TR2023-107
Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches
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- "Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches", IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), DOI: 10.1109/SDEMPED54949.2023.10271414, August 2023, pp. 42-48.BibTeX TR2023-107 PDF
- @inproceedings{Wang2023aug,
- author = {Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
- title = {Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches},
- booktitle = {2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)},
- year = 2023,
- pages = {42--48},
- month = aug,
- publisher = {IEEE},
- doi = {10.1109/SDEMPED54949.2023.10271414},
- url = {https://www.merl.com/publications/TR2023-107}
- }
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- "Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches", IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED), DOI: 10.1109/SDEMPED54949.2023.10271414, August 2023, pp. 42-48.
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MERL Contact:
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Research Areas:
Abstract:
Fault detection using motor current signature analysis (MCSA) is attractive for industrial applications due to its simplicity with no additional sensor installation required. However current components associated with faults are often very subtle and much smaller than the supply frequency component, making it challenging to detect and quantify fault levels. In this paper, we present our work on quantitative eccentricity fault diagnosis technologies for electric motors, including physical- model approach using improved winding function theory, which can simulate motor dynamics under faulty conditions and agrees well with experiment data, and data-driven approach using topo- logical data analysis (TDA), which can effectively differentiate signals measured at different eccentricity levels. The advantages and limitations of each approach is discussed. Both methods can be extended to the detection and quantification of other types of electric motor faults.
Related News & Events
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AWARD Best Paper Award at SDEMPED 2023 Date: August 30, 2023
Awarded to: Bingnan Wang, Hiroshi Inoue, and Makoto Kanemaru
MERL Contact: Bingnan Wang
Research Areas: Applied Physics, Data Analytics, Multi-Physical ModelingBrief- MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.
SDEMPED was established as the only international symposium entirely devoted to the diagnostics of electrical machines, power electronics and drives. It is now a regular biennial event. The 14th version, SDEMPED 2023 was held in Chania, Greece from August 28th to 31st, 2023.
- MERL and Mitsubishi Electric's paper titled “Motor Eccentricity Fault Detection: Physics-Based and Data-Driven Approaches” was awarded one of three best paper awards at the 14th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED 2023). MERL Senior Principal Research Scientist Bingnan Wang presented the paper and received the award at the symposium. Co-authors of the paper include Mitsubishi Electric researchers Hiroshi Inoue and Makoto Kanemaru.