Diagnostics of gas turbine engine blades using the forced vibration method

Кочергин, А. В.; Ivshin, I. V.; Sabirov, Askadula Galimzyanovich; Gavrilov, V. A.

doi:10.3103/s1068799807030178

Cited by 1 publication

(2 citation statements)

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“…To prevent this phenomenon, gas turbines are equipped with monitoring systems to continuously monitor the mode of operation of these machines [12][13][14][15][16][17][18]. Indeed, the solutions proposed so far to prevent this phenomenon have not given encouraging results, because these systems always present a risk of passage in vibrational phase.…”

Section: Gas Turbine Installationmentioning

confidence: 99%

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

Benrahmoune

Hafaifa

Guemana

et al. 2018

Strojnícky Časopis - Journal of Mechanical Engineering

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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.

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Section: Gas Turbine Installationmentioning

confidence: 99%

“…The performances of the learning and validation models are evaluated by the square root of the Root Mean Square Error (RMSE) given by [27][28]: (17) where N is the number of observations and (.) y is the output of the model.…”

Section: From the Cost Function For The Output Layermentioning

confidence: 99%