Prediction-based diagnosis and loss prevention using model-based reasoning

Authors:
Erzsébet Németh;Rozália Lakner;Katalin M. Hangos;Ian T. Cameron
Affiliations:
Systems and Control Laboratory, Computer and Automation Research Institute, Budapest, Hungary and Department of Computer Science, University of Veszprém, Veszprém, Hungary;Department of Computer Science, University of Veszprém, Veszprém, Hungary;Systems and Control Laboratory, Computer and Automation Research Institute, Budapest, Hungary and Department of Computer Science, University of Veszprém, Veszprém, Hungary;School of Engineering, The University of Queensland, Brisbane, Australia
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 4
Cited 2

Diagnosing multiple faults

Artificial Intelligence
Readings in model-based diagnosis

Readings in model-based diagnosis
Artificial intelligence: a modern approach

Artificial intelligence: a modern approach
Diagnosis and Fault-Tolerant Control

Diagnosis and Fault-Tolerant Control

A procedure ontology for advanced diagnosis of process systems

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Multiagent realization of prediction-based diagnosis and loss prevention

IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

A diagnostic expert system established on model-based reasoning for on-line diagnosis and loss prevention is described in the paper. Its diagnostic "cause-effect" rules and possible actions (suggestions) are extracted from the results of standard HAZOP analysis. Automatic focusing as well as "What-if" type reasoning for testing hypothetical actions have been also implemented. The diagnostic system is tested on a granulator drum of a fertilizer plant in a simulation test-bed.