Autor:

Amrani, Mohamed ; Benazzouz, Djamel

Współtwórca:

Jurczak, Paweł - red.

Tytuł:

Fault prediction of pharmaceutical air compressor using the intelligent model based on the Bayesian network

Tytuł publikacji grupowej:

IJAME, volume 30 (2025)

Temat i słowa kluczowe:

pharmaceutical standard requirements ; air compressor (oil free) ; artificial intelligence ; Bayesian networks ; industrial diagnosis and prognostic ; fault tree

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2025

Typ zasobu:

artykuł

Format:

application/pdf

DOI:

10.59441/ijame/195999

Strony:

20-30

Źródło:

IJAME, volume 30, number 2 (2025)

Jezyk:

eng

Licencja:

CC 4.0

Licencja CC BY-NC-ND 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego