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Subjective priors for maintenance models

Subjective priors for maintenance models Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we gather data to learn about these parameters. However, such data are often difficult to collect for maintenance applications, leading to poor parameter estimates and incorrect decisions. A subjective modelling approach can resolve this problem, but requires us to specify suitable prior distributions for the unknown parameters. This paper considers which priors to adopt for common maintenance models and describes the method of predictive elicitation for determining unknown hyperparameters associated with these prior distributions. We discuss the computational difficulties of this approach and consider numerical methods for resolving this problem. Finally, we present practical demonstrations to illustrate the potential benefits of predictive elicitation and subjective analysis. This work provides a major step forward in making the methods of subjective Bayesian inference available to maintenance decision makers in practice. Practical implications . This paper recommends powerful strategies for expressing subjective knowledge about unknown model parameters, in the context of maintenance applications that involve making decisions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Quality in Maintenance Engineering Emerald Publishing

Subjective priors for maintenance models

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
1355-2511
DOI
10.1108/13552510410553271
Publisher site
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Abstract

Successful strategies for maintenance require good decisions and we commonly use stochastic reliability models to help this process. These models involve unknown parameters, so we gather data to learn about these parameters. However, such data are often difficult to collect for maintenance applications, leading to poor parameter estimates and incorrect decisions. A subjective modelling approach can resolve this problem, but requires us to specify suitable prior distributions for the unknown parameters. This paper considers which priors to adopt for common maintenance models and describes the method of predictive elicitation for determining unknown hyperparameters associated with these prior distributions. We discuss the computational difficulties of this approach and consider numerical methods for resolving this problem. Finally, we present practical demonstrations to illustrate the potential benefits of predictive elicitation and subjective analysis. This work provides a major step forward in making the methods of subjective Bayesian inference available to maintenance decision makers in practice. Practical implications . This paper recommends powerful strategies for expressing subjective knowledge about unknown model parameters, in the context of maintenance applications that involve making decisions.

Journal

Journal of Quality in Maintenance Engineering – Emerald Publishing

Published: Sep 1, 2004

Keywords: Maintenance; Modelling; Predictive process

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