Email this article USING MACHINE LEARNING METHODS TO PREDICT HYDRAULIC PUMP REMAINING USEFUL LIFE
- Authors: Gareyev A.M.1, Prokofiev A.B.2, Ryzhkova Y.1, Stadnik D.1
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Affiliations:
- Samara National Research University
- Samara State Aerospace University
- Issue: Vol 7, No 3 (2021)
- Pages: 13-21
- Section: Articles
- URL: https://dynvibro.ru/dynvibro/article/view/10233
- DOI: https://doi.org/10.18287/2409-4579-2021-7-3-13-21
- ID: 10233
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Abstract
This article discusses a sampling algorithm for machine learning in order to capture the trend of the cumulative deterioration of the characteristics of a hydraulic pump (cumulative degradation), which affects the efficiency of its operation and manifests itself in the form of a drop in volumetric efficiency. To generate data, a simulation model of a typical station for the supply of working fluid in technological complexes, developed in the SimulationX program, is used. The transient processes of pressure change in the system are described, from the analysis of which a tendency of a decrease in the average component of the pressure signal is traced, which is used as a diagnostic feature - an indicator of the state of the system. An example is also considered that describes the possibility of assessing the residual life of the system based on data characterizing the past state of the system, and can be adapted when forming a more complex base, taking into account the use of artificial neural networks.
About the authors
A. M. Gareyev
Samara National Research University
Author for correspondence.
Email: gareyev@ssau.ru
Candidate of Science (Engineering)
Associate Professor of the Department of Aircraft Maintenance
A. B. Prokofiev
Samara State Aerospace University
Email: prok@ssau.ru
Russian Federation
Yu. Ryzhkova
Samara National Research University
Email: gareyev@ssau.ru
Russian Federation, 34, Moskovskoe shosse, Samara, 443086, Russian Federation
Dmitry Stadnik
Samara National Research University
Email: sdm-63@bk.ru
Assistant lecturer at Department of Power Plants Automatic Systems
Russian FederationReferences
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- Mohamad Danish Anis. Towards Remaining Useful Life Prediction in Rotating Machine Fault Prognosis: An Exponential Degradation Model (2018) IEEE International Conference on Condition Monitoring and Diagnosis – Perth – Australia. doi: 10.1109/CMD.2018.8535765
- Gareev, A., Gimadiev, A., Popelnyuk, I., Stadnik, D., Sverbilov, V. Simulation of electro-hydraulic systems taking into account typical faults (2020) BATH/ASME 2020 Symposium on Fluid Power and Motion Control, FPMC 2020, article № V001T01A045.
- Gebraeel, Nagi. "Sensory-Updated Residual Life Distributions for Components with Exponential Degradation Patterns." IEEE Transactions on Automation Science and Engineering. Vol. 3, Number 4, 2006, pp. 382–393.
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