Random Forest Model of Flow Pattern Identification in Scavenge Pipe Based on EEMD and Hilbert Transform

Xiaodi Liang, Suofang Wang () and Wenjie Shen
Additional contact information
Xiaodi Liang: College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Suofang Wang: College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Wenjie Shen: College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Energies, 2023, vol. 16, issue 16, 1-15

Abstract: Complex oil and gas two-phase flow exists within an aero-engines bearing cavity scavenge pipe, prone to lubricated self-ignition and coking. Lubricant system designers must be able to accurately identify and understand the flow state of the scavenge pipe. The prediction accuracy of previous models is insufficient to meet the more demanding needs. This paper establishes a visualized flow pattern identification test system for the scavenge pipe, with a test temperature of up to 370 k, using a high-speed camera to photograph four flow patterns, decomposing the pressure signals obtained from high-frequency dynamic pressure sensors using the ensemble empirical mode decomposition (EEMD) method, and then performing Hilbert transform, using the Hilbert spectrum to quantify the changes of amplitude and frequency with time, and establishing the energy and flow pattern correspondence analysis. Then the energy percentage of IMFs is used as the input of feature values, and the random forest algorithm machine learning is used for predictive classification. The experimental results show that the flow pattern recognition rate established in this paper can reach 98%, which can identify the two-phase flow pattern in the scavenge pipe more objectively and accurately.

Keywords: oil-gas flow; EEMD; Hilbert transform; random forest; flow pattern identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/16/6084/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/16/6084/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:16:p:6084-:d:1221443

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().