![]() ![]() Garcia-Perez A, Romero-Troncoso RJ, Cabal-Yepez E, Osornio-Rios RA (2011) The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. Kim YH, Youn YW, Hwang DH, Sun JH, Kang DS (2013) High-resolution parameter estimation method to identify broken rotor bar faults in induction motors. In: 8th IEEE symposium on diagnostics for electrical machines, power electronics & drives pp 664–668. Menacer A, Kechida R, Champenois G, Tnani S (2011) Application of the Fourier and the Wavelet transform for the fault detection in induction motors at the startup electromagnetic torque. Yang T, Pen H, Wang Z, Chang CS (2016) Feature knowledge based fault detection of induction motors through the analysis of stator current data. Int J Electr Power Energy Syst 35(1):180–185. Rajalakshmi Samaga BL, Vittal KP (2012) Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis. In: Proceedings of the CPE-POWERENG: 10th international conference on compatibility, power electronics and power engineering (CPE-POWERENG) pp 298–303. Pires JVF, Martins JF, Pires AJ, Rodrigues L (2016) Induction motor broken bar fault detection based on MCSA, MSCSA and PCA: a comparative study. Enabling the diagnosis of rotor asymmetries at very low slip. Puche-Panadero R, Pineda-Sanchez M, Riera-Guasp M, Roger-Folch J, Hurtado-Perez E, Perez-Cruz J (2009) Improved resolution of the MCSA method via Hilbert transform. Khelif MA, Bendiabdellah A, Cherif BDE (2019) A combined RMS-MEAN value approach for an inverter open-circuit fault detection. In: Proceedings of the CISTEM’18: 3rd international conference on electrical sciences and technologies in Maghreb (CISTEM’18), pp 1–6. ĭehina W, Boumehraz M, Kratz F (2018) Diagnosis of rotor and stator faults by fast Fourier transform and discrete wavelet in induction machine. Moussa MA, Boucherma M, Khezzar A (2017) A detection method for induction motor bar fault using sidelobes leakage phenomenon of the sliding DFT. The performances of these approaches are demonstrated in simulation results using the MATLAB environment and in the experimental validation. In this context, the results exhibit the effectiveness of the methodology to detect induction machine fault in time varying, it is capable to detect a rotor failure. ![]() This article is intended for a comparative study between the spectrogram, the scalogram and the Hilbert-Huang transform. However, for the diagnosis in time varying conditions, non-stationary approaches are required to diagnose and detection IM failures in variable speed operation or transient. To address these problems, the Multiple Signal Classification technique allows for reducing the spectrum noises and to reduce the computation of signal data samples, requires less memory. Sadly, the Fast Fourier transform technique cannot give good results such as the spectral leakage, it needs a big number of measurement data samples. These methods can be classified into: high resolution approaches and time–frequency representations. The proposed techniques are based on advanced signal processing tools. This paper investigated the ability of the diagnosis techniques and detectability of induction motor faults through a stator current. ![]()
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