Optimization of entropy estimation computing algorithm for random signals in digital communication devices

Melnychuk, Stepan; Lazarovych, Ihor; Kozlenko, Mykola

doi:10.1109/tcset.2018.8336380

Cited by 5 publications

(1 citation statement)

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“…The "Adam" [27] was used as an optimizer. The categorical cross-entropy (3) was used as the loss function; the efficient calculation of the entropy was carried out in accordance with [28]. The quality metric is overall accuracy (4) [29].…”

Section: Neural Network Depth Model Designmentioning

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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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: Neural Network Depth Model Designmentioning

confidence: 99%