Control and diagnostic of vibration in gas turbine system using neural network approach

Rahmoune, Mohamed Ben; Hafaifa, Ahmed; Kouzou, Abdellah; Guemana, Mouloud; Salam, Abudura

doi:10.1109/icmic.2016.7804177

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…A new approach to diagnosing the technical condition of rotating machines based on nonlinear autoregression with external (exogenous) neural networks NARX (Nonlinear Autoregressive Exogenous Model) is given in [10]. The network learns on the basis of experimental data received from a vibration sensor, which is installed on a power bearing of a GE 3002 two-shaft GPU.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…For the overwhelming majority of the proposed neural networks, their training is carried out on the GTU, GTE and JE models using the generated sets of diagnostic data for modeled defects of individual elements of the diagnostic object. In the works considered, there are practically no references to the use of experimental data, with the exception of [7,10], for training the proposed neural networks regarding the diagnosis of modular units or elements of GTU, GTE and JE. Finally, none of the considered neural networks uses the characteristics of acoustic processes accompanying the operation of GTU, GTE and JE as input data.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Kozlenko

Zamikhovska

Zamikhovskyі

2021

EEJET

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In recent years, more and more attention has been paid to the use of artificial neural networks (ANN) for the diagnostics of gas pumping units (GPU). Usually, ANN training is carried out on GPU workflow models, and generated sets of diagnostic data are used to simulate defect conditions. At the same time, the results obtained do not allow assessing the real state of the GPU. It is proposed to use the characteristics of the acoustic and vibration processes of the GPU as the input data of the ANN. A descriptive statistical analysis of real vibration and acoustic processes generated by the operation of the GPU type GTK-25-i (Nuovo Pignone, Italy) was carried out. The formation of batches of diagnostic features arriving at the input of the ANN was carried out. Diagnostic features are the five maximum amplitude components of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic features are calculated directly in the ANN input data pipeline in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedforward ANN, trained on the backpropagation algorithm. The results of training and testing the developed ANN are presented. During testing, it was found that the signal classification precision for the “nominal” state of all 1,475 signal samples is 1.0000, for the “current” state, precision equals 0.9853, and for the “defective” state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Kozlenko

Zamikhovska

Zamikhovskyі

2021

EEJET

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“…An analysis of the diagnostic of effective power and efficiency of the GTU was proposed in [7][8][9]. In recent years, the usage of computer neural networks has been growing for parametric analysis of the technical systems state [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Stepanov

Zhirnov

Sazonkin

et al. 2022

Sensors

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In this article, we study the possibility of gas turbine unit (GTU) monitoring using interferometric fiber optic sensors. We used the Mach–Zehnder interferometer (MZI) scheme, which can be easily implemented and simply installed on the turbine, and also allows us to solve the problem of phase unwrapping conveniently. In this research, the following main steps were carried out: an experimental scheme based on the MZI was assembled, and its sensitive arm was fixed on the GTU under study; data on various operation modes of the GTU was collected; the data were subjected to frequency FFT analysis, based on which the main rotational speeds of the turbine were identified. With FFT analysis, we also demonstrated multiples harmonics, which appear in the case of GTU after operating time, caused by the number of blades. The possibility of GTU monitoring and analysis using a non-invasive compact fiber-optic sensor is demonstrated: spectral analysis is used to detect the rotor speed, as well as the presence or absence of high-order multiple frequencies indicating blade and bearing defects, which are determined by the number of GTU’s blades and rolling bearing used as turbines rotor supports.

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“…Indeed, the basic idea of the retro-propagation is to minimize the mean square error MSE criterion with respect to the connection weight [18]- [20].…”

mentioning

confidence: 99%

Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks

Bouzidi

Harrouz

Mansouri

et al. 2022

AMM

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In this paper, we present a modeling of the photovoltaic array in order to tracking the maximum power point (MPPT) using a soft computing approach based on artificial neural network, The maximum power point tracking MPPT play a crucial role in photovoltaic systems for their ability to maximize the power output under varying conditions; The photovoltaic array modeled and implemented in matlab simulink environnement using the conventional perturb and observe algorithm for multiple ranges under varying temperatures and irradiances levels, a feed forward neural network collect the training data from the photovoltaic array simulink model, after the training process check, the neural network model tested with new temperatures and irradiance data to predict the maximum power point of the photovoltaic array, The developed neural network model shown an interesting results compared to simulink model based on classic perturb and observe algorithm.

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Control and diagnostic of vibration in gas turbine system using neural network approach

Cited by 3 publications

References 17 publications

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network

Non-Invasive Acoustic Monitoring of Gas Turbine Units by Fiber Optic Sensors

Modeling of a Photovoltaic Array with Maximum Power Point Tracking Using Neural Networks

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