Development of the method for estimating the technical condition of gas pumping units by their accelerating characteristic

Gorbiychuk, Mykhailo; Zamikhovska, Olena; Zamikhovsky, Leonid; Pavlyk, Volodymyr

doi:10.15587/1729-4061.2020.206476

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such information can be the technological parameters of the GPU operation or the characteristics of vibration and acoustic processes accompanying its operation. The paper [3] provides a brief analysis of methods for diagnosing such objects as a gas turbine power plant, a two-flow turbojet engine, and a gas turbine. In this case, mathematical models of their technological parameters are used, which are represented both in the form of a conventional artificial neural network (Convolutional Neural Network -CNN), and in the form of a probabilistic neural network (PNN).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The solution to this problem requires the use of technical diagnostics methods. Methods of parametric and vibroacoustic diagnostics are used to diagnose the technical condition of the modular units and elements of the GPU [2,3]. Methods of parametric diagnostics are based on the processing and analysis of technological parameters that characterize the process of GPU operation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kozlenko

Zamikhovska

Zamikhovskyі

2021

EEJET

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%