Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-367-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-6-367-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Mar 2021
Research article |
| 05 Mar 2021
| 05 Mar 2021
Condition monitoring of roller bearings using acoustic emission
Daniel Cornel
CORRESPONDING AUTHOR
Chair for Wind Power Drives (CWD), RWTH Aachen University, 52074 Aachen, Germany
Francisco Gutiérrez Guzmán
Chair for Wind Power Drives (CWD), RWTH Aachen University, 52074 Aachen, Germany
Georg Jacobs
Chair for Wind Power Drives (CWD), RWTH Aachen University, 52074 Aachen, Germany
Stephan Neumann
Chair for Wind Power Drives (CWD), RWTH Aachen University, 52074 Aachen, Germany
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Cited
21 citations as recorded by crossref.
- Recent Advances of Artificial Intelligence Methods in PMSM Condition Monitoring and Fault Diagnosis in Elevator Systems V. Vlachou et al. https://doi.org/10.3390/asi8050154
- Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings B. Scheeren et al. https://doi.org/10.1177/14759217231164912
- Design and Implementation of a Misalignment Experimental Data Management Platform for Wind Power Equipment J. Cao et al. https://doi.org/10.3390/en18195047
- Acoustic virtual sensors for industrial process monitoring using non-negative matrix factorization C. Luzon-Alvarez et al. https://doi.org/10.1186/s13636-025-00417-2
- Uncertainty-involved evaluation of real-time lubrication states in tilting pad thrust bearings P. Dou et al. https://doi.org/10.1016/j.ymssp.2026.114352
- Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery I. Hassan et al. https://doi.org/10.3390/data9050069
- Early-Stage Damage Diagnosis of Rolling Bearings Based on Acoustic Emission Signals Interpreted by Friction Behavior and Machine Learning T. Nakai et al. https://doi.org/10.3390/lubricants14020095
- Experimental Investigation of Crack Detection in Ring Gears of Wind Turbine Gearboxes Using Acoustic Emissions F. Leaman et al. https://doi.org/10.1007/s42417-023-00970-0
- Hybrid methodology development for lubrication regimes identification based on measurements, simulation, and data clustering J. Tervo et al. https://doi.org/10.1016/j.triboint.2024.109631
- Acoustic emission fault detection and localization for rolling bearings N. Gregarek et al. https://doi.org/10.1177/13506501261448870
- Variable speed induction motors’ fault detection based on transient motor current signatures analysis: A review M. Yakhni et al. https://doi.org/10.1016/j.ymssp.2022.109737
- An In-Depth Study of Vibration Sensors for Condition Monitoring I. Hassan et al. https://doi.org/10.3390/s24030740
- Diagnostics of hydraulic machines using AI for predictive maintenance. A. Kindl et al. https://doi.org/10.1088/1755-1315/1561/1/012045
- A Lightweight Heterogeneous Neural Network Architecture for Multiscale Feature Extraction in Bearing Fault Diagnosis H. Nguyen & C. Kim https://doi.org/10.1109/ACCESS.2026.3694523
- KFD-AEEScan: a Katz fractal dimension acoustic emission event scanner for single-sensor multi-fault diagnosis in milling machines A. Maliuk et al. https://doi.org/10.1177/14759217261433437
- Wind turbine drivetrains: state-of-the-art technologies and future development trends A. Nejad et al. https://doi.org/10.5194/wes-7-387-2022
- Location- and time-resolved strain measurement in thrust roller bearings using thin-film sensors D. Konopka et al. https://doi.org/10.1007/s10010-025-00857-4
- Novel Vibration Diagnosis Technologies for Lubrication Deficiency in Rolling Bearings of Induction Motors L. Gelman & R. Kerrouche https://doi.org/10.3390/en19071741
- Vibration-Based Predictive Maintenance for Wind Turbines: A PRISMA-Guided Systematic Review on Methods, Applications, and Remaining Useful Life Prediction C. Constantino-Robles et al. https://doi.org/10.3390/applmech7010011
- Stand der Technik: Anschmierungen in Radial-Zylinderrollenlagern P. Stuhler & N. Nagler https://doi.org/10.1007/s10010-021-00490-x
- Research on bearing equipment fault diagnoses via SAWOA-LSTM Y. Li https://doi.org/10.21595/jme.2025.24805
21 citations as recorded by crossref.
- Recent Advances of Artificial Intelligence Methods in PMSM Condition Monitoring and Fault Diagnosis in Elevator Systems V. Vlachou et al. https://doi.org/10.3390/asi8050154
- Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings B. Scheeren et al. https://doi.org/10.1177/14759217231164912
- Design and Implementation of a Misalignment Experimental Data Management Platform for Wind Power Equipment J. Cao et al. https://doi.org/10.3390/en18195047
- Acoustic virtual sensors for industrial process monitoring using non-negative matrix factorization C. Luzon-Alvarez et al. https://doi.org/10.1186/s13636-025-00417-2
- Uncertainty-involved evaluation of real-time lubrication states in tilting pad thrust bearings P. Dou et al. https://doi.org/10.1016/j.ymssp.2026.114352
- Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery I. Hassan et al. https://doi.org/10.3390/data9050069
- Early-Stage Damage Diagnosis of Rolling Bearings Based on Acoustic Emission Signals Interpreted by Friction Behavior and Machine Learning T. Nakai et al. https://doi.org/10.3390/lubricants14020095
- Experimental Investigation of Crack Detection in Ring Gears of Wind Turbine Gearboxes Using Acoustic Emissions F. Leaman et al. https://doi.org/10.1007/s42417-023-00970-0
- Hybrid methodology development for lubrication regimes identification based on measurements, simulation, and data clustering J. Tervo et al. https://doi.org/10.1016/j.triboint.2024.109631
- Acoustic emission fault detection and localization for rolling bearings N. Gregarek et al. https://doi.org/10.1177/13506501261448870
- Variable speed induction motors’ fault detection based on transient motor current signatures analysis: A review M. Yakhni et al. https://doi.org/10.1016/j.ymssp.2022.109737
- An In-Depth Study of Vibration Sensors for Condition Monitoring I. Hassan et al. https://doi.org/10.3390/s24030740
- Diagnostics of hydraulic machines using AI for predictive maintenance. A. Kindl et al. https://doi.org/10.1088/1755-1315/1561/1/012045
- A Lightweight Heterogeneous Neural Network Architecture for Multiscale Feature Extraction in Bearing Fault Diagnosis H. Nguyen & C. Kim https://doi.org/10.1109/ACCESS.2026.3694523
- KFD-AEEScan: a Katz fractal dimension acoustic emission event scanner for single-sensor multi-fault diagnosis in milling machines A. Maliuk et al. https://doi.org/10.1177/14759217261433437
- Wind turbine drivetrains: state-of-the-art technologies and future development trends A. Nejad et al. https://doi.org/10.5194/wes-7-387-2022
- Location- and time-resolved strain measurement in thrust roller bearings using thin-film sensors D. Konopka et al. https://doi.org/10.1007/s10010-025-00857-4
- Novel Vibration Diagnosis Technologies for Lubrication Deficiency in Rolling Bearings of Induction Motors L. Gelman & R. Kerrouche https://doi.org/10.3390/en19071741
- Vibration-Based Predictive Maintenance for Wind Turbines: A PRISMA-Guided Systematic Review on Methods, Applications, and Remaining Useful Life Prediction C. Constantino-Robles et al. https://doi.org/10.3390/applmech7010011
- Stand der Technik: Anschmierungen in Radial-Zylinderrollenlagern P. Stuhler & N. Nagler https://doi.org/10.1007/s10010-021-00490-x
- Research on bearing equipment fault diagnoses via SAWOA-LSTM Y. Li https://doi.org/10.21595/jme.2025.24805
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs. This paper should form a basis for the implementation of a predictive maintenance system. Therefore an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs. The system has shown that a damaged surface can be detected at least ~ 4 % (8 h, regarding the time to failure) and possibly up to ~ 50 % (130 h) earlier than by using the vibration-based system.
Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair...
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