Jet engine degradation prognostic using artificial neural networks

Giorgi, Maria Grazia De; Ficarella, Antonio; Carlo, Laura De

doi:10.1108/aeat-01-2018-0054

Cited by 22 publications

(12 citation statements)

References 13 publications

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“…The software can predict engine performance at design point, steady-state off-design and transient calculations. The engine description as well as the design parameters that were used in GSP to model each engine component can be found in [ 1 , 3 ].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data regarding dynamic performance predictions of an aeroengine

Giorgi

Quarta

2020

Data in Brief

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The design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack. This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software. The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT). The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article. The data presented here are related to the article entitled “MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine” [1] .

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data regarding dynamic performance predictions of an aeroengine

Giorgi

Quarta

2020

Data in Brief

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“…However, the work does not indicate the effectiveness of the method when using a test set from real experimental data. In [7], the use of ANN for diagnosing defects in a Viper 632-43 jet engine (JE) (jointly developed by Rolls-Royce, Britain and Fiat Aviazione, Italy), such as compressor fouling, turbine erosion, both defects at the same time is considered. The work consists of three parts: the first part contains a mathematical model of a real jet engine for its two states, defect and defect-free.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The second part of the work is devoted to the optimization of ANN to predict the performance of a jet engine, and the third concerns the application of ANN to predict its technical condition. A feature of [7] is the use of experimental data obtained in real flight conditions of Viper 632-43 for ANN training, which is a significant advantage of the proposed method in comparison with [6].…”

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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“…Zhou DJ 6 used support vector machine to diagnose the deterioration of gas turbines, and studied the influence of sample size, kernel function, and monitoring parameters on the accuracy of diagnosis. Maria G 7 proposed and developed artificially intelligent methodologies to discover degradation trends through the detection of the engine's status. Three different scenarios were considered: compressor fouling, turbine erosion, and the presence of both degraded conditions.…”

Section: Introductionmentioning

confidence: 99%

Classification and prediction of gas turbine gas path degradation based on deep neural networks

et al. 2021

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Summary This paper mainly analyzes the performance degradation of turbomachinery in gas turbines, classifies the main types of degradation: increased tip clearance, corrosion/wear, fouling, and multiple degradation, and predicts the degradation trend through deep neural networks. The deep feedforward neural network is used to build the regression model and two classification models. The regression model uses a back propagation algorithm optimized by Lenvenberg Marquardt to convert the efficiency and flow capacity calculated by the thermodynamic model into the values under full load and ISO conditions to ensure that the comparison is performed on the same performance benchmark. Compared with deep feedforward neural network and random forest regression, the optimized model has higher accuracy and no overfitting. The data after the overhaul is selected as the performance benchmark, and the efficiency and flow capacity of different degradation types are calculated according to the benchmark and classified by the classification model. By testing the simulated data, the classification accuracy of the compressor and the turbine exceeds 99.9% and 99.5% respectively. Long short‐term memory is used to predict the degradation trends and the degradation of the predictions is classified by the classification model. The degradation classification accuracy of the prediction reached 93.65% and 81.65% in compressor and turbine, which shows that the prediction model has high accuracy.

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Jet engine degradation prognostic using artificial neural networks

Cited by 22 publications

References 13 publications

Data regarding dynamic performance predictions of an aeroengine

Data regarding dynamic performance predictions of an aeroengine

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

Classification and prediction of gas turbine gas path degradation based on deep neural networks

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