TR2022-150

Robust Motor Current Signature Analysis (MCSA)-based Fault Detection under Varying Operating Conditions


    •  Liu, D., Inoue, H., Kanemaru, M., "Robust Motor Current Signature Analysis (MCSA)-based Fault Detection under Varying Operating Conditions", 2022 International Conference on Electrical Machines and Systems, DOI: 10.1109/​ICEMS56177.2022.9983454, November 2022.
      BibTeX TR2022-150 PDF
      • @inproceedings{Liu2022nov2,
      • author = {Liu, Dehong and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {Robust Motor Current Signature Analysis (MCSA)-based Fault Detection under Varying Operating Conditions},
      • booktitle = {2022 25th International Conference on Electrical Machines and Systems},
      • year = 2022,
      • month = nov,
      • publisher = {IEEE},
      • doi = {10.1109/ICEMS56177.2022.9983454},
      • issn = {2642-5513},
      • isbn = {978-1-6654-9302-4},
      • url = {https://www.merl.com/publications/TR2022-150}
      • }
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  • Research Areas:

    Electric Systems, Signal Processing

Abstract:

Motor current signature analysis (MCSA) has been widely used in motor fault detection including bearing fault, broken-bar, and eccentricity, etc. When a motor’s fault is in its early stage or a faulty motor is operating in varying load conditions, fault signature may be submerged in the background noise and interference, making fault detection a very challenging problem. In this paper, we address the problem of extracting small fault signature of frequency components under a varying load condition and a noisy background. To this end, we segment the time-domain stator current into overlapped sequences, and treat each sequence as an independent measurement of an imaginary sensor. A minimum variance beam-forming method is then employed to generate the current frequency spectrum with robust performance under varying-load operations. Our method is validated with experimental data collected on a motor with a minor eccentricity fault operating in varying conditions.