Industrial Applications of Knowledge-based Diagnosis
Published in October 1992
Book
Hardback
340 pages
978-0-444-89246-1 (ISBN)
Description
Diagnosis of industrial systems and processes is a crucial issue in industrial engineering. Over the last two decades, considerable investments have been made on diagnostic techniques and significant advances have been obtained in many industrial sectors, including discrete manufacturing as well as continuous production. While the application of knowledge-based system technology to diagnostic tasks has been largely investigated in the last decade and a lot of significant experience has been accumulated, its suitability to large scale industrial applications is still to some degree an open issue. Knowledge-based diagnosis has often been oriented towards advanced research issues rather than near-term applications. The aim of this book is to provide a broad overview on the application of knowledge-based diagnostic systems in industrial environments. The volume will prove an indispensable reference source for all those interested in knowledge-based systems which can explicitly represent knowledge on the task domain.
The chapters cover a variety of general problem-solving methods, reviewing problems which involve non-deterministic reasoning about empirical, unstructured, uncertain, incomplete, or qualitative knowledge. The choice of topics emphasise that integration of knowledge-based diagnosis with existing systems and working practices is still a challenging issue.
Diagnosis of industrial systems and processes is a crucial issue in industrial engineering. Over the last two decades, considerable investments have been made on diagnostic techniques and significant advances have been obtained in many industrial sectors, including discrete manufacturing as well as continuous production. While the application of knowledge-based system technology to diagnostic tasks has been largely investigated in the last decade and a lot of significant experience has been accumulated, its suitability to large scale industrial applications is still to some degree an open issue. Knowledge-based diagnosis has often been oriented towards advanced research issues rather than near-term applications. The aim of this book is to provide a broad overview on the application of knowledge-based diagnostic systems in industrial environments. The volume will prove an indispensable reference source for all those interested in knowledge-based systems which can explicitly represent knowledge on the task domain.
The chapters cover a variety of general problem-solving methods, reviewing problems which involve non-deterministic reasoning about empirical, unstructured, uncertain, incomplete, or qualitative knowledge. The choice of topics emphasise that integration of knowledge-based diagnosis with existing systems and working practices is still a challenging issue.
The chapters cover a variety of general problem-solving methods, reviewing problems which involve non-deterministic reasoning about empirical, unstructured, uncertain, incomplete, or qualitative knowledge. The choice of topics emphasise that integration of knowledge-based diagnosis with existing systems and working practices is still a challenging issue.
Diagnosis of industrial systems and processes is a crucial issue in industrial engineering. Over the last two decades, considerable investments have been made on diagnostic techniques and significant advances have been obtained in many industrial sectors, including discrete manufacturing as well as continuous production. While the application of knowledge-based system technology to diagnostic tasks has been largely investigated in the last decade and a lot of significant experience has been accumulated, its suitability to large scale industrial applications is still to some degree an open issue. Knowledge-based diagnosis has often been oriented towards advanced research issues rather than near-term applications. The aim of this book is to provide a broad overview on the application of knowledge-based diagnostic systems in industrial environments. The volume will prove an indispensable reference source for all those interested in knowledge-based systems which can explicitly represent knowledge on the task domain.
The chapters cover a variety of general problem-solving methods, reviewing problems which involve non-deterministic reasoning about empirical, unstructured, uncertain, incomplete, or qualitative knowledge. The choice of topics emphasise that integration of knowledge-based diagnosis with existing systems and working practices is still a challenging issue.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 230 mm
ISBN-13
978-0-444-89246-1 (9780444892461)
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Schweitzer Classification
Content
Parts: I. Industrial Applications and Protoypes. Re-using configuration information: High-level diagnosis of Philips P9000 computer systems (R.R. Bakker et al.). An intelligent on-line diagnostic system for cycle chemistry control in thermal power plants (M. Benini et al.). Development and testing of turbine and generator expert monitoring systems (S.M. Divakaruni, J.R. Scheibel). The cache bus experiment: Model based diagnosis applied to a real problem (K. Eshghi, C. Priest). The ARTEX project and the lessons learned for diagnosis (G. Fleischanderl, G. Friedrich). AMETHYST: The development experience (R.W. Milne). II. Tools. Evolution and adaptation of a knowledge-based shell for use in new products as embedded technology (D.L. Bodin). Real-time knowledge-based supervision for continuous and batch processing applications using COGSYS (S.J. Davison). Using the G2 diagnostic assistant for real-time fault diagnosis (F.E. Finch et al.). III. Research Trends. Developing diagnostic applications using multiple models: The role of interpretative knowledge (L. Chittaro et al.). Abductive diagnosis and its application to a mechanical troubleshooting problem (L. Console et al.). ARTIST: A methodological approach to specifying model based diagnostic systems (R. Leitch et al.). An amalgamation of model-based and heuristic reasoning for diagnosis (P. Struss).