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

A Chan and K R McNaught ()
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A Chan: Test Systems Engineering, Motorola
K R McNaught: Cranfield University

Journal of the Operational Research Society, 2008, vol. 59, issue 4, 423-430

Abstract: Abstract 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.

Keywords: Bayesian network; fault diagnosis; manufacturing; intelligent system; intelligent user interface; decision support system (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/palgrave.jors.2602388

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