DESIGN OF A SYSTEM FOR DIAGNOSING THE TECHNICAL CONDITION OF A GAS PUMPING UNIT BASED ON THE ANALYSIS OF EXISTING METHODS OF PROCESSING VIBROACOUSTIC SIGNALS
DOI:
https://doi.org/10.32851/tnv-tech.2022.4.2Keywords:
gas pumping unit, technical condition, diagnostics, Fourier transform, wavelet transform, artificial neural network, discrete cosine transform, autocorrelation function.Abstract
The gas transportation system of Ukraine provides transportation of natural gas from deposits to consumers located both on the territory of Ukraine and outside its borders. One of the key elements of the gas transportation system of Ukraine are compressor stations, the task of which is to maintain the specified pressure in the system for uninterrupted transportation of natural gas. The main element of the compressor station is the gas pumping unit (GPU). It is a complex mechanical system consisting of many nodes. In the process of functioning of the gas pumping unit, in its nodes and units, the process of wear of parts occurs, which, subsequently, can lead to an accident. To avoid such situations, current and capital repairs of the system are periodically carried out. A number of non-destructive technical condition control methods have been developed to monitor the technical condition of the GPU between repairs, to optimize the periodicity of their carrying out and to provide early warning of the GPU leaving the nominal state. Such methods are divided into parametric methods, based on the analysis of the operating parameters of the system (temperature and pressure at various GPU nodes, turbine rotation frequency, chemical composition of combustion products), and vibroacoustic, based on the analysis of vibration and acoustic oscillations generated by the GPU nodes. The current state of vibroacoustic signal processing methods, such as Fourier transform, wavelet transform, artificial neural networks, discrete cosine transform, and autocorrelation functions are considered in the work. Based on the analysis, a system for diagnosing the technical state of the GPU based on the Fourier transform in combination with the wavelet transformation and an artificial neural network with two hidden layers is proposed, the task of which is to recognize the technical condition of the GPU, based on the result of the wavelet transformation, divided into separate images in primary colors.
References
Czyżewski, A. Remote Health Monitoring of Wind Turbines Employing Vibroacoustic Transducers and Autoencoders(2022) Frontiers in Energy Research, 10, art. no. 858958, . DOI: 10.3389/fenrg.2022.858958.
García Márquez, F.P., Bernalte Sánchez, P.J., Segovia Ramírez, I. Acoustic inspection system with unmanned aerial vehicles for wind turbines structure health monitoring (2022) Structural Health Monitoring, 21 (2), pp. 485-500. DOI: 10.1177/14759217211004822
Yu, X., Yang, Y., He, Q., Peng, Z. Time-Frequency Bandpass Filter with Nonstationary Signal Decomposition Application (2021) Journal of Physics: Conference Series, 1880 (1), art. no. 012003. DOI: 10.1088/1742-6596/1880/1/012003
Rogovskii, I.L., Zapadlovskij, O.S., Voinash, S.A., Maksimovich, K.Y., Sokolova, V.A., Alekseeva, S.V., Taraban, M.V. Research of vibroacoustic signals in diagnostics of technical condition of engines of beet harvesters combines (2020) Journal of Physics: Conference Series, 1679 (4), art. no. 042032. DOI: 10.1088/1742-6596/1679/4/042032
Mills, A.R., Kadirkamanathan, V. Sensing for aerospace combustor health monitoring (2020) Aircraft Engineering and Aerospace Technology, 92 (1), pp. 37-46. DOI: 10.1108/AEAT-11-2018-0283
Pang, Y., Jia, L., Liu, Z. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network-Based Remaining Useful Life Estimation of Bearings (2020) Shock and Vibration, 2020, art. no. 8240168. DOI: 10.1155/2020/8240168
An, G., Song, K., Li, R., Sun, H., Li, H. Degradation feature extraction method for piezoelectric ceramic of ultrasonic motor based on DCT-SV cross entropy (2019) Journal of Vibroengineering, 21 (6), pp. 1651-1664. DOI: 10.21595/jve.2019.20525
Hoell, S., Omenzetter, P. Sequential projection pursuit for optimised vibrationbased damage detection in an experimental wind turbine blade (2018) Smart Materials and Structures, 27 (2), art. no. 025007. DOI: 10.1088/1361-665X/aa9f8e
Reseach of the characteristics of acoustic processes using wavelet transformation for detecting a diagnostic sign of the technical state of gas pumping units. Zamikhovskiy L., Zamikhovska O., Pavlyk V. TECHNOLOGY AUDIT AND PRODUCTION RESERVES, 2021. № 1/2(57).P. 6-12. DOI: 10.15587/2706-5448.2021.224432. Khalil, A.E.E., Gupta, A.K. Acoustic and heat release signatures for swirl assisted distributed combustion (2017) Applied Energy, 193, pp. 125-138. DOI: 10.1016/j.apenergy. 2017.02.030
Jonak, J., Machrowska, A., Podgórski, J., Bȩc, J. Identification of the operating parameters for the mechanical system using EMD algorithm (2016) MATEC Web of Conferences, 83, art. no. 05001. DOI: 10.1051/matecconf/20168305001
Feng, Z., Shangjun, Y., Yantao, H., Xianfeng, S. Cyclic Wiener filtering algorithm in discrete cosine transform domain for vibration signal (2016) MATEC Web of Conferences, 61, art. no. 02019, . DOI: 10.1051/matecconf/20166102019
Kozlenko, O. Zamikhovska, V. Tkachuk, and L. Zamikhovskyi, "Deep Learning Based Fault Detection of Natural Gas Pumping Unit", 2021. IEEE 12th International Conference on Electronics and Information Technologies (ELIT), 2021, pp. 71-75, doi: 10.1109/ELIT53502.2021.9501066Babanin, O., Bulba, V. Designing the technology of express diagnostics of electric train's traction drive by means of fractal analysis (2016) Eastern-European Journal of Enterprise Technologies, 4 (9-82), pp. 45-54. DOI: 10.15587/1729-4061.2016.76520
