-membership methods are a promising approach to fault detection because they take into account a priori knowledge of model uncertainties and measurement errors. Every uncertain model parameter and/or measurement is represented by a bounded variable. In this paper, detection consists of verifying the membership of measurements to an interval. First order discrete time models are used and their output is explicitly computed with interval arithmetic. Fault isolation relies on a causal analysis and the exoneration principle, which allows focusing the consistency tests on simple local models. The isolation strategy consists of two steps: performing minimal tests found with the causal graph and determining on line additional relevant tests that reduce the final diagnosis. An application for a nuclear process is used in order to illustrate the method's efficiency.