@article{
 title = {The use of Bayesian network modelling for maintenance planning in a manufacturing industry},
 type = {article},
 year = {2010},
 keywords = {Bayesian network modelling,Delay-time analysis,Environment,Inspection maintenance,Maintenance},
 pages = {267-277},
 volume = {95},
 websites = {http://www.sciencedirect.com/science/article/pii/S0951832009002518},
 month = {3},
 id = {599ac3d9-e791-3766-ac5b-8e946c56a269},
 created = {2015-04-11T19:52:13.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {4a6a1914-6ba6-3cdc-b1f4-f10a6e56cb6c},
 last_modified = {2017-03-14T14:27:43.598Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper has been written in order to apply Bayesian network modelling to a maintenance and inspection department. The primary aim of this paper is to establish and model the various parameters responsible for the failure rate of a system, using Bayesian network modelling, in order to apply it to a delay-time analysis study. The use of Bayesian network modelling allows certain influencing events to be considered which can affect parameters relating to the failure rate of a system. Bayesian network modelling also allows these influencing events to change and update depending on the influencing data available at any given time, thus changing the failure rate or probability of failure. A methodology has been developed and applied to a case study in order to demonstrate the process involved.},
 bibtype = {article},
 author = {Jones, B. and Jenkinson, I. and Yang, Z. and Wang, J.},
 doi = {10.1016/j.ress.2009.10.007},
 journal = {Reliability Engineering & System Safety},
 number = {3}
}

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