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Current Signature Analysis to Detect Induction Motor Faults
Abstract
Three-phase induction motors are the "workhorses" of industry and are the most widely used electrical machines. In an industrialized nation, they can typically consume between 40 to 50% of all the generated capacity of that country. This article focuses on the industrial application of motor current signature analysis (MCSA) to diagnose faults in three-phase induction motor drives. MCSA is a noninvasive, online monitoring technique for the diagnosis of problems in induction motors. Reliability-based maintenance (RBM) and condition-based maintenance (CBM) strategies are now widely used by industry, and health monitoring of electrical drives is a major feature in such programs.
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Citations
Cited by
Advances in Diagnostic Techniques for Induction Machines
Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G.-A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2008.2007527 · Cited by 1,140
Review of condition monitoring of rotating electrical machines
Tavner, P. J. (2008) · IET Electric Power Applications · doi:10.1049/iet-epa:20070280 · Cited by 531
Machine Learning approach for Predictive Maintenance in Industry 4.0
Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018) · 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) · doi:10.1109/mesa.2018.8449150 · Cited by 296
Faiz, J., & Mazaheri-Tehrani, E. (2017) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2016.2608950 · Cited by 207
Gurusamy, V., Bostanci, E., Li, C., Qi, Y., & Akin, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3013128 · Cited by 93
Metadata
- Authors
- Thomson, W. T., & Fenger, M.
- Venue
- IEEE Industry Applications Magazine
- Published
- 2001-07
- Volume
- 7
- Issue
- 4
- Pages
- 26-34
- ISSN
- 1077-2618 (ISSN-L)
- 1558-0598
- OpenAlex
- W2026550072
- Cited by
- 1,139
- References
- 0
- FWCI
- 37.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Thomson, W. T., & Fenger, M. (2001). Current Signature Analysis to Detect Induction Motor Faults. IEEE Industry Applications Magazine. https://doi.org/10.1109/2943.930988
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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.
