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A semantically constrained Bayesian network for manufacturing diagnosis

W. R., Ransing R.S., Rajesh Ransing Orcid Logo

International Journal of Production Research, Volume: 35, Issue: 8, Pages: 2171 - 2188

Swansea University Author: Rajesh Ransing Orcid Logo

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DOI (Published version): 10.1080/002075497194796

Abstract

The diagnostic problem is posed as recognizing patterns in rejection data and thesubsequent mapping to causes. A new network architecture has been proposedwhich should overcome many of the disadvantages of the existing diagnostictools. The network is based on the authors’ earlier work (Ransing et al...

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Published in: International Journal of Production Research
ISSN: 0020-7543 1366-588X
Published: Informa UK Limited 1997
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URI: https://cronfa.swan.ac.uk/Record/cronfa24534
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spelling 2019-05-30T10:57:54.5708710 v2 24534 2015-11-19 A semantically constrained Bayesian network for manufacturing diagnosis 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false 2015-11-19 MECH The diagnostic problem is posed as recognizing patterns in rejection data and thesubsequent mapping to causes. A new network architecture has been proposedwhich should overcome many of the disadvantages of the existing diagnostictools. The network is based on the authors’ earlier work (Ransing et al. 1995)on representing the causal relationship in the defect-metacause-rootcause form.Although the algorithm is based on the Bayesian analysis, many of the laws ofprobability have been altered to suit the complexities involved. For example, thenotion of conditional probability has been generalized to enable the belief revisioneven in the presence of partial evidence. The inherent presence of the degree ofignorance or uncertainty in the quanti® cation of a relationship has also beenconsidered. Rigorous constraints, again based on the laws of probability, havebeen developed to check the consistency among the network values. The networkis required to be initialized with only a few values or the range for the same andthen a set of globally consistent values is generated automatically and e ciently.Using the most suitable set of consistent values, the diagnosis is performed usingthe generalized Bayesian analysis. The network has been tested for a pressure diecasting process, however, it is generic in nature and can also be applied to othermanufacturing processes. Journal Article International Journal of Production Research 35 8 2171 2188 Informa UK Limited 0020-7543 1366-588X 7Epsilon, Casting Process, ISO9001:2015, Knowledge Discovery, Knowledge Representation, Process Improvement, Six Sigma, Total Quality Management 31 8 1997 1997-08-31 10.1080/002075497194796 @articleLewis_1997,doi = 10.1080/002075497194796,url = http://dx.doi.org/10.1080/002075497194796,year = 1997,month = aug,publisher = Informa UK Limited,volume = 35,number = 8,pages = 2171--2188,author = R. W. Lewis and R.S. Ransing,title = A semantically constrained Bayesian network for manufacturing diagnosis,journal = International Journal of Production Research COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2019-05-30T10:57:54.5708710 2015-11-19T09:38:18.6362287 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering W. R. 1 Ransing R.S. 2 Rajesh Ransing 0000-0003-4848-4545 3
title A semantically constrained Bayesian network for manufacturing diagnosis
spellingShingle A semantically constrained Bayesian network for manufacturing diagnosis
Rajesh Ransing
title_short A semantically constrained Bayesian network for manufacturing diagnosis
title_full A semantically constrained Bayesian network for manufacturing diagnosis
title_fullStr A semantically constrained Bayesian network for manufacturing diagnosis
title_full_unstemmed A semantically constrained Bayesian network for manufacturing diagnosis
title_sort A semantically constrained Bayesian network for manufacturing diagnosis
author_id_str_mv 0136f9a20abec3819b54088d9647c39f
author_id_fullname_str_mv 0136f9a20abec3819b54088d9647c39f_***_Rajesh Ransing
author Rajesh Ransing
author2 W. R.
Ransing R.S.
Rajesh Ransing
format Journal article
container_title International Journal of Production Research
container_volume 35
container_issue 8
container_start_page 2171
publishDate 1997
institution Swansea University
issn 0020-7543
1366-588X
doi_str_mv 10.1080/002075497194796
publisher Informa UK Limited
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 0
active_str 0
description The diagnostic problem is posed as recognizing patterns in rejection data and thesubsequent mapping to causes. A new network architecture has been proposedwhich should overcome many of the disadvantages of the existing diagnostictools. The network is based on the authors’ earlier work (Ransing et al. 1995)on representing the causal relationship in the defect-metacause-rootcause form.Although the algorithm is based on the Bayesian analysis, many of the laws ofprobability have been altered to suit the complexities involved. For example, thenotion of conditional probability has been generalized to enable the belief revisioneven in the presence of partial evidence. The inherent presence of the degree ofignorance or uncertainty in the quanti® cation of a relationship has also beenconsidered. Rigorous constraints, again based on the laws of probability, havebeen developed to check the consistency among the network values. The networkis required to be initialized with only a few values or the range for the same andthen a set of globally consistent values is generated automatically and e ciently.Using the most suitable set of consistent values, the diagnosis is performed usingthe generalized Bayesian analysis. The network has been tested for a pressure diecasting process, however, it is generic in nature and can also be applied to othermanufacturing processes.
published_date 1997-08-31T03:29:08Z
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score 11.014358

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