A qualitative physics based on confluences
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Qualitative modelling of dynamical systems motivation, methods, and prospective applications
Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
CyclePad: an articulate virtual laboratory for engineering thermodynamics
Artificial Intelligence - Special issue on applications of artificial intelligence
Semi-quantitative system identification
Artificial Intelligence
Fault diagnosis using Rough Sets Theory
Computers in Industry
Process Monitoring and Diagnosis: A Model-Based Approach
IEEE Expert: Intelligent Systems and Their Applications
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A precise process monitoring is fundamental to guarantee the correct operation of a technical process. The required monitoring applications are frequently based on models of the ''correct'' system behavior. However, the development of precise process models is very time-consuming and expensive, if at all possible, due to the complexity of real process plants. In this paper a modeling approach is presented, which is based on the qualitative description of the process states in complex technical systems, and incorporates vague and uncertain information about the industrial process that otherwise would be discarded or ignored during the modeling, as a way of enriching the information in the model, without increasing its size. The proposed method integrates principles of the Rough Set Theory and Stochastic Automata in the Situation-based Qualitative Modeling and Analysis method. The interplay of these three techniques allows the development of compact but precise models of complex industrial systems, and therefore enables a closer monitoring of complex real systems.