The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art
Open accessAbstract
Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.
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Citations
Cites
An Industry-Based Survey of Reliability in Power Electronic Converters
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Self-citationSurvey on Reliability of Power Electronic Systems
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Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques
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Self-citationRecent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors
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A New Approach for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Source Inverters
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Open-Circuit Fault Diagnosis in PMSG Drives for Wind Turbine Applications
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Cited by
An Overview of Artificial Intelligence Applications for Power Electronics
Zhao, S., Blaabjerg, F., & Wang, H. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3024914 · Cited by 947
Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges
Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M., & Kowalski, C. T. (2022) · IEEE Access · doi:10.1109/access.2022.3180153 · Cited by 328
“Over the last 30 years, many review articles on the FDD of electrical machines have been published, however, most of them concern the induction motors (IM) [7]-[13], due to the fact that, until recently, they accounted for more than 90% of all electric motors installed in industry [1]-[3].”
BackgroundChoi, S., Haque, M. S., Tarek, M. T. B., Mulpuri, V., Duan, Y., Das, S., Garg, V., Ionel, D. M., Masrur, M. A., Mirafzal, B., et al. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2018.2819627 · Cited by 192
A deep learning approach for fault diagnosis of induction motors in manufacturing
Shao, S.-Y., Sun, W.-J., Yan, R.-Q., Wang, P., & Gao, R. X. (2017) · Chinese Journal of Mechanical Engineering · doi:10.1007/s10033-017-0189-y · Cited by 167
Cost-Effective Three-Phase PMSM Drive Tolerant to Open-Phase Fault
Kontarcek, A., Bajec, P., Nemec, M., Ambrozic, V., & Nedeljkovic, D. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2015.2437357 · Cited by 106
Metadata
- Authors
- Riera-Guasp, M., Antonino-Daviu, J. A., & Capolino, G.-A.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015-03
- Volume
- 62
- Issue
- 3
- Pages
- 1746-1759
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2014526686
- Cited by
- 573
- References
- 65
- FWCI
- 46.90
- Type
- journal article
- Language
- en
- Open access
- green
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- Riera-Guasp, M., Antonino-Daviu, J. A., & Capolino, G.-A. (2015). Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2014.2375853
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.