Classification of wear state for a positive displacement pump using deep machine learning
Authors:
- Jarosław Konieczny,
- Waldemar Łatas,
- Jerzy Stojek
Abstract
Hydraulic power systems are commonly used in heavy industry (usually highly energy-intensive) and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump’s operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network’s recognition of the pump’s condition. The whole study was summarized with conclusions.
- Record ID
- CUT9b34c9d0e4bf4be7b208ec38e33ce17b
- Publication categories
- ;
- Author
- Journal series
- Energies, ISSN , e-ISSN 1996-1073, Biweekly
- Issue year
- 2023
- Vol
- 16
- No
- 3
- Pages
- [1-19]
- Article number
- 1408
- Other elements of collation
- il. (w tym kolor.); Bibliografia (na s.) - 17-19; Bibliografia (liczba pozycji) - 42; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 16, Iss. 3
- Substantive notes
- This article belongs to the Special Issue: Energy Problems in Control and Robotics System
- Keywords in English
- learning systems, deep machine learning, diagnostics, signal analysis, multi-piston pump, vibration, feature ranking
- ASJC Classification
- DOI
- DOI:10.3390/en16031408 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1073/16/3/1408 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT9b34c9d0e4bf4be7b208ec38e33ce17b/
- URN
urn:pkr-prod:CUT9b34c9d0e4bf4be7b208ec38e33ce17b
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.