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Diagnosis of bearing defects in induction motors using discrete wavelet transform

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Abstract

The analysis of motor current signature analysis was used many years ago, but the fast Fourier transform (FFT) technique has some disadvantages under some conditions when the speed and the load torque are not constants. The FFT has problems due to a non-stationary signal if we must report accurately the frequency characteristics of the defects. Discrete wavelets transform (DWT) treats the non-stationary stator current signal, which becomes complex when it has noises. In this paper, a technique of de-noising signals is presented by the stator current based on a series of decomposition which are compared with respect to each other. We studied a normal bearings and bearings with outer and inner faults. The choice of the decomposition order was for: Daubechies, Symlets and Meyer. The limit point of determination of the levels number is presented. In addition, we look for informations about the basic defect signal on the energy stored in each level of decomposition. DWT has the ability to allow simultaneous time–frequency analysis, so it is an appropriate tool for studying transient phenomena and non-stationary signals.

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References

  • Abbaszadeh K, Milimonfared J, Haji M, Toliyat HA (2001) Broken bar detection in induction motor via wavelet transformation. In: IEEE industrial electronics society, IECON’O1, the 27th annual conference, p 95–99

  • Antonino-Daviu JA, Riera-Guasp M, Folch JR, Palomares MPM (2006) A method for the diagnosis of rotor bar failures in induction machines. IEEE Trans Ind Appl 42(4):990–996

    Article  Google Scholar 

  • Bae H, Kim YT, Lee SH, Kim S, Lee MH (2005) Fault diagnostic of induction motors for equipment reliability and health maintenance based upon Fourier and wavelet analysis. Artif Life Robot 9(3):112–116

    Article  Google Scholar 

  • Boukra T, Lebaroud A, Clerc G (2013) Statistical and neural-network approaches for the classification of induction machine faults using the ambiguity plane representation. IEEE Trans Ind Electron 60(9):4034–4042

    Article  Google Scholar 

  • Ceban A, Pusca R, Romary R (2012) Study of rotor faults in induction motors using external magnetic field analysis. IEEE Trans Ind Electron 59(5):2082–2093

    Article  Google Scholar 

  • Chattopadhyay P, Konar P (2014) Feature extraction using wavelet transform for multi-class fault detection of induction motor. J Inst Eng 95(1):73–81

    Google Scholar 

  • Faiz J, Ebrahimi BM, Akin B, Toliyat HA (2009) Comprehensive eccentricity fault diagnosis in induction motors using finite element method. IEEE Trans Magn 45(3):1764–1767

    Article  Google Scholar 

  • Foo GHB, Zhang X, Vilathgamuwa DM (2013) A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter. IEEE Trans Ind Electron 60(8):3485–3495

    Article  Google Scholar 

  • Guedidi S, Zouzou SE, Laala W, Yahia K, Sahraoui M (2013) Induction motors broken rotor bars detection using MCSA and neural network: experimental research. Int J Syst Assur Eng Manag 4(2):173–181

    Article  Google Scholar 

  • Gyftakis KN, Kappatou JC (2013) A novel and effective method of static eccentricity diagnosis in three-phase PSH induction motors. IEEE Trans Energy Convers 28(2):405–412

    Article  Google Scholar 

  • Laala W, Zouzou SE, Guedidi S (2014) Induction motor broken rotor bars detection using fuzzy logic: experimental research. Int J Syst Assur Eng Manag 5(3):329–336

    Article  Google Scholar 

  • Lau ECC, Ngan HW (2010) Detection of motor bearing outer raceway defect by wavelet packet transformed motor current signature analysis. IEEE Trans Instrum Meas 59(10):2683–2690

    Article  Google Scholar 

  • Mohammed OA, Abed NY, Garni SC (2006) Modeling and characterization of induction motor internal faults using finite element and discrete wavelet transforms. IEEE Trans Magn 42(10):3434–3436

    Article  Google Scholar 

  • Nandi S, Ahmed S, Toliyat HA (2001) Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages. IEEE Trans Energy Convers 16(3):253–260

    Article  Google Scholar 

  • Ocak H, Loparo KA (2004) Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mech Syst Signal Process 18(3):515–533

    Article  Google Scholar 

  • Önel IY, Dalci KB, Senol I (2006) Detection of bearing defects in three-phase induction motors using Park’s transform and radial basis function neural networks. Sadhana 31(3):235–244

    Article  Google Scholar 

  • Rigatos G, Siano P, Zervos N (2013) An approach to fault diagnosis of nonlinear systems using neural networks with invariance to Fourier transform. J Ambient Intell Humaniz Comput 4(6):621–639

    Article  Google Scholar 

  • Rodríguez PVJ, Arkkio A (2008) Detection of stator winding fault in induction motor using fuzzy logic. Appl Soft Comput 8(2):1112–1120

    Article  Google Scholar 

  • Sahraoui M, Ghoggal A, Zouzou SE, Benbouzid ME (2008) Dynamic eccentricity in squirrel cage induction motors–simulation and analytical study of its spectral signatures on stator currents. Simul Model Pract Theory 16(9):1503–1513

    Article  Google Scholar 

  • Schoen RR, Habetler TG, Kamran F, Bartheld RG (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 31(6):1274–1279

    Article  Google Scholar 

  • Soualhi A, Clerc G, Razik H (2013) Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Trans Ind Electron 60(9):4053–4062

    Article  Google Scholar 

  • Trajin B, Regnier J, Faucher J (2009) Comparison between stator current and estimated mechanical speed for the detection of bearing wear in asynchronous drives. IEEE Trans Ind Electron 56(11):4700–4709

    Article  Google Scholar 

  • Vedreno-Santos F, Riera-Guasp M, Henao H, Pineda-Sánchez M, Puche-Panadero R (2014) Diagnosis of rotor and stator asymmetries in wound-rotor induction machines under nonstationary operation through the instantaneous frequency. IEEE Trans Ind Electron 61(9):4947–4959

    Article  Google Scholar 

  • Verma A, Sarangi S (2015) Fault diagnosis of broken rotor bars in induction motor using multiscale entropy and backpropagation neural network. In: Intelligent computing and applications, p 393–404

  • Yazici B, Kliman GB (1999) An adaptive statistical time–frequency method for detection of broken bars and bearing faults in motors using stator current. IEEE Trans Ind Appl 35(2):442–452

    Article  Google Scholar 

  • Ye Z, Wu B, Sadeghian A (2003) Current signature analysis of induction motor mechanical faults by wavelet packet decomposition. IEEE Trans Ind Electron 50(6):1217–1228

    Article  Google Scholar 

  • Zarei J, Tajeddini MA, Karimi HR (2014) Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2):151–157

    Article  Google Scholar 

  • Zhang L, Xiong G, Liu H, Zou H, Guo W (2010) Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst Appl 37(8):6077–6085

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Electrical Engineering (LGEB), Department of Electrical Engineering, Mohamed Khider University, 07000, Biskra, Algeria

    N. Bessous, S. E. Zouzou & M. Sahraoui

  2. LI3CUB Laboratory, Department of Electronics, Mohamed Khider University, 07000, Biskra, Algeria

    W. Bentrah & S. Sbaa

Authors
  1. N. Bessous
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  2. S. E. Zouzou
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  3. W. Bentrah
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  4. S. Sbaa
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  5. M. Sahraoui
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Correspondence to N. Bessous.

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Bessous, N., Zouzou, S.E., Bentrah, W. et al. Diagnosis of bearing defects in induction motors using discrete wavelet transform. Int J Syst Assur Eng Manag 9, 335–343 (2018). https://doi.org/10.1007/s13198-016-0459-6

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  • DOI: https://doi.org/10.1007/s13198-016-0459-6

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