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.
Highlights
An effective means of improving turbofan engine efficiency is to increase the bypass ratio (BPR).The BPR is driven by the fan diameter associated with aerodynamic influences combined with the thermodynamic requirements of the turbine design
Classification of the three main friction states by using machine learning algorithms applied on Acoustic Emission (AE) signals (Section 3.1)
Friction state classification: This was done under varying rotational speeds and radial loads by pre-processing the AE signals, extracting and selecting suitable AE features from time, frequency and time-frequency domain using continuous wavelet transform (CWT) and applying Support Vector Machine (SVM) as classifier
Summary
The BPR is driven by the fan diameter associated with aerodynamic influences combined with the thermodynamic requirements of the turbine design. The fan operates at slow speeds and Lubricants 2020, 8, 29; doi:10.3390/lubricants8030029 www.mdpi.com/journal/lubricants. Lubricants 2020, 8, 29 the turbine at high rotational speeds. These contradicting requirements can be sorted out by using a planetary gearbox between the components. Using a gearbox introduces additional failure modes such as journal bearing wear caused by mixed or dry friction. A breakdown of this component could have a negative impact on the product reliability which causes high maintenance costs and downtime. This paper outlines journal bearing monitoring opportunities to address technical diagnosis of the world’s most powerful aircraft gearbox
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