This paper introduces a new method to learn the probabilities defining a BBNs from databases with missing data. The intuition behind this method is close to the robust sensitivity analysis interpretation of probability; the method computes the extreme points of the set of possible distributions cons
Guidelines for corrective replacement based on low stochastic structure assumptions
โ Scribed by F. P. A. Coolen; M. J. Newby
- Book ID
- 101292061
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 114 KB
- Volume
- 13
- Category
- Article
- ISSN
- 0748-8017
No coin nor oath required. For personal study only.
โฆ Synopsis
This paper presents corrective replacement decisions, e.g. for machines in a production process or other technical systems. In an attempt to base decisions on observed failure times only, some guidelines are provided for replacing failed machines. The method does not provide an optimal strategy in all situations, indicating that sometimes more information or assumptions are needed. The optimal policy indicates how to act if the low assumptions model recommends action. If the model does not strongly indicate an action, more data need to be collected or more sophisticated modelling is needed. Further modelling would require additional assumptions or input from expert judgements, and could be an expensive exercise. A method that gives clear guidelines if the data are strongly indicative may save time and money. This paper presents the model in an elementary form and is intended as a first step towards modelling more realistic maintenance situations.
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