Name
Abstract | ||
|---|---|---|
Many imperfect maintenance models have been developed to mathematically characterize the efficiency of maintenance activity from various points of view. However, the adequacy of an imperfect maintenance model must be validated before it is used in decision making. The most adequate imperfect maintenance model among the candidates to facilitate decision making is also desired. The contributions of this paper lie in three aspects: 1 it proposes an approach to conducting a goodness-of-flt test, 2 it introduces a Bayesian approach to selecting the most adequate model among several competitive candidates, and 3 it develops a framework that incorporates the model selection results into the preventive maintenance decision making. The effectiveness of the proposed methods is demonstrated by three designed numerical studies. The case studies show that the proposed methods are able to identify the most adequate model from the competitive candidates, and incorporating the model selection results into the maintenance decision model achieves better estimation for applications with limited data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TR.2011.2170252 | IEEE Transactions on Reliability |
Keywords | Field | DocType |
bayesian model selection,decision making,goodness-of-flt test,data-driven approach,bootstrap sampling,maintenance decision model,goodness-of-fit,imperfect maintenance model,u-pooling method,preventive maintenance,bayesian approach,imperfect maintenance models,optical fiber,data model,optical fibers,mathematical model,computer model,goodness of fit,data models,computational modeling | Data modeling,Imperfect,Data-driven,Model selection,Decision model,Statistics,Goodness of fit,Reliability engineering,Preventive maintenance,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
61 | 1 | 0018-9529 |
Citations | PageRank | References |
3 | 0.44 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 190 | 19.09 |
| Hong-Zhong Huang | 2 | 583 | 58.24 |
| Xiaoling Zhang | 3 | 6 | 0.90 |