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Details of Grant 

EPSRC Reference: GR/L88801/01
Title: CAUSAL EXPLANATION OF DIAGNOSTIC FINDINGS IN MODEL- BASED DIAGNOSIS OF DYNAMIC SYSTEMS
Principal Investigator: Shen, Professor Q
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Intelligent Applications Ltd
Department: Sch of Informatics
Organisation: University of Edinburgh
Scheme: Standard Research (Pre-FEC)
Starts: 01 March 1998 Ends: 31 August 1999 Value (£): 51,123
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
Process supervision becomes increasingly important as a major factor affecting the efficiency of the manufacturing industries. An important problem in process supervision is the diagnosis of faults affecting the normal operation of the plant. Model-based diagnosis (MBD) allows automatically locating faults by reasoning about explicit models of the system under diagnosis. This approach offers many advantages over the alternative of relying on a rule base of anticipated or previously encountered malfunctions. Whilst much research has been done on the development of the diagnostic techniques, the area of explanation in MBD has been largely unexplored, especially in diagnosing dynamic systems containing feedback loops. This research aim to develop a formal causal explanation mechanism which will allow the generation of explanations of the findings returned by an MBD system working in dynamic domains. The generated explanations will achieve two design requirements: a) offering a meaningful causal interpretation of the found fault, explaining how and why and not just what fault model reflects the observed faulty behaviour of the system, and b) presenting information in a format that is comprehensible to the human user. The resulting self-explanatory fault diagnostic technique will significantly increase the effectiveness and efficiency of process supervision in industrial applications.
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