Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Learning to enhance reliability of electronic systems through effective modeling and risk assessment

Walls, L.A. and Quigley, J.L. and , IEST (2000) Learning to enhance reliability of electronic systems through effective modeling and risk assessment. In: Annual reliability and maintainability symposium - 2000 proceedings, 1900-01-01.

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Now that electronic components have demonstrated high reliability, attention has centered upon enhancing the reliability of electronic systems. We introduce a modeling framework to support decision-making during electronic systems design with a view to enhancing operational reliability. We differentiate our work from those models that seek only to provide reliability predictions. Our premise is that modeling can be used to give a better understanding of the impact of engineering decisions on those factors affecting reliability. Through modeling, the decision-maker is encouraged to reflect upon the consequences of actions to learn how a design might be enhanced. The model formulation and data management processes are described for an assumed evolutionary design process. Bayesian approaches are used to combine data types and sources. Exploratory data analysis identifies those factors affecting operational reliability. Expert knowledge is elicited to assess how these factors might impact upon proposed designs. Statistical inference procedures are used to support an assessment of risks associated with design decisions. Applications to the design of electronic systems for aircraft illustrate the usefulness of the model. On-going research is being conducted to fully evaluate the proposed approach.

Item type: Conference or Workshop Item (Paper)
ID code: 18301
Keywords: reliability model, Bayes method, data analysis, expert opinion, aerospace, electronic equipment, Management. Industrial Management
Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
Department: Strathclyde Business School > Management Science
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 31 Mar 2010 14:52
    Last modified: 04 Oct 2012 17:33
    URI: http://strathprints.strath.ac.uk/id/eprint/18301

    Actions (login required)

    View Item