Design and implementation of a generic nonconformance tracking and recovery (GINTR) system

Liu, Wei; Cheraghi, S. Hossein

doi:10.1016/j.compind.2005.11.005

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In maintenance, Chou (2009) study early cost budgeting for pavement maintenance and Cheng et al (2008) present a framework for intelligent reliability centred maintenance analysis. In fault diagnosis, Liu and Cheraghi (2006) use CBR with a generic intelligent system in design to address the non-conformance tracking and diagnosis problem.…”

Section: Cbr In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Cbr In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…One particular linkage which we are interested in pursuing is with CBR. For example, Liu and Cheraghi (2006) have demonstrated a successful hybrid approach in the related area of tracking and diagnosing quality nonconformance in the manufacturing domain, making use of CBR in conjunction with neural networks.…”

Section: Discussionmentioning

confidence: 99%

Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure

Chan

McNaught

2008

Journal of the Operational Research Society

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Intense competition and the requirement to continually drive down costs within a mature mobile telephone infrastructure market calls for new and innovative solutions to process improvement. One particular challenge is to improve the quality and reliability of the diagnostic process for systems testing of Global System for Mobile Communications and Universal Mobile Telecommunications System products. In this paper, we concentrate on a particularly important equipment type-the Base Transceiver Station (BTS). The BTS manages the radio channels and transfers signalling information to and from mobile stations (ie mobile phones). Most of the diagnostic processes are manually operated and rely heavily on individual operators and technicians' knowledge for their performance. Hence, there is a high cost associated with troubleshooting in terms of time and manpower. In this paper, we employ Bayesian networks (BNs) to model the domain knowledge that comprises the operations of the System Under Test, Automated Test Equipment (ATE), and the diagnostic skill of experienced engineers, in an attempt to enhance the efficiency and reliability of the diagnostic process. The proposed automated diagnostic tool (known as Wisdom) consists of several modules. An intelligent user interface provides possible solutions to test operators/technicians, captures their responses, and activates the automated test program. Server and client software architecture is used to integrate Wisdom with the ATE seamlessly and to maintain Wisdom as an independent module. A local area network provides the infrastructure for managing and deploying the multimedia information in real time. We describe how a diagnostic model can be developed and implemented using a BN approach. We also describe how the resulting process of diagnosis following failure, advice generation, and subsequent actions by the operator are handled interactively by the prototype system. The results from an initial survey are presented, indicating sizeable reductions in fault correction times for many fault types.

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Design and implementation of a generic nonconformance tracking and recovery (GINTR) system

Cited by 2 publications

References 18 publications

A survey of AI in operations management from 2005 to 2009

A survey of AI in operations management from 2005 to 2009

Using Bayesian networks to improve fault diagnosis during manufacturing tests of mobile telephone infrastructure

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