Abstract
The durable and reliable work of compressor machines and their technical security have great practical importance in reducing the environmental impact of hydrocarbon transport. Investigation and control of the vibration processes of these machines by sensors are current problems for oil and gas transportation. Piston and centrifugal machines are heavy-duty systems that are still failure-prone due to high stresses and wear proses. Specialized methods will be developed for the influence of impacts, vibrations, temperature, film lubrication and unified system model. The research efforts will lead to more reliable and efficient design alternatives for reciprocating machines which are used for the transport of oil and gas and will thus contribute directly to the competitiveness of a key industry in the Caspian Sea - Black Sea Region. The durable and reliable work of compressor-pump stations used in the oil and gas industry has great importance for practice. The article investigated the main causes of failures through the collection and analysis of data from different parts of the machines are used for transportation the oil and gas resources using sensors. The need for planned technical diagnostics of aggregates was sufficiently justified. The main causes of malfunctions of these machines and their causes were identified. It is noted that the statistical unbalance is characterized by a high amplitude of vibration signals. Finally, we explain how we can determine the existence of a statistical unbalance using graphs constructed using these vibration signals.
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Bakhshali, V.I., Mardanov, N.T., Bekirova, A.A., Ismayil, I.A. (2022). Development of Methods for Processing Acoustic Emission Signals of Sensors for the Compressor-Pump Station’s Control. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021. ICSCCW 2021. Lecture Notes in Networks and Systems, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-92127-9_93
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DOI: https://doi.org/10.1007/978-3-030-92127-9_93
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