Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Dehong Liu Anantaram Varatharajan Abraham Goldsmith

Abstract

An effective way to detect broken-bar faults of squirrel-cage induction motors is to extract the characteristic frequency component in the stator current as a fault signature, or so-called motor current signature analysis (MCSA). However, for inverter-fed motor drive systems, the motor is typically operating under varying-speed, varying-load, and noisy environments, which makes the fault signature extraction a very challenging problem. In this paper, we propose a sparsity-driven and graph-based method to extract the fault signature effectively, where the fault signature is modeled as a sparse component in the frequency domain for each short-time window measurement while gradually changing from window to window in the time-domain. Compared to the conventional short-time Fourier transform-based method, our method is more robust to noise and varying speed operations. Experiments are carried out to demonstrate the effectiveness of the proposed method.  

Abstract 184 | PDF Downloads 117

##plugins.themes.bootstrap3.article.details##

Keywords

Broken-bar, Induction motor, Motor current signature analysis, Varying speed operation

References
Chen, S., Sandryhaila, A., Moura, J. M., & Kovacevic, J. (2014). Signal denoising on graphs via graph filtering. In 2014 ieee global conference on signal and information processing (globalsip) (pp. 872–876).

Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE transactions on information theory, 41(3), 613–627.

Fernandez-Cavero, V., Morinigo-Sotelo, D., Duque-Perez, O., & Pons-Llinares, J. (2017). A comparison of techniques for fault detection in inverter-fed induction motors in transient regime. IEEE Access, 5, 8048–8063.

Garcia-Calva, T. A., Morinigo-Sotelo, D., Garcia-Perez, A., Camarena-Martinez, D., & de Jesus Romero-Troncoso, R. (2019). Demodulation technique for broken rotor bar detection in inverter-fed induction motor under non-stationary conditions. IEEE Transactions on Energy Conversion, 34(3), 1496–1503.

Kelkar, V. A., Liu, D., Inoue, H., & Kanemaru, M. (2023). Sparsity-driven joint blind deconvolutiondemodulation with application to motor fault detection. In Icassp 2023-2023 ieee international conference on acoustics, speech and signal processing (icassp).

Krause, P. C., Wasynczuk, O., Sudhoff, S. D., & Pekarek, S. D. (2013). Analysis of electric machinery and drive systems (Vol. 75). John Wiley & Sons.

Liu, D., Chen, S., & Boufounos, P. T. (2020). Graphbased array signal denoising for perturbed synthetic aperture radar. In Igarss 2020-2020 ieee international geoscience and remote sensing symposium (pp. 1881– 1884).

Liu, D., Inoue, H., & Kanemaru, M. (2022). Robust motor current signature analysis (mcsa)-based fault detection under varying operating conditions. In 2022 25th international conference on electrical machines and systems (icems) (pp. 1–5).

Liu, D., & Lu, D. (2015). Off-the-grid compressive sensing for broken-rotor-bar fault detection in squirrel-cage induction motors. IFAC-PapersOnLine, 48(21), 1451– 1456.

Wang, Y., Yin, W., & Zeng, J. (2019). Global convergence of admm in nonconvex nonsmooth optimization. Journal of Scientific Computing, 78, 29–63.
Section
Special Session Papers