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
Improving decision making in fault detection and isolation using model validity
Engineering Applications of Artificial Intelligence
Hierarchical representation of complex systems for supporting human decision making
Advanced Engineering Informatics
Artificial intelligence for industrial process supervision
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Brief Causal fault detection and isolation based on a set-membership approach
Automatica (Journal of IFAC)
Progress in root cause and fault propagation analysis of large-scale industrial processes
Journal of Control Science and Engineering
| Hi-index | 22.15 |
Model-based diagnosis is founded on the construction of fault indicators. The methods proposed for this purpose generally represent the process by means of an extremely inflexible formalism that limits the scope of applications. Moreover, it is usually difficult and costly to develop precise mathematical models of complex plants. New and more flexible techniques intended notably to explain the observed behavior open new perspectives for fault detection and diagnosis. The diagnostic procedures for such plants are generally integrated into a supervisory system, and must therefore be provided with explanatory features that are essential interpretation and decision-making supports. Techniques based on causal graphs constitute a promising approach for this purpose. A causal graph represents the process at a high level of abstraction, and may be adapted to a variety of modeling knowledge corresponding to different degrees of precision in the underlying mathematical models. When the process is dynamic the causal structure must allow temporal reasoning. Lastly, because reasoning on real numbers is often used by human beings, fuzzy logic is introduced as a numeric-symbolic interface between the quantitative fault indicators and the symbolic diagnostic reasoning on them; it also provides an effective decision-making tool in imprecise or uncertain environments. An industrial application in the nuclear fuel reprocessing industry is presented.