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Risk Analysis of the Space Shuttle : Pre-Challenger Bayesian Prediction of Failure NASA Space Systems Engineering & Risk Management Symposium
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kelly, Dana L. Smith, Curtis L. |
| Copyright Year | 2008 |
| Abstract | Dalal et al performed a statistical analysis of field and nozzle O-ring data collected prior to the ill-fated launch of the Challenger in January 1986. The purpose of their analysis was to show how statistical analysis could be used to provide information to decisionmakers prior to the launch, information that could have been expected to lead to a decision to abort the launch due to the low temperatures (~30 F.) present at the launch pad on the morning of the scheduled launch. Dalal et al. performed a frequentist analysis of the O-ring data, and found that a logistic regression model provided a relatively good fit to the past data. In the second portion of their paper, Dalal et al. propagated parameter uncertainties through the fitted logistic regression model in order to estimate the probability of shuttle failure due to O-ring failure at the estimated launch temperature of ~30 F. Because their analysis was frequentist in nature, probability distributions representing epistemic uncertainty in the input parameters were not available, and the authors had to resort to an approximate approach based on bootstrap confidence intervals. In this paper, we will re-evaluate the analyses of Dalal et al. from a Bayesian perspective. Markov chain Monte Carlo (MCMC) sampling will be used to sample from the joint posterior distribution of the model parameters, and to sample from the posterior predictive distributions at the estimated launch temperature, a temperature that had not been observed in prior launches of the space shuttle. Uncertainties, which are represented by probability distributions in the Bayesian approach, are propagated through the model to obtain a probability distribution for O-ring failure, and subsequently for shuttle failure as a result of O-ring failure. No approximations are required in the Bayesian approach and the resulting distributions can be input to a decision analysis to obtain expected utility for the decision to launch. When using a mathematical model, careful attention must be given to uncertainties in the model. Richard Feynman |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://digital.library.unt.edu/ark:/67531/metadc895441/m2/1/high_res_d/926328.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
