Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data
Abstract
:1. Introduction
2. Synthetic Data for Training
2.1. Simulation Setting
2.2. Simulation Results
3. Measurement
Measurement Results
4. Convolutional Neural Network
4.1. Data Preprocessing
4.2. Wavelet Transformation and Image Compilation
4.3. CNN-Network Architecture
5. Results
5.1. CNN-Trained with Simulation Data
5.2. CNN-Trained with Measurement Data
6. Discussion and Outlook
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Kahr, M.; Kovács, G.; Loinig, M.; Brückl, H. Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data. Sensors 2022, 22, 2490. https://doi.org/10.3390/s22072490
Kahr M, Kovács G, Loinig M, Brückl H. Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data. Sensors. 2022; 22(7):2490. https://doi.org/10.3390/s22072490
Chicago/Turabian StyleKahr, Matthias, Gabor Kovács, Markus Loinig, and Hubert Brückl. 2022. "Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data" Sensors 22, no. 7: 2490. https://doi.org/10.3390/s22072490