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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Measuring vibration velocity is one of the most common techniques to estimate the condition of industrial machines. At a constant operating point, as the vibration velocity value increases, the machine’s condition worsens. However, there are no precise thresholds that indicate the condition of a machine at different operating points. Also, the axial piston pump, which is the subject of the article, is a device that generates stronger vibrations by design and cannot be enclosed in general vibration norms. Due to different use cases and work regimes of axial piston pumps, the need to determine whether the device is working correctly for a broad spectra of operating points emerges. This article aims to present and compare different methods for vibration velocity prediction for axial piston pumps with use of neural networks including dense networks, variants of recurrent neural networks, and ensemble methods. The result of this research consists of models that have performance metrics that clearly indicate whether the monitored pump has malfunctioned or not across a wide variety of operating points, working conditions, and in case of reassembling. A detailed analysis of the influence of available measured variables on the performance of models is also provided. The conclusion is that the application of commercial implementation of developed models is reasonable in the context of both performance quality and costs of sensors needed to provide the necessary data.

Details

Title
Vibration Velocity Prediction with Regression and Forecasting Techniques for Axial Piston Pump
Author
Fic, Paweł 1   VIAFID ORCID Logo  ; Czornik, Adam 1   VIAFID ORCID Logo  ; Rosikowski, Piotr 2 

 Department of Automatic Control and Robotics, Silesian University of Technology, 44-100 Gliwice, Poland; [email protected] 
 PONAR Wadowice S.A., Św. Jana Pawła II 10, 43-170 Łaziska Górne, Poland; [email protected] 
First page
11636
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20763417
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2888111829
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.