Detection and Modeling Vibrational Behavior of a Gas Turbine Based on Dynamic Neural Networks Approach
Journal Title: Strojnicky casopis - Journal of Mechanical Engineering - Year 2018, Vol 68, Issue 3
Abstract
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.
Authors and Affiliations
Mohamed Benrahmoune, Hafaifa Ahmed, Guemana Mouloud and Chen XiaoQi
Keywords
Related Articles
- EP ID EP43954
- DOI https://doi.org/10.2478/scjme-2018-0032
- Views 224
- Downloads 0
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