Journal
TRIBOLOGY INTERNATIONAL
Volume 155, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.triboint.2020.106811
Keywords
Sliding bearings; Acoustic emission; Machine learning; Anomaly detection classification
Categories
Funding
- Excellence Initiative of the German federal and state governments
- Deutsche Forschungsgemeinschaft (DFG) [GRK 1856]
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The study aims to monitor and classify the multi-variant wear behavior of sliding bearings using acoustic emission (AE) technique and deep learning based on convolutional neural networks. It successfully achieved high accuracy and sensitivity in detecting three-body abrasion due to particle contamination.
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.
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