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Instruments and Systems: Monitoring, Control, and Diagnostics

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Application of Machine Learning for Vibration Diagnostics of a Hydraulic Unit
V.N. KLYACHKIN, D.A. ZHUKOV, A.V. ALEKSEEVA

In the process of vibration monitoring of the hydraulic unit, ten indicators are controlled: vibration of the generator bearing, the battle of the turbine shaft and others. According to these data, it is necessary to recognize the state of the unit – it is working or faulty. This is the task of technical diagnostics – recognition of the state of the system in conditions of limited information obtained during the operation of the unit. To solve it, machine learning methods can be applied. During the study, cross-validation was used for various options for splitting the initial sample, building classification models using various methods, as well as the selection of significant indicators. Based on the results of the study, a program for automated diagnostics of technical objects was developed. The program provides the search for the best basic classifiers, the calculation of the optimal volume of the control sample, the assessment of the impact of the selection method of significant indicators, as well as the construction of aggregated classifiers. The study showed that to assess the health of a hydraulic unit, aggregated classifiers can be used, which provide higher accuracy compared to the basic classification methods.
Keywords: hydraulic unit vibration, technical diagnostics, machine learning, cross-validation, aggregated classifi ers.


DOI: 10.25791/pribor.01.2020.1140

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Pp. 01-05.

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