A Comparative Study of Bayesian-Based Reliability Prediction Methodologies 2005-01-1777
As new technology is introduced into automotive engineering, the level of uncertainty regarding system robustness increases. With it reliability assessment tools that account for such uncertainty is expected to gain increased attention. This can naturally lead to Bayesian-based tools. This paper examines three reliability assessment methodologies that operate in the Bayesian framework. Two of them are geared towards electronic parts and assemblies, with the remaining one being geared towards systems in general. In doing so, they were critiqued in terms of four dimensions: (1) basic architecture, (2) input factors, (3) handling of qualitative data, and (4) failure rate updating mechanisms.
Citation: Itabashi-Campbell, R., Goel, P., and Yadav, O., "A Comparative Study of Bayesian-Based Reliability Prediction Methodologies," SAE Technical Paper 2005-01-1777, 2005, https://doi.org/10.4271/2005-01-1777. Download Citation
Author(s):
Rachel Itabashi-Campbell, Parveen S. Goel, Om P. Yadav
Affiliated:
TRW Automotive Steering & Suspension Systems
Pages: 11
Event:
SAE 2005 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Also in:
Reliability and Robust Design in Automotive Engineering 2005-SP-1956
Related Topics:
Reliability
Gears
Tools and equipment
Parts
Architecture
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