The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
Suhayya Abu-hakima - One of the best experts on this subject based on the ideXlab platform.
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Canadian Conference on AI - Automating Model Acquisition by Fault Knowledge Re-Use: Introducing the Diagnostic Remodeler Algorithm
Lecture Notes in Computer Science, 1996Co-Authors: Suhayya Abu-hakimaAbstract:The paper addresses the problem of automated model acquisition through the re-use of fault knowledge. The Diagnostic Remodeler (DR) algorithm has been implemented for the automated generation of behavioural component models with an explicit representation of function by re-using fault-based knowledge. DR re-uses as its first application the fault knowledge of the Jet Engine Troubleshooting Assistant (JETA). DR extracts a model of the Main Fuel System using real-world engine fault knowledge and two types of background knowledge as input: device dependent and device independent background knowledge. To demonstrate DR's generality, it has also been applied to a coffee maker fault knowledge base to extract the component models of a full coffee device.
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IEA/AIE (Vol. 1) - Intelligent Troubleshooting of complex machinery
Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA AIE ', 1990Co-Authors: Philippe L. Davidson, Mike Halasz, Sieu Phan, Suhayya Abu-hakimaAbstract:Proper maintenance and Troubleshooting of complex mechanical equipment is a difficult task. A large amount of information, such as sensor data and previous repair actions, is available but infrequently used for interpretation by technical staff which is continually losing expertise due to turnover. Well structured knowledge-based systems can provide effective techniques for assisting in this task. A set of functional requirements for a Troubleshooting advisory system is presented. Domain knowledge should be structured to make the system's operation more explicit to the knowledge engineer or end user. Reasoning must be carried out by navigating through a diagnostic network constructed from maintenance procedures and heuristics. The generic and modular approach used has the distinct advantage of making the task of creating diagnostic knowledge-bases easier. In addition, the large amount of information which is normally disseminated through many maintenance manuals can be easily accessible in a single computer environment, which in itself represents considerable savings in time for a mechanic. A generic system, called JETA (Jet Engine Troubleshooting Assistant) has been developed within this framework. In particular, JETA has been applied to troubleshoot the General Electric J85-CAN-15 jet engine that powers the CF-5 trainer fighters used by the Canadian Air Force.
M.w. Swartwout - One of the best experts on this subject based on the ideXlab platform.
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The CANASTA experience: key management and technical decisions in a hybrid expert system project
[1991] Proceedings of the IEEE ACM International Conference on Developing and Managing Expert System Programs, 1991Co-Authors: A. Rewari, M.w. SwartwoutAbstract:A case study is presented of a successful intelligent system. CANASTA (the Crash Analysis Troubleshooting Assistant) is a knowledge-based system designed to assist support engineers in isolating the underlying causes of operating system crashes, whether due to hardware faults or system software bugs. CANASTA's success is largely due to a combination of project management actions and the innovative technical design and development of the system. The lessons learned from developing a large hybrid system such as CANASTA are presented.
Philippe L. Davidson - One of the best experts on this subject based on the ideXlab platform.
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IEA/AIE (Vol. 1) - Intelligent Troubleshooting of complex machinery
Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA AIE ', 1990Co-Authors: Philippe L. Davidson, Mike Halasz, Sieu Phan, Suhayya Abu-hakimaAbstract:Proper maintenance and Troubleshooting of complex mechanical equipment is a difficult task. A large amount of information, such as sensor data and previous repair actions, is available but infrequently used for interpretation by technical staff which is continually losing expertise due to turnover. Well structured knowledge-based systems can provide effective techniques for assisting in this task. A set of functional requirements for a Troubleshooting advisory system is presented. Domain knowledge should be structured to make the system's operation more explicit to the knowledge engineer or end user. Reasoning must be carried out by navigating through a diagnostic network constructed from maintenance procedures and heuristics. The generic and modular approach used has the distinct advantage of making the task of creating diagnostic knowledge-bases easier. In addition, the large amount of information which is normally disseminated through many maintenance manuals can be easily accessible in a single computer environment, which in itself represents considerable savings in time for a mechanic. A generic system, called JETA (Jet Engine Troubleshooting Assistant) has been developed within this framework. In particular, JETA has been applied to troubleshoot the General Electric J85-CAN-15 jet engine that powers the CF-5 trainer fighters used by the Canadian Air Force.
A. Rewari - One of the best experts on this subject based on the ideXlab platform.
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The CANASTA experience: key management and technical decisions in a hybrid expert system project
[1991] Proceedings of the IEEE ACM International Conference on Developing and Managing Expert System Programs, 1991Co-Authors: A. Rewari, M.w. SwartwoutAbstract:A case study is presented of a successful intelligent system. CANASTA (the Crash Analysis Troubleshooting Assistant) is a knowledge-based system designed to assist support engineers in isolating the underlying causes of operating system crashes, whether due to hardware faults or system software bugs. CANASTA's success is largely due to a combination of project management actions and the innovative technical design and development of the system. The lessons learned from developing a large hybrid system such as CANASTA are presented.
Mike Halasz - One of the best experts on this subject based on the ideXlab platform.
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IEA/AIE (Vol. 1) - Intelligent Troubleshooting of complex machinery
Proceedings of the third international conference on Industrial and engineering applications of artificial intelligence and expert systems - IEA AIE ', 1990Co-Authors: Philippe L. Davidson, Mike Halasz, Sieu Phan, Suhayya Abu-hakimaAbstract:Proper maintenance and Troubleshooting of complex mechanical equipment is a difficult task. A large amount of information, such as sensor data and previous repair actions, is available but infrequently used for interpretation by technical staff which is continually losing expertise due to turnover. Well structured knowledge-based systems can provide effective techniques for assisting in this task. A set of functional requirements for a Troubleshooting advisory system is presented. Domain knowledge should be structured to make the system's operation more explicit to the knowledge engineer or end user. Reasoning must be carried out by navigating through a diagnostic network constructed from maintenance procedures and heuristics. The generic and modular approach used has the distinct advantage of making the task of creating diagnostic knowledge-bases easier. In addition, the large amount of information which is normally disseminated through many maintenance manuals can be easily accessible in a single computer environment, which in itself represents considerable savings in time for a mechanic. A generic system, called JETA (Jet Engine Troubleshooting Assistant) has been developed within this framework. In particular, JETA has been applied to troubleshoot the General Electric J85-CAN-15 jet engine that powers the CF-5 trainer fighters used by the Canadian Air Force.
