Creator:

Amrani, Mohamed ; Benazzouz, Djamel

Contributor:

Jurczak, Paweł - red.

Title:

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

Group publication title:

IJAME, volume 30 (2025)

Subject and Keywords:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2025

Resource Type:

artykuł

Format:

application/pdf

DOI:

10.59441/ijame/195999

Pages:

20-30

Source:

IJAME, volume 30, number 2 (2025)

Language:

eng

License:

CC 4.0

License CC BY-NC-ND 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego