Detection and Modeling Vibrational Behavior of a Gas Turbine Based on Dynamic Neural Networks Approach-Reference-Cited by-同舟云学术

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

Published:2018-11-01 Issue:3 Volume:68 Page:143-166
ISSN:2450-5471
Container-title:Strojnícky casopis – Journal of Mechanical Engineering
language:en
Short-container-title:

Author:

Benrahmoune Mohamed¹²,Ahmed Hafaifa¹²,Mouloud Guemana³,XiaoQi Chen⁴

Affiliation:

1. Applied Automation and Industrial Diagnostics Laboratory, University of Djelfa, Djelfa , Algeria

2. Gas Turbine Joint Research Team, University of Djelfa, Djelfa , Algeria

3. Science and Technology Faculty, Médéa University, Médéa , Algeria

4. Department of Mechanical Engineering, University of Canterbury, Christchurch , New Zealand

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering

Link

https://www.sciendo.com/pdf/10.2478/scjme-2018-0032

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