Browsing by Author "Mokhtari, Noushin"

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    Friction and wear monitoring methods for journal bearings of geared turbofans based on acoustic emission signals and machine learning
    (MDPI, 2020-03-07) Mokhtari, Noushin; Pelham, Jonathan Gerald; Nowoisky, Sebastian; Bote-Garcia, José-Luis; Gühmann, Clemens
    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.
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    Monitoring concept study for aerospace power gear box drive train
    (VDI Verlag GmbH, 2019-09-19) Nowoisky, Sebastian; Grzeszkowski, Mateusz; Mokhtari, Noushin; Pelham, Jonathan G.; Gühmann, Clemens
    Using a gearbox in a turbojet engine implies additional monitoring tasks due to new introduced failure modes. This paper outlines monitoring options to address technical diagnosis of the world’s most powerful aerospace gearbox. For this novel technology different monitoring options are assessed to enable the trade between technical effort and monitoring capability. In this paper options to monitor the gears and journal bearings are described. To detect gear wear, pitting, and gear teeth cracks the use of acceleration, acoustic emission sensors, and different methods will be assessed. First stage results are based on Back2Back test run results in occurring pitting and gear teeth loss [1]. The journal bearing mixed friction will be detected by the use of an acoustic emission sensor [3], [5]. Due to the location of the journal bearing in the rotating area of the gearbox a Wireless Data Transfer Unit (WDTU) must be introduced [6], [7]. Results of early subscale component test runs are used to define requirements to adjust the WDTU and accommodate the new power gearbox (PGB) requirements. The electronics of the WDTU must cope with challenges such as the environmental conditions of the gearbox. To extract the mixed friction pattern by the applied signal processing steps from the noise disturbance caused by gear mesh is a technical challenge. Finally the paper closes with a recommendation on how to monitor such a gearbox and provides an outlook to the next test campaign, where the WDTU will be applied based on a back2back configuration of a subscale planetary gearbox [8].