Abstract
Induction motor diagnosis using the Power Spectral Density estimation or PSD, based on the Fourier Transform calculation, is not recommended for the processing of non stationary signals (case of variable speed applications). In fact, under these conditions, the analysis with this approach is no more reliable. To resolve this, we use in this paper, the Short Time Fourier Transform (STFT), to obtain information on changes of the frequencies over time. Furthermore, we propose the use of a new approach called Maxima’s Location Algorithm (MLA) which will be associated to the STFT analysis to show only harmonics with useful information on existing faults. This approach will be used in the diagnosis of bearing faults of a PWM inverter-fed induction motor operating at variable speed. Experimental results show the merits of the proposed approach on the reliability of the bearing fault detection.
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References
Bonnett, A.H., Yung, C.: Increased efficiency versus increased reliability. Ind. Appl. Mag. IEEE 14, 29–36 (2008)
Boudinar, A.H., Bendiabdellah, A., Benouzza, N., Ferradj, M.: Improved stator current spectral analysis technique for bearing faults diagnosis. In: 16th International Power Electronics and Motion Control Conference and Exposition, Antalya, Turkey, 21–24 September 2014
Immovilli, F., Bellini, A., Rubini, R., Tassoni, C.: Diagnosis of bearing faults in induction machines by vibration or current signals: a critical comparison. IEEE Trans. Ind. Appl. 46, 1350–1360 (2010)
Garcia-Perez, A., Romero-Troncoso, R., Cabal-Yepez, E., Osornio-Rios, R.A.: The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans. Ind. Electron. 58, 2002–2011 (2011)
Concari, C., Franceschini, G., Tassoni, C.: Differential diagnosis based on multivariable monitoring to assess induction machine rotor conditions. IEEE Trans. Ind. Electron. 55, 4156–4167 (2008)
Aïmer, A.F., Boudinar, A.H., Bendiabdellah, A.: Use of the short time fourier transform for induction motor broken bars detection. In: International Review on Modelling and Simulations, vol. 16, 6th edn., December 2013
Jung, J.H., Lee, J.J., Kwon, B.H.: Online diagnosis of induction motors using MCSA. IEEE Trans. Ind. Electron. 53, 1842–1853 (2006)
Mehala, N., Dahiya, R.: Detection of bearing faults of induction motor using park’s vector approach. Int. J. Eng. Technol. 2(4), 263–266 (2010)
El Bouchikhi, E., Choqueuse, V., Benbouzid, M.E.H.: Current frequency spectral subtraction and its contribution to induction machines’ bearings condition monitoring. IEEE Trans. Energy Convers. 28(1), 135–144 (2012)
Gong, X., Qiao, W.: Bearing fault detection for direct-drive wind turbines via stator current spectrum analysis. In: Proceedings of IEEE Energy Conversion Congress and Exposition (ECCE) (2011)
El Bouchikhi, E., Choqueuse, V., Benbouzid, M.E.H.: Induction machine faults detection using stator current parametric spectral estimation. In: Mechanical Systems and Signal Processing, pp. 447–464. Elsevier (2014)
Aïmer, A.F., Boudinar, A.H., Bendiabdellah, A., Mokhtar, C.: Effet du fenêtrage sur la résolution de la DSP et son apport dans le diagnostic des défauts rotoriques du moteur asynchrone. In: Proceedings of International Conference on Industrial Engineering and Manufacturing, Batna, 09–10 May 2010, Algeria
Amirat, Y., Choqueuse, V., Benbouzid, M.E.H., Turri, S.: Hilbert transform based bearing failure detection in DFIG-based wind turbines. Int. Rev. Electr. Eng. 6(3), 1249–1256 (2011)
Espinosa, A.G., Rosero, A., Cusido, J., Romeral, L., Ortega, J.A.: Fault detection by means of Hilbert–Huang transform of the stator current in a PMSM with demagnetization. IEEE Trans. Energy Convers 25(2), June 2010
Aïmer, A.F., Boudinar, A.H., Benouzza, N., Bendiabdellah, A.: Simulation and experimental study of induction motor broken rotor bars fault diagnosis using stator current spectrogram. In: Proceedings of IEEE 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, Algeria, 25–27 May 2015
El Ahmar, E., Choqueuse, V., Benbouzid, M.E.H., Amirat, Y., El Assad, J.: Advanced signal processing techniques for fault detection and diagnosis in a wind turbine induction generator drive train: a comparative study. In: Proceedings of IEEE Energy Conversion Congress and Exposition (ECCE), September 2010, Atlanta, United States, pp. 3576–3581 (2010)
Climente-Alarcon, V., Antonino-Daviu, J.A., Riera-Guasp, M., Vlcek, M.: Induction motor diagnosis by advanced notch FIR filters and the Wigner-Ville distribution. IEEE Trans. Ind. Electron. 61(8), 4217–4227 (2014)
Henao, H., et al.: Trends in fault diagnosis for electric machines: a review of diagnostic methods. IEEE Ind. Electron. Mag. 8(2), 31–42 (2014)
Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Processing. Elsevier (2014)
Llinares, J.P., Antonino-Daviu, J.A., Riera-Guasp, M., Pineda-Sanchez, M., Climente-Alarcon, V.: Induction motor diagnosis based on a transient current analytic wavelet transform via frequency B-splines. IEEE Trans. Ind. Electron. 58(5), 530–1544 (2011)
Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., Rezzoug, A.: Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans. Ind. Electron. 58(9), 4385–4395 (2011)
Gaeid, K.S., Ping, H.W.: Wavelet fault diagnosis and tolerant of induction motor: a review. Int. J. Phys. Sci. 6(3), 358–376 (2011)
Blödt, M., Granjon, P., Raison, B., Rostaing, G.: Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55, 1813–1823 (2008)
Boudinar, A.H., Benouzza, N., Bendiabdellah, A., Khodja, M.: Induction motor bearing fault analysis using root-MUSIC method. IEEE Trans. Ind. Appl. 52(5), 3851–3860 (2016)
Bendiabdellah, A., Boudinar, A.H., Benouzza, N., Khodja, M.: The enhancements of broken bar fault detection in induction motors. In: Proceedings of International Aegean Conference on Electrical Machines & Power Electronics (ACEMP), International Conference on Optimization of Electrical & Electronic Equipment (OPTIM), International Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION), 02–04 September 2015, Side, Turkey
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Boudinar, A.H., Aimer, A.F., Khodja, M.E.A., Benouzza, N. (2019). Induction Motor’s Bearing Fault Diagnosis Using an Improved Short Time Fourier Transform. In: Chadli, M., Bououden, S., Ziani, S., Zelinka, I. (eds) Advanced Control Engineering Methods in Electrical Engineering Systems. ICEECA 2017. Lecture Notes in Electrical Engineering, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-97816-1_31
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DOI: https://doi.org/10.1007/978-3-319-97816-1_31
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