Dynamic diagnosis based on interval analytical redundancy relations and signs of the symptoms
AI Communications - Model-Based Systems
A compiled model for faults diagnosis based on different techniques
AI Communications - Model-Based Systems
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
Improving robustness in consistency-based diagnosis using possible conflicts
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Real-Time Model-Based Fault Detection and Isolation for UGVs
Journal of Intelligent and Robotic Systems
Compiling all possible conflicts of a CSP
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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
Ensemble methods and model based diagnosis using possible conflicts and system decomposition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Dynamic Bayesian network factors from possible conflicts for continuous system diagnosis
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Early fault classification in dynamic systems using case-based reasoning
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
An integration of FDI and DX techniques for determining the minimal diagnosis in an automatic way
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Automated design of an FDI system for the wind turbine benchmark
Journal of Control Science and Engineering
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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Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.