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A learning, representation and diagnostic methodology for engine fault diagnosis

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IEA/AIE '89: Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

Pages 824 - 830

https://doi.org/10.1145/67312.67353

Published: 01 June 1989 Publication History

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Pham DAfify A(2016)Machine-learning techniques and their applications in manufacturingProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture10.1243/095440505X32274219:5(395-412)Online publication date: 10-Aug-2016
https://doi.org/10.1243/095440505X32274
Ali M(2009)Intelligent Systems in aerospaceThe Knowledge Engineering Review10.1017/S02698889000053855:3(147-166)Online publication date: 7-Jul-2009
https://doi.org/10.1017/S0269888900005385
Ke MAli M(1991)Induction in database systems: A bibliographyApplied Intelligence10.1007/BF001190001:3(263-270)Online publication date: Nov-1991
https://doi.org/10.1007/BF00119000
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A learning, representation and diagnostic methodology for engine fault diagnosis

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Practical automotive engine fault diagnosis in an automotive service center is usually performed by analyzing the qualitative symptom descriptions provided by the vehicle owner. However, it is a non-trivial and time-consuming procedure because; (i) the ...

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IEA/AIE '89: Proceedings of the 2nd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

June 1989

590 pages

ISBN:0897913205

DOI:10.1145/67312

Editor:
A. Monnis

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 01 June 1989

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SIGAI

IEA/AIE-89: 2nd Intl Conf. on Industrial & Engineering Appl. of AI & Expert Systems

Tennessee, Tullahoma, USA

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Pham DAfify A(2016)Machine-learning techniques and their applications in manufacturingProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture10.1243/095440505X32274219:5(395-412)Online publication date: 10-Aug-2016
https://doi.org/10.1243/095440505X32274
Ali M(2009)Intelligent Systems in aerospaceThe Knowledge Engineering Review10.1017/S02698889000053855:3(147-166)Online publication date: 7-Jul-2009
https://doi.org/10.1017/S0269888900005385
Ke MAli M(1991)Induction in database systems: A bibliographyApplied Intelligence10.1007/BF001190001:3(263-270)Online publication date: Nov-1991
https://doi.org/10.1007/BF00119000
Ke MAli MMatthews M(1990)Knowledge-directed induction in a DB environmentProceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 110.1145/98784.98850(325-332)Online publication date: 1-Jun-1990
https://dl.acm.org/doi/10.1145/98784.98850

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Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

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Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis

Efficient RT-level fault diagnosis methodology

Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis

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