Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning

Simple item page
dc.contributor.authorMokhtari, Noushin
dc.contributor.authorPelham, Jonathan Gerald
dc.contributor.authorNowoisky, Sebastian
dc.contributor.authorBote-Garcia, José-Luis
dc.contributor.authorGühmann, Clemens
dc.date.accessioned2020-03-10T12:51:24Z
dc.date.available2020-03-10T12:51:24Z
dc.date.issued2020-03-07
dc.description.abstractIn this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.en_UK
dc.identifier.citationMokhtari N, Pelham JG, Nowoisky S, et al., (2020) Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning. Lubricants, Volume 8, Issue 3, Article number 29en_UK
dc.identifier.cris26419516
dc.identifier.issn2075-4442
dc.identifier.urihttps://doi.org/10.3390/lubricants8030029
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15257
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectjournal bearingen_UK
dc.subjectacoustic emissionen_UK
dc.subjectmachine learningen_UK
dc.subjectfriction classificationen_UK
dc.subjectfriction localizationen_UK
dc.subjectrun-in wearen_UK
dc.subjectlong-term wearen_UK
dc.titleFriction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learningen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
journal_bearings_of_geared_turbofans-2020.pdf
Size:
2.39 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)