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- W3108914650 abstract "The present study aims at monitoring and classifying the multi-variant wear behavior of sliding bearings. For this purpose, acoustic emission (AE) technique was applied to a test rig for sliding bearings. AE signals were evaluated with machine learning methods in order to detect anomalies from a hydrodynamic bearing operation. Furthermore, a deep learning approach based on convolutional neural networks was used for multi-class classification into three different wear failure modes, namely running-in, inadequate lubrication and particle-contaminated oil. A high accuracy and high sensitivity have been achieved in the detection and classification of three-body abrasion due to particle contamination. In the cases of running-in and inadequate lubrication, the incubation period during the onset of inadequate lubrication is sometimes mistaken for running-in and vice-versa, which reduces the overall accuracy of the classification." @default.
- W3108914650 created "2020-12-07" @default.
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- W3108914650 date "2021-03-01" @default.
- W3108914650 modified "2023-10-03" @default.
- W3108914650 title "Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems" @default.
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- W3108914650 doi "https://doi.org/10.1016/j.triboint.2020.106811" @default.
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