Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine
Alili Bachir 1  
,   Hafaifa Ahmed 2  
,   Iratni Abdelhamid 3  
 
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1
Gas Turbine Joint Research Team, University of Djelfa, Algeria
2
Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Algeria
3
Faculty of Science and Technology, University of Bordj Bou Arreridj, 34030 DZ, Algeria
CORRESPONDING AUTHOR
Alili Bachir   

Gas Turbine Joint Research Team, University of Djelfa, Algeria
Submission date: 2020-08-04
Final revision date: 2020-10-22
Acceptance date: 2020-10-30
Online publication date: 2020-11-17
Publication date: 2020-11-17
 
Diagnostyka 2020;21(4):67–77
 
KEYWORDS
TOPICS
ABSTRACT
The use of new technologies in modern industry improves productivity but induces complexity in the industrial system. This complexity makes it vulnerable to faults, which requires significant expense in terms of safety, reliability and availability. Hence, the diagnosis of gas turbines is a main component for making maintenance decisions for this type of machine. In this paper, the faults detection approach based on fuzzy logic is applied for the vibrations monitoring of a gas turbine, in order to monitor their operating state by including the detection and occurrence of vibration faults, thus using determined fault indicators based on the input / output variables of the examined gas turbine. In this work, the investigation results of fuzzy fault detection approach applied on gas turbine vibration are presented, based on the actual data recorded in the different gas turbine operating modes. However, analysis of the defect detection results was performed in order to determine the influence of these vibration defects on the deferent operating modes of the examined machine. This makes it possible to find the causes of failures and then to deduce the actions to follow the operational safety of the examined turbine.
FUNDING
This work is supported by the Directorate General for Scientific Research and Technological Development (DGRSDT) and was carried out at the Applied Automation and Industrial Diagnostics Laboratory and at the Gas Turbine Joint Research Team in the University of Djelfa, Algeria
 
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