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Reliability data collection and analysis system

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4 Author(s)
Groen, G.J. ; Maryland Univ., College Park, MD, USA ; Siyuan Jiang ; Mosleh, A. ; Droguett, E.L.

This paper presents ReDCAS, the reliability data collection and analysis system. ReDCAS is a software tool for reliability data collection and analysis developed for Ford Motor Company. The software employs Bayesian data analysis techniques to estimate reliability measures based on warranty data, test data, and engineering judgments regarding the impact of design changes on the reliability. The software was developed over the period 1995-1999, and has been used to perform reliability assessments for products under development. ReDCAS provides a working environment for engineers provide engineers to incorporate reliability considerations into the design or redesign of products, even though data on the actual product under design is lacking or absent. This is achieved by basing reliability assessments on data available for different, yet similar products. By considering that these products will typically have similar reliability characteristics, this data can be considered (partially) relevant to the estimation of the new product's reliability characteristics. Incorporation of the data into the assessment requires that corrections are made based on the anticipated reliability impact of design modifications. Similarly, prototype life test data is incorporated into reliability assessments, considering the effectiveness of the design corrective action to those failures observed during the tests. Analyses are performed using Bayesian data analysis procedures. The reliability behavior is modeled via the Weibull and increasing decreasing bathtub (IDB) models. The latter model is a three parameter model capable of representing bathtub-shaped failure rate functions. The procedures provide reliability estimates in the form of reliability and failure rate estimates, plus associated uncertainties.

Published in:

Reliability and Maintainability, 2004 Annual Symposium - RAMS

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