Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/emerald-publishing/bayesian-networks-in-manufacturing-4JWkaq0riJ
References (40)
-
E. Wolbrecht, B. D'Ambrosio, R. Paasch, D. Kirby (2000)
Monitoring and diagnosis of a multistage manufacturing process using Bayesian networksArtificial Intelligence for Engineering Design, Analysis and Manufacturing, 14
-
A. Cannata, M. Gerosa, M. Taisch (2008)
SOCRADES: A framework for developing intelligent systems in manufacturing2008 IEEE International Conference on Industrial Engineering and Engineering Management
-
A. Muller, M. Suhner, B. Iung (2008)
Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial systemReliab. Eng. Syst. Saf., 93
-
S. Lauritzen, D. Spiegelhalter (1990)
Local computations with probabilities on graphical structures and their application to expert systemsJournal of the royal statistical society series b-methodological, 50
-
H. Langseth, L. Portinale (2014)
Bayesian networks in reliabilityReliab. Eng. Syst. Saf., 92
-
R. Barco, V. Wille, L. Díez (2005)
System for automated diagnosis in cellular networks based on performance indicatorsEur. Trans. Telecommun., 16
-
Satyabrata Pradhan, Rajveer Singh, Komal Kachru, S. Narasimhamurthy (2007)
A Bayesian Network Based Approach for Root-Cause-Analysis in Manufacturing Process2007 International Conference on Computational Intelligence and Security (CIS 2007)
-
10.1002/9780470994559
-
Hei-Chia Wang, H. Wang (2005)
A hybrid expert system for equipment failure analysisExpert Syst. Appl., 28
-
C. Romessis, K. Mathioudakis (2004)
Bayesian Network Approach for Gas Path Fault DiagnosisJournal of Engineering for Gas Turbines and Power-transactions of The Asme, 128
-
10.1016/B978-0-08-051489-5.50008-4
-
K. McNaught, A. Zagorecki (2009)
Using dynamic Bayesian networks for prognostic modelling to inform maintenance decision making2009 IEEE International Conference on Industrial Engineering and Engineering Management
-
O. Pourret, Patrick. Naïm, B. Marcot (2008)
Bayesian networks : a practical guide to applications -
Ronald Anderson, G. Vastag (2004)
Causal modeling alternatives in operations research: Overview and applicationEur. J. Oper. Res., 156
-
Eduardo Gilabert, A. Arnaiz (2006)
Intelligent automation systems for predictive maintenance: A case studyRobotics and Computer-integrated Manufacturing, 22
-
K. Kobbacy, S. Vadera (2011)
A survey of AI in operations management from 2005 to 2009Journal of Manufacturing Technology Management, 22
-
M. Correa, C. Bielza, J. Pamies-Teixeira (2009)
Comparison of Bayesian networks and artificial neural networks for quality detection in a machining processExpert Syst. Appl., 36
-
M. Sanseverino, Fulvio Cascio (1997)
Model-Based Diagnosis for Automotive RepairIEEE Expert, 12
-
Z. Ok, J. Isaacs, J. Benneyan (2008)
Probabilistic and Monte Carlo risk models for carbon nanomaterial production processes2008 IEEE International Symposium on Electronics and the Environment
-
Yue Wang, M. Tseng (2008)
Defining Specifications for Custom Products: A Bayesian Probabilistic Approach, 5
-
B. Jones, I. Jenkinson, Zaili Yang, Jin Wang (2010)
The use of Bayesian network modelling for maintenance planning in a manufacturing industryReliab. Eng. Syst. Saf., 95
-
S. Du, L. Xi (2007)
Faults diagnosis based on system model in a discrete-part machining system2007 IEEE International Conference on Industrial Engineering and Engineering Management
-
M. Bouissou, O. Pourret (2003)
A BAYESIAN BELIEF NETWORK BASED METHOD FOR PERFORMANCE EVALUATION AND TROUBLESHOOTING OF MULTISTATE SYSTEMSInternational Journal of Reliability, Quality and Safety Engineering, 10
-
K. Kobbacy, S. Vadera (1997)
European Conference on Intelligent Management Systems in OperationsJournal of the Operational Research Society, 55
-
K. Korb, A. Nicholson (2004)
Bayesian Artificial Intelligence -
K. Kobbacy, S. (eds.) Vadera
Intelligent management systems in operations -
Yi Lu, T. Chen, Brennan Hamilton (2000)
A fuzzy system for automotive fault diagnosis: fast rule generation and self-tuningIEEE Trans. Veh. Technol., 49
-
10.1201/9780203491294
-
J. Garcia, R. Morales, P. Miyagi (2008)
Supervisory system for hybrid productive systems based on Bayesian networks and OO-DPT Nets2008 IEEE International Conference on Emerging Technologies and Factory Automation
-
Sean Eom, E. Kim (2006)
A survey of decision support system applications (1995–2001)Journal of the Operational Research Society, 57
-
Yinhua Liu, Sun Jin (2009)
Bayesian Network Approach for Dimensional Variation Diagnosis in Assembly Process -
S. Srinivas (1993)
A Generalization of the Noisy-Or Model -
N. Masruroh, K. Poh (2007)
A Bayesian network approach to job-shop rescheduling2007 IEEE International Conference on Industrial Engineering and Engineering Management
-
J. Pearl (1991)
Probabilistic reasoning in intelligent systems - networks of plausible inference -
S. Gebus, E. Juuso, K. Leiviskä (2009)
Knowledge-based linguistic equations for defect detection through functional testing of printed circuit boardsExpert Syst. Appl., 36
-
Yoseba Lab, Pablo Lab, Argoitz Zabala (2008)
Advanced fault prediction in high-precision foundry production2008 6th IEEE International Conference on Industrial Informatics
-
R. Lewis, R. Ransing (1997)
A semantically constrained Bayesian network for manufacturing diagnosisInternational Journal of Production Research, 35
-
N. Proudlove, S. Vadera, K. Kobbacy (1998)
Intelligent management systems in operations: a reviewJournal of the Operational Research Society, 49
-
10.1109/IWISA.2009.5072768
-
Xinghua Fan, Feng Hu, Simon Yang (2007)
Integrated decision-support system for diagnosis, maintenance planning, and scheduling of manufacturing systemsInternational Journal of Production Research, 45
- Publisher
- Emerald Publishing
- Copyright
- Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
- ISSN
- 1741-038X
- DOI
- 10.1108/17410381111149611
- Publisher site
- See Article on Publisher Site
Abstract
Purpose – The purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing. Design/methodology/approach – The intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators. Findings – Surveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators. Originality/value – Such efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators.
Journal
Journal of Manufacturing Technology Management – Emerald Publishing
Published: Jul 26, 2011
Keywords: Bayesian network; Manufacturing; Fault diagnosis; System testing; Intelligent user interface; Decision support; Intelligent manufacturing systems; Mobile communication systems