Optimal Rotor Fault Detection in Induction Motor Using Particle-Swarm Optimization Optimized Neural Network

This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and the network outputs were rotor fault state, and the number of conductive bars with broken fault. Moreover, particle-swarm optimization algorithm was used to determine the optimal network weights and neuron penetration radius in the neural network. The results obtained from the proposed method showed the optimal and efficient performance of the method in detecting conductive bars broken fault in induction motor in low load conditions. doi: 10.5829/ije.2018.31.11b.11

[1]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[2]  M Tarafdar Hagha,et al.  Radial Basis Neural Network Based Islanding Detection in Distributed Generation , 2014 .

[3]  Lie Xu,et al.  Improvement of the Hilbert Method via ESPRIT for Detecting Rotor Fault in Induction Motors at Low Slip , 2013, IEEE Transactions on Energy Conversion.

[4]  B Bessam,et al.  Detection of broken rotor bar faults in induction motor at low load using neural network. , 2016, ISA transactions.

[5]  Mohd Amran Mohd Radzi,et al.  Fault Detection of Broken Rotor Bar in LS-PMSM Using Random Forests , 2017, ArXiv.

[6]  Zheng Chen,et al.  A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor , 2014 .

[7]  M. B. Abd-el-Malek,et al.  Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform , 2017 .

[8]  Hayde Peregrina-Barreto,et al.  Hilbert Spectrum Analysis of Induction Motors for the Detection of Incipient Broken Rotor Bars , 2017 .

[9]  Pawel Strumillo,et al.  Radial Basis Function Neural Networks: Theory and Applications , 2003 .

[10]  Giorgio Sulligoi,et al.  A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks , 2016 .

[11]  Wilson Wang,et al.  A Morphological Hilbert-Huang Transform Technique for Bearing Fault Detection , 2016, IEEE Transactions on Instrumentation and Measurement.

[12]  Wenjun Sun,et al.  Induction Motor Fault Diagnosis Based on Deep Neural Network of Sparse Auto-encoder , 2016 .

[13]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[14]  Pratyay Konar,et al.  Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform , 2015, Appl. Soft Comput..

[15]  Arezki Menacer,et al.  Rotor resistance estimation using Extended Kalman filter and spectral analysis for rotor bar fault diagnosis of sensorless vector control induction motor , 2017 .

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

[17]  Alireza Alfi,et al.  A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers , 2015, Appl. Soft Comput..

[18]  Alessandro Goedtel,et al.  Stator fault analysis of three-phase induction motors using information measures and artificial neural networks , 2017 .

[19]  Dejan Gojko Jerkan,et al.  Broken Rotor Bar Fault Detection of IM Based on the Counter-Current Braking Method , 2017, IEEE Transactions on Energy Conversion.