Utilizing multilevel models and reasoning for diagnosis of a complex electro-mechanical system

A multi-level system which utilizes both an evidential and a qualitative model for diagnosing fault symptoms in complex electro-mechanical systems is presented. The operation of both models, enhancement by a historical database, and the global control strategy are all discussed. In addition, the constraining of qualitative reasoning with information from the evidential session and the enhancement of the evidential model with information from the qualitative model is demonstrated.

[1]  Kenneth D. Forbus.  Qualitative Process Theory , 1984, Artif. Intell..

[2]  Frank J. Pipitone,et al.  The FIS Electronics Troubleshooting System , 1986, Computer.

[3]  Bruce Porter,et al.  Protos: a unified approach to concept representation, classification, and learning , 1988 .

[4]  Pamela K. Fink,et al.  Expert Systems and Diagnostic Expertise in the Mechanical and Electrical Domains , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Gautam Biswas,et al.  Applications of qualitative modeling to knowledge-based risk assessment studies , 1989, IEA/AIE '89.

[6]  Ethan A. Scarl,et al.  Diagnosis and Sensor Validation through Knowledge of Structure and Function , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Robert Milne,et al.  Strategies for Diagnosis , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Vasant Dhar,et al.  Rule-based versus structure-based models for explaining and generating expert behavior , 1987, CACM.

[9]  Benjamin J. Kaipers,et al.  Qualitative Simulation , 1989, Artif. Intell..

[10]  Kenneth D. Forbus.  Intelligent Computer-Aided Engineering , 1988, AI Mag..

[11]  Benjamin Kuipers,et al.  Commonsense Reasoning about Causality: Deriving Behavior from Structure , 1984, Artif. Intell..

[12]  Johan de Kleer,et al.  A Qualitative Physics Based on Confluences , 1984, Artif. Intell..

[13]  J ClanceyWilliam,et al.  From Guidon to Neomycin and Heracles in twenty short lessons , 1986 .

[14]  Johan de Kleer,et al.  Theories of Causal Ordering , 1986, Artif. Intell..

[15]  B. Chandrasekaran,et al.  Deep versus Compiled Knowledge Approaches to Diagnostic Problem-Solving , 1982, Int. J. Man Mach. Stud..

[16]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[17]  Gautam Biswas,et al.  Qualitative modeling in engineering applications , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[18]  Benjamin Kuipers,et al.  Qualitative Simulation , 1986, Artificial Intelligence.

[19]  Michael J. Pazzani,et al.  Failure-Driven Learning of Fault Diagnosis Heuristics , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Frank J. Pipitone,et al.  Model-Based Probabilistic Reasoning for Electronics Troubleshooting , 1983, IJCAI.

[21]  Kenneth D. Forbus.  Interpreting Observations of Physical Systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Herbert A. Simon,et al.  Causality in Device Behavior , 1989, Artif. Intell..

[23]  Michael R. Genesereth,et al.  The Use of Design Descriptions in Automated Diagnosis , 1984, Artif. Intell..

[24]  William J. Clancey,et al.  The Epistemology of a Rule-Based Expert System - A Framework for Explanation , 1981, Artif. Intell..

[25]  M. R. Herbert,et al.  An initial evaluation of the detection and diagnosis of power plant faults using a deep knowledge representation of physical behaviour , 1987 .

[26]  Gregor Kiczales,et al.  Issues in the pragmatics of qualitative modeling: lessons learned from a xerographics project , 1987, CACM.

[27]  William J. Clancey,et al.  From Guidon to Neomycin and Heracles in Twenty Short Lessons: ORN Final Report 1979-1985 , 1986, AI Mag..

[28]  Randall Davis,et al.  Reasoning from First Principles in Electronic Troubleshooting , 1983, Int. J. Man Mach. Stud..