Work

Fault Prediction Of Pharmaceutical Air Compressor Using The Intelligent Model Based On The Bayesian Network

Year: 2025

Type: article

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, BEKER Laboratories (Dar El Beida-Algeria). 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.

Source: International Journal of Applied Mechanics and Engineering

DOI: 10.59441/ijame/195999

Authors: Mohamed AMRANI, Djamel BENAZZOUZ

Institution: University of Boumerdes

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Good health and well-being

Open Access status: gold

Estimated APC paid: $200