Abstract

The centrifugal compressors (CC) are considered the main pillars of power production units, as their damage leads to the shutdown of the entire unit, which causes great material losses as decrease in production, an increase in maintenance expenses, and others. Therefore, good monitoring and continuous maintenance of the centrifugal compressors may increase the reliability of the system and the continuity of its work permanently, however, this does not make it free from
defects and risks that cause damage to some parts such as bearings, shaft, ...etc. Therefore, a thorough examination and diagnosis of compressor components helps a lot to protect them from various defects and increase their life cycle. In this paper, We studied two different cases of two compressors, one of which was subjected to vibrations and the other to high temperatures
in the bearings at AL-MERK and BRN units in Algeria. where the unit was completely stopped to fix this defect, this made us think about how to know the main causes of this malfunction. Where we adopted in this paper a hybrid approach, which is a methodology that evaluates the reliability of the system by combining two methods, one traditional and the second based on artificial intelligence, The first is FTA to identify risks and find out the type of defects that cause the main failure, and Bayesian Network relies on artificial intelligence to know the extent of the
impact of each event on the system (quantitative analysis), where this study helps in analyzing the system and predicting all the causes that would impede the continuity of the compressors
work, Through pre-stored data collected by maintenance experts and monitoring workers, relying on a Bayesian network to identify the fragile components in the system and the most influential ones and calculate the extent of their impact on the system. Finally, by performing this analysis, we found that vibration is the main cause of compressor failure.

Information

Item Type:	Conference
Divisions:	» Laboratory of Research on Electromechanical and Dependability » Faculty of Science and Technology
ePrint ID:	4714
Date Deposited:	2023-12-15
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4714

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