Development of a data acquisition method to train neural networks to diagnose gas turbine engines and gas pumping units

Кulyk, Mykola; Abdullayev, P. S.; Yakushenko, Oleksandr; Popov, Oleksandr; Mirzoyev, Azer; Chumak, Oleg; Okhmakevych, Valerii

doi:10.15587/1729-4061.2018.147720

Cited by 1 publication

(6 citation statements)

References 7 publications

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“…3. is for the second attempt; is for the third attempt); the best δ min values of δ Т ( ) and δ ( ) parameters achieved at the moment the parameter δ С reaches the minimum value (e) e f As can be seen, when comparing these data and the data given in [25], the introduction of measurement errors into numerical experiment leads to a significant increase in the overlap of classes in the zone of their delimitation.…”

Section: 1 Forming the Training And Control Sets Of Parameters Containing Measurement Errorsmentioning

confidence: 80%

“…The method of obtaining initial data for training and testing the neural network was described in [25,26]. In addition, the study [25] The data sets describe the behavior of the GTEs belonging to 6 Classes of TS of the main elements of the flow path:…”

Section: The Diagnosing Objectmentioning

confidence: 99%

“…The task of this stage of the study is the selection of AF of the neurons that make up layers of the NN and the initial estimation of the number of layers and neurons in them which are necessary for TS classification. At this stage, the data sets obtained without taking into account the influence of measurement errors are used (without using dependences (9) to (18) in [25]). The initial data for training neural networks are given in the same study.…”

Section: Determination Of the Optimal Combination Of Neuron Activation Functions For Solving The Problemmentioning

confidence: 99%

“…When forming data sets, the method described in [25] was used. In this case, in contrast to the data sets given in this study, dependences (9) to (13) given in the same study were additionally involved in the same study.…”

Section: 1 Forming the Training And Control Sets Of Parameters Containing Measurement Errorsmentioning

confidence: 99%

“…It can be concluded from all that has been said that with the adopted characteristics of the TS classes (Table 2 in [25]) and metrological characteristics of the parameter measurement system given in Table 1, the training set can be considered representative if each class is represented by 4,000 or more points. when analyzing the data described in Section 5.…”

Section: 2 Determining the Optimal Size Of The Training Setmentioning

confidence: 99%

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Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

Yakushenko

Popov

Mirzoyev

et al. 2020

EEJET

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A process of creating a static neural network intended for diagnosing bypass gas turbine aircraft engines by a method of categorizing the technical state of the engine flow path was considered. Diagnostics depth was "to the structural assembly". A variant of diagnosing single faults of the flow path was considered.The following tasks were set:select the best neuron activation functions in the network layers;determine the number of layers; -determine the optimal number of neurons in layers;determine the optimal size of the training set.The problem was solved taking into account the influence of parameter measurement errors. The method of structure optimization implies training the network of the selected configuration using a training data set. The training was periodically interrupted to analyze the results of the network operation according to the criterion characterizing the quality of classification of the engine technical state. The assessment was performed with training and control sets. The network that provides the best value of the classification quality parameter assessed by the test set was selected as the final network. The PS-90A turbojet engine was selected as the object of diagnostics. Diagnostics was carried out on takeoff mode and during the initial climb. Primary optimization was carried out according to the data with no measurement errors. It was shown that a two-layer network with the use of neurons having a hyperbolic tangent function in both layers is sufficient to solve the problem. The size of the first network layer was finally optimized according to the data containing measurement errors.A two-layer network with eight neurons in the first layer was obtained. The share of erroneous diagnoses measured 14.

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Section: 1 Forming the Training And Control Sets Of Parameters Containing Measurement Errorsmentioning

confidence: 80%

Section: The Diagnosing Objectmentioning

confidence: 99%

Section: Determination Of the Optimal Combination Of Neuron Activation Functions For Solving The Problemmentioning

confidence: 99%

Section: 1 Forming the Training And Control Sets Of Parameters Containing Measurement Errorsmentioning

confidence: 99%

Section: 2 Determining the Optimal Size Of The Training Setmentioning

confidence: 99%

See 3 more Smart Citations

Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

Yakushenko

Popov

Mirzoyev

et al. 2020

EEJET

Self Cite

View full text Add to dashboard Cite

show abstract

Development of a data acquisition method to train neural networks to diagnose gas turbine engines and gas pumping units

Cited by 1 publication

References 7 publications

Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

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