Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
University of Maryland, Baltimore · Ulsan National Institute of Science and Technology · +2 more institutions
Abstract
Abstract Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web…
Citation impact
- FWCI
- 28.38
- Percentile
- 100%
- References
- 111
Authors
4Topics & keywords
- Troubleshooting
- Computer science
- Vibration
- Deep learning
- Variety (cybernetics)
- Artificial intelligence
- Fault (geology)
- Predictive maintenance
Funding
- NRNational Research FoundationAward: 2020R1A2C1009744
- UNUlsan National Institute of Science and TechnologyAward: 1.220083
- MOMinistry of Trade, Industry and EnergyAwards: 2020R1A2C1009744, 19-CM-GU-01, 20206610100290
- NRNational Research Foundation of KoreaAwards: 2020R1A2C1009744, 2021R1F1A1046416
- IFInstitute for Basic ScienceAward: IBS-R029-C2-001
- MOMinistry of Science and ICT, South KoreaAward: 2020R1A2C1009744
- IOInstitute of Civil-Military Technology CooperationAward: 19-CM-GU-01
- DADefense Acquisition Program AdministrationAward: 19-CM-GU-01
- KIKorea Institute of Energy Technology Evaluation and PlanningAward: 20206610100290