Authors: Jason Matthew Aughenbaugh, Jeffrey W. Herrmann
Addresses: Applied Research Laboratories, University of Texas at Austin, Austin, TX 78713, USA. ' Department of Mechanical Engineering and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA
Abstract: Reliability estimates are useful for making design decisions. We consider the case where a designer must choose between an existing component whose reliability is well-established and a new component that has an unknown reliability. This paper compares the statistical approaches for updating reliability assessments based on additional simulation or experimental data. We consider four statistical approaches for modelling the uncertainty about a new component|s failure probability: a classical approach, a precise Bayesian approach, a robust Bayesian approach and an imprecise probability approach. We show that an imprecise beta model is compatible with both the robust Bayesian approach and the imprecise probability approach. The different approaches for forming and updating the designer|s beliefs about the product reliability are illustrated and compared under different scenarios of available information. The goal is to gain insight into the relative strengths and weaknesses of the approaches. Examples are presented for illustrating the conclusions.
Keywords: sampling theory; robust Bayesian; imprecise probability; reliability assessment; uncertainty; information; statistical testing; Bayesian; reliability estimates; design decisions; designer beliefs; product reliability.
International Journal of Reliability and Safety, 2008 Vol.2 No.4, pp.265 - 285
Available online: 17 Dec 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article