Artificial Intelligence
Automatica (Journal of IFAC)
Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Model-based causal reasoning for process supervision
Automatica (Journal of IFAC)
Qualitative analysis for decision making in supervision of industrial continuous processes
Mathematics and Computers in Simulation - Special issue: 3rd IMACS international workshop on qualitative reasoning and decision support systems
Diagnosis based on explicit means-end models
Artificial Intelligence
A Supervision Support System for Industrial Processes
IEEE Expert: Intelligent Systems and Their Applications
Augmenting the diagnostic power of flow-based approaches to functional reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
DiKe: a model-based diagnosis kernel and its application
AI Communications - Special issue on KI-2001
DiKe - A Model-Based Diagnosis Kernel and Its Application
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Hierarchical representation of complex systems for supporting human decision making
Advanced Engineering Informatics
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Model‐based supervision developed by systems analysts has become an acknowledged supervision aid, ensuring early detection of malfunctions and thereby allowing control of the availability and vulnerability of a process facility. However, it is associated with diagnostics of the process itself, and not of the process control situation, which is the veritable subject of supervision. The operator, facility, control triplet determines a complex situation that must be considered from multiple viewpoints beyond knowledge of the single behavioral model usually advocated in process control approaches. Representing different aspects of process control situation from multiple viewpoints notably allows the on line selection of the behavioral models relevant to the observed situation. Given the size of the application, it was essential not only to structure the knowledge required for the supervision system functions into operating system viewpoints, but also to provide a unique representation method for each viewpoint. The systemic approach SAGACE provides this formal representation framework and the methodology adopted to design and implement our industrial prototype relies on it. All these principles are illustrated by a description of an industrial application in the area of nuclear fuel reprocessing: the size and complexity of the facilities and their high degree of computerization make reprocessing particularly well suited for supervision applications.