@misc{Amrani_Mohamed_Fault,
 author={Amrani, Mohamed and Benazzouz, Djamel},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={This paper presents a new approach of diagnosis and prognostic in real-time of strategic equipment of pharmaceutical industry. This approach is developed using Bayesian network (BN) which consider industrial data and feedback experience. The objective is to detect, locate and prevent any malfunction of the air compressor (oil-free) without air contamination, dedicated to pharmaceutical industry. The study is based on the functional analysis of the air compressor to obtain the fault tree (FT). This FT is transformed into BN to diagnose automatically the compressor and prevent any malfunctioning.},
 type={artykuł},
 title={Fault prediction of pharmaceutical air compressor using the intelligent model based on the Bayesian network},
 keywords={pharmaceutical standard requirements, air compressor (oil free), artificial intelligence, Bayesian networks, industrial diagnosis and prognostic, fault tree},
}