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In the early 1980s, 120 gas compressor units (GPU) type GTK-25i were installed on the Urengoy-Pomary-Uzhgorod transcontinental gas pipeline, and three of them are in operation at CS-39 “U-P-U” of the Bogorodchansk Linear Production Department of trunk gas pipelines. Today, about 80% of GPU type  GTK-25i have worked out the established service life, or those close to it. Their further operation does not ensure reliable and efficient operation, and therefore numerous failures and accidents occur, leading to significant economic losses.  Methods of parametric and vibroacoustic diagnostics of GPU are analyzed. It is noted that the most fruitful years of development of the methods of vibroacoustic diagnostics of GPUs are the 70-90s of the last century. Today, their development is taking place in the direction of using modern information technologies and various transformations in the processing of vibroacoustic processes to identify diagnostic signs of the technical state of the GPU.  The methods of diagnosing GPU type GTK-25i the analysis showed their absence. The exception is certain methods of their diagnostics based on modern information technologies, which were developed by the authors of the article. At the same time, the carried out improvement of the automatic control system (ACS) of the GPU type GTK-25i in terms of its technical and software makes it possible to obtain information about additional, in comparison with the standard ACS, technological parameters of the GPU type GTK-25i operation and vibroacoustic processes that accompany its operation. and can be used to create diagnostic methods for GPU type GTK-25i. The methodology for monitoring the technical condition of GPU type GTK-25i based on the determination of the highest values of the discriminant functions for each of the three technical states of GPU type GTK-25i for 16 technological parameters and acoustic and vibration characteristics is considered. At the same time, the best "nominal" condition is considered to be the state of GPU type GTK-25i after the repair work, the "defective" state "- before the repair work, and" current "- after the corresponding operating time of the GPU type GTK-25i. The use of the technique made it possible to develop a complex method, which is a combination of parametric and vibroacoustic diagnostics methods. It is shown that the use of the proposed method allows tracing the trend of changes in the technical state of GPU type GTK-25i in time and predicting the moment of its decommissioning. The developed method does not require additional technical means for its implementation, as it receives information from the improved ACS GPU type GTK-25i, which, in turn, can use the diagnostic results to control the gas compression process, taking into account the technical condition of the GPU type GTK-25i.

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

The object of research is the degradation processes that take place in gas-pumping units (GPU) during its long-term operation and lead to the appearance of defects and, as a result, to a change in its technical state. Today, methods of parametric and vibration diagnostics are used to determine the technical condition of the GPU. To identify diagnostic signs of the technical state of the GPU, various transformations are used, in particular the wavelet transforms used in vibration processing that accompany the operation of the GPU and their technological parameters. At the same time, in the study of the diagnostic signs of the technical state of the GPU, the acoustic processes accompanying the operation of the GPU, which can be more informative in comparison with the vibration ones, were practically not considered. The developed experimental research methodology and their technical support made it possible to record the acoustic processes accompanying the operation of the gas compressor unit type GTK-25-i of the Nuovo Pignon company (Italy). In the course of the experimental studies, the realizations of the acoustic processes of the GPU were obtained for its three states – «nominal», «defective» and «current». Further studies of acoustic processes for three states of the GPU type GTK-25-i and using the wavelet transform showed that by the appearance of the wavelet spectrograms it is difficult to notice the difference in the appearance or disappearance of various frequency components depending on the technical state of the GPU. To obtain quantitative indicators of this dependence, a discrete wavelet transform was carried out, which makes it possible to identify characteristic trends in the change in noise values at different scales. The values of the approximation norm and the detail norms in relation to the signal norm (in percent) were obtained for a five-level wavelet decomposition with datasets. A linear dependence of the norm of the wavelet-component of the fifth-order detailing on the operating time of GPU type GTK-25-i and (changes in the technical state), which can be taken as a diagnostic sign of its technical state, has been established. The investigated diagnostic feature can be used as the basis for the method of diagnosing the technical state of GPU type GTK-25-i based on the characteristics of its acoustic process using the wavelet transform. An approach to identifying a diagnostic sign of the technical state of a GPU type GTK-25-i is considered based on the characteristics of acoustic processes using a wavelet transform can be used to identify a diagnostic sign of a condition for any type of GPU.

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