@ARTICLE{Lakehal_Abdelaziz_Development_2019,
 author={Lakehal, Abdelaziz and Nahal, Mourad and Harouz, Riad},
 volume={vol. 10},
 number={No 2},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2019},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By identifying combinations of faults in a logical framework it’s possible to define the structure of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic tools is in their ability to reasoning under uncertainty. Some typical constraints to the fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper shows that information processing has become simple and easy through the use of Bayesian networks. The study presented showed that updating knowledge and exploiting new knowledge does not complicate calculations. The contribution is the structural approach of faults diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are defined in descending order. The approach presented in this paper has been successfully applied to turbo compressor, which represent vital equipment in petrochemical plant.},
 title={Development and application of a decision making tool for fault diagnosis of turbocompressor basedon Bayesian network and fault tree},
 URL={http://journals.pan.pl/Content/113084/PDF/2-Lakehal.pdf},
 doi={10.24425/mper.2019.129565},
 keywords={plant maintenance, prioritization, Bayesian networks, fault trees, diagnosis, turbo-compressor},
}