Induction motors broken rotor bars detection using RPVM and neural network

Purpose The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates. Design/methodology/approach First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs. Findings The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach. Originality/value The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).

[1]  K. McManama,et al.  The global HazLoc impact of the IECEx scheme , 2001 .

[2]  R. Puche-Panadero,et al.  Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip , 2009, IEEE Transactions on Energy Conversion.

[3]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[4]  Elhoussin Elbouchikhi,et al.  An Efficient Hilbert–Huang Transform-Based Bearing Faults Detection in Induction Machines , 2017, IEEE Transactions on Energy Conversion.

[5]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[6]  Robson Pederiva,et al.  Electrical fault diagnosis in induction motors using local extremes analysis , 2016 .

[7]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[8]  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.

[9]  Maury M. Gouvea,et al.  High impedance fault detection in power distribution systems using wavelet transform and evolving neural network , 2018 .

[10]  Antonio J. Marques Cardoso,et al.  Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's Vector approach , 1997 .

[11]  Mike Nicolai,et al.  Simulation-driven machine learning: Bearing fault classification , 2018 .

[12]  Imadeddine Harzelli,et al.  Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study , 2017 .

[13]  Fiorenzo Filippetti,et al.  Recent developments of induction motor drives fault diagnosis using AI techniques , 2000, IEEE Trans. Ind. Electron..

[14]  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.

[15]  W. T. Thomson,et al.  Current signature analysis to detect induction motor faults , 2001 .

[16]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[17]  G. S. Yadava,et al.  Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[18]  Makarand Sudhakar Ballal,et al.  Adaptive Neural Fuzzy Inference System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor , 2007, IEEE Transactions on Industrial Electronics.