Detection and Modeling Vibrational Behavior of a Gas Turbine Based on Dynamic Neural Networks Approach

During the gas turbine exploitation the presence of small defects can cause very high vibration amplifications, localized on the components of this rotating machine. For this, a diagnostic process is necessary for decision-making during the monitoring of failures caused by vibration phenomena, which...

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Bibliographic Details
Published inStrojnícky casopis - Journal of Mechanical Engineering Vol. 68; no. 3; pp. 143 - 166
Main Authors Benrahmoune, Mohamed, Ahmed, Hafaifa, Mouloud, Guemana, XiaoQi, Chen
Format Journal Article
LanguageEnglish
Published Sciendo 01.11.2018
Subjects
dynamic neural networks
Gas turbine
model based faults detection
vibrations monitoring
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Summary:During the gas turbine exploitation the presence of small defects can cause very high vibration amplifications, localized on the components of this rotating machine. For this, a diagnostic process is necessary for decision-making during the monitoring of failures caused by vibration phenomena, which consists in observing the system by comparing its current data with the data coming from a normal operation. These indicators help engineer to determine the symptoms for the failing components of the system. This work deals with problems related to these vibrations, with the aim of developing a system of detection of failures using dynamic neural networks approach. The originality of this contribution is to calculate the various alarms based on this system which used the determined vibration models in order to ensure a reliable and safe operation of the gas compression installation using the examined gas turbine.
ISSN:2450-5471
2450-5471
DOI:10.2478/scjme-2018-0032