Mechanical fault detection in rotating electrical machines using MCSA-FFT and MCSA-DWT techniques
Treść / Zawartość
This paper presents mechanical fault detection in squirrel cage induction motors (SCIMs) by means of two recent techniques. More precisely, we have analyzed the rolling element bearing (REB) faults in SCIM. Rolling element bearing faults constitute a major problem among different faults which cause catastrophic damage to rotating machinery. Thus early detection of REB faults in SCIMs is of crucial importance. Vibration analysis is among the key concepts for mechanical vibrations of rotating electrical machines. Today, there is massive competition between researchers in the diagnosis field. They all have as their aim to replace the vibration analysis technique. Among them, stator current analysis has become one of the most important subjects in the fault detection field. Motor current signature analysis (MCSA) has become popular for detection and localization of numerous faults. It is generally based on fast Fourier transform (FFT) of the stator current signal. We have detailed the analysis by means of MCSA-FFT, which is based on the stator current spectrum. Another goal in this work is the use of the discrete wavelet transform (DWT) technique in order to detect REB faults. In addition, a new indicator based on the MCSA-DWT technique has been developed in this study. This new indicator has the advantage of expressing itself in the quantity and quality form. The acquisition data are presented and a comparative study is carried out between these recent techniques in order to ensure a final decision. The proposed subject is examined experimentally using a 3 kW squirrel cage induction motor test bed.
Bibliogr. 19 poz., wykr., rys., tab.
-  W.T. Thomson, “A review of on-line condition monitoring techniques for three-phase squirrel-cage induction motors – past, present and future”, in Proc. IEEE Int. Symp. SDEMPED 1, 3–18 (1999).
-  R.K. Patel and V.K. Giri, “Condition monitoring of induction motor bearing based on bearing damage index”, Archives of Electrical Engineering, 66(1), 105?119 (2017).
-  L. Saidi, J.B. Ali, E. Bechhoefer, and M. Benbouzid, “Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR”, Applied Acoustics 120, 1?8, (2017).
-  F.B. Abid, S. Zgarni, and A. Braham, “Distinct Bearing Faults Detection in Induction Motor by a Hybrid Optimized SWPT and aiNet-DAG SVM”, IEEE Trans. En.Conv. (2018).
-  A. Głowacz and Z. Głowacz, “Recognition of rotor damages in a DC motor using acoustic signals”, Bull. Pol. Ac.: Tech 65(2), 187?194 (2017).
-  B. Rachid, A. Hafaifa, and M. Boumehraz, “Vibrations Detection in Industrial Pumps based on Spectral Analysis to Increase Their Efficiency”, Manag. Syst. .Prod. Eng. 21(1), 55?61 (2016).
-  R. Dash and B. Subudhi, “Stator inter-turn fault detection of an induction motor using neuro-fuzzy techniques”, Archives of Control Sciences 20(3), 363?376 (2010).
-  C.T. Kowalski, and M. Kaminski, “Rotor fault detector of the converter-fed induction motor based on RBF neural network”, Bull. Pol. Ac.: Tech 62(1), 69?76 (2014).
-  R. Valles-Novo, J. de Jesus Rangel-Magdaleno, J.M. Ramirez- Cortes, H. Peregrina-Barreto, and R. Morales-Caporal, “Empirical mode decomposition analysis for broken-bar detection on squirrel cage induction motors”, IEEE Trans. Instr. Meas. 64(5), 1118?1128 (2015).
-  A.M. Júnior, V.V. Silva, L.M. Baccarini, and L.F. Mendes, “The design of multiple linear regression models using a genetic algorithm to diagnose initial short-circuit faults in 3-phase induction motors”, Applied Soft Computing, 63, 50?58 (2018).
-  G. Singh, and V.N.A. Naikan, “Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis”, Mechanical Systems and Signal Processing 110, 333?348 (2018).
-  A. Mejia-Barron, M. Valtierra-Rodriguez, D. Granados-Lieberman, J.C. Olivares-Galvan, and R. Escarela-Perez, “The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents”, Measurement 117, 371?379 (2018).
-  N. Bessous, S.E. Zouzou, and A. Chemsa, “A new analytical model dedicated to diagnose the rolling bearing damage in induction motors-simulation and experimental investigation,” in Control Engineering and Information Technology (CEIT) IEEE, 1?9, (2016).
-  M. Ojaghi and R. Akhondi, “Modeling Induction Motors Under Mixed Radial-Axial Asymmetry of the Air Gap Produced by Oil- Whirl Fault in a Sleeve Bearing”, IEEE Trans. Mag. (99) (2018).
-  C. Wang, X. Bao, S. Xu, Y. Zhou, W. Xu, and Y. Chen, “Analysis of Vibration and Noise for Different Skewed Slot-Type Squirrel-Cage Induction Motors”, IEEE Trans. Mag. 53(11), 1?6 (2017).
-  C. Kumar, G. Krishnan, and S. Sarangi, “Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis”, in Futuristic Trends on Computational Analysis and Knowledge Management IEEE, 61?66 (2015).
-  A. Ibrahim, “Contribution au diagnostic de machines électromécaniques: Exploitation des signaux électriques et de la vitesse instantanée”, Doctoral dissertation, Université Jean Monnet- Saint-Etienne, (2009).
-  J.A. Antonino-Daviu, M. Riera-Guasp, J.R. Folch, and M.P.M. Palomares, “Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines”, IEEE Trans. Ind. App. 42(4), 990?996 (2006).
-  N. Bessous, S.E. Zouzou, W. Bentrah, S. Sbaa, and M. Sahraoui, “Diagnosis of bearing defects in induction motors using discrete wavelet transform”, International Journal of System Assurance Engineering and Management 9(2), 335?343 (2018).
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).