Sensor placement for fault isolation in linear differential-algebraic systems
Automatica (Journal of IFAC)
An algorithm based on structural analysis for model-based fault diagnosis
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
A Method for Sensor Placement Taking into Account Diagnosability Criteria
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
A comprehensive diagnosis methodology for complex hybrid systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Analyzing the influence of differential constraints in possible conflict and ARR computation
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new efficient and flexible algorithm for the design of testable subsystems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Automated design of an FDI system for the wind turbine benchmark
Journal of Control Science and Engineering
Fault diagnosis of electric railway traction substation with model-based relation guiding algorithm
Expert Systems with Applications: An International Journal
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
An event-based distributed diagnosis framework using structural model decomposition
Artificial Intelligence
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In model-based diagnosis, diagnostic system construction is based on a model of the technical system to be diagnosed. To handle large differential algebraic imemodels and to achieve fault isolation, a common strategy is to pick out small overconstrained parts of the model and to test these separately against measured signals. In this paper, a new algorithm for computing all minimal overconstrained subsystems in a model is proposed. For complexity comparison, previous algorithms are recalled. It is shown that the time complexity under certain conditions is much better for the new algorithm. This is illustrated using a truck engine model.