Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor

It is well known that stator winding faults such the inter-turn short circuit are the most frequent source of breakdowns in induction motors. Early detection of any small inter-turn short circuit and location of the faulty phase at different load would eliminate some subsequent damage to adjacent coils and stator core, reducing then the repair cost. To achieve this purpose, the present paper presents a new method of diagnosis and detection of inter turn short circuit fault using discrete wavelet transform (DWT) and neural networks (NN). This method consists in analyzing the stator current by DWT in order to compute the energy associated with the stator fault in the frequency bandwidth. Then, this energy is used as input for a NN classifier. The results obtained are astonishing and the approach is able to detect any small number of shorted turns and the faulty phase even under different load of the machine.

[1]  Khaled Jelassi,et al.  An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor , 2008, IEEE Transactions on Industrial Electronics.

[2]  Khalaf Salloum Gaeid,et al.  Wavelet Fault Diagnosis of Induction Motor , 2011 .

[3]  V. K. Giri,et al.  Health Monitoring and Fault Diagnosis in Induction Motor-A Review , 2014 .

[4]  Slim Tnani,et al.  Application of the fourier and the wavelet transform for the fault detection in induction motors at the startup electromagnetic torque , 2011, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives.

[5]  Narayan C. Kar,et al.  Analysis of stator winding inter-turn short-circuit fault in interior and surface mounted permanent magnet traction machines , 2014, 2014 IEEE Transportation Electrification Conference and Expo (ITEC).

[6]  A. R. Mohanty,et al.  Fault Detection in a Multistage Gearbox by Demodulation of Motor Current Waveform , 2006, IEEE Transactions on Industrial Electronics.

[7]  Remus Pusca,et al.  Study of Rotor Faults in Induction Motors Using External Magnetic Field Analysis , 2012, IEEE Transactions on Industrial Electronics.

[8]  P. Purkait,et al.  Separating induction Motor Current Signature for stator winding faults from that due to supply voltage unbalances , 2012, 2012 1st International Conference on Power and Energy in NERIST (ICPEN).

[9]  Adel Belouchrani,et al.  Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform , 2011, IEEE Transactions on Industrial Electronics.

[10]  Slim Tnani,et al.  Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines , 2006, IEEE Transactions on Industrial Electronics.

[11]  Susmita Das,et al.  Induction motor stator inter-turn fault detection using wavelet transform technique , 2010, 2010 5th International Conference on Industrial and Information Systems.

[12]  Teresa Orlowska-Kowalska,et al.  Neural networks application for induction motor faults diagnosis , 2003, Math. Comput. Simul..

[13]  Arezki Menacer,et al.  Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. , 2014, ISA transactions.

[14]  Vilas N. Ghate,et al.  Optimal MLP neural network classifier for fault detection of three phase induction motor , 2010, Expert Syst. Appl..

[15]  Anjali U. Jawadekar,et al.  Novel Wavelet ANN Technique to ClassifyInterturn Fault in Three Phase Induction Motor , 2011 .

[16]  Mauridhi Hery Purnomo,et al.  Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network , 2012, Expert Syst. Appl..