Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9966
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Main Title: Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning
Author(s): Mokhtari, Noushin
Pelham, Jonathan Gerald
Nowoisky, Sebastian
Bote-Garcia, José-Luis
Gühmann, Clemens
Type: Article
Language Code: en
Abstract: In 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.
URI: https://depositonce.tu-berlin.de/handle/11303/11078
http://dx.doi.org/10.14279/depositonce-9966
Issue Date: 7-Mar-2020
Date Available: 4-May-2020
DDC Class: 621 Angewandte Physik
Subject(s): journal bearing
acoustic emission
machine learning
friction classification
friction localization
run-in wear
long-term wear
Sponsor/Funder: DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berlin
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Lubricants
Publisher: MDPI
Publisher Place: Basel
Volume: 8
Issue: 3
Article Number: 29
Publisher DOI: 10.3390/lubricants8030029
EISSN: 2075-4442
Appears in Collections:FG Elektronische Mess- und Diagnosetechnik » Publications

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