Multiple Faults Diagnosis of Induction Motor Using Artificial Neural Network

This paper presents multiple fault diagnosis and detection using artificial neural feed forward network. In this work analysis is done on induction motor, as these motor are widely used in industries because of their robustness, easy maintenance etc. The current and vibration responses of healthy motor, motor with bearing, rotor and stator defects are analysed. The feature extraction process is done in time domain only. From the results it is cleared that among various transfer functions in ANN the trainlm performs best and traingdm performs worst for fault detection.

[1]  Guy Clerc,et al.  The use of features selection and nearest neighbors rule for faults diagnostic in induction motors , 2006, Eng. Appl. Artif. Intell..

[2]  Xianghui Huang,et al.  Detection of Rotor Eccentricity Faults in a Closed-Loop Drive-Connected Induction Motor Using an Artificial Neural Network , 2007, IEEE Transactions on Power Electronics.

[3]  Alok K. Verma,et al.  Misalignment Faults Detection in an Induction Motor Based on Multi-scale Entropy and Artificial Neural Network , 2016 .

[4]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[5]  Vehbi C. Gungor,et al.  Online and Remote Motor Energy Monitoring and Fault Diagnostics Using Wireless Sensor Networks , 2009, IEEE Transactions on Industrial Electronics.

[6]  Mahmood Moghadasian,et al.  Induction motor fault diagnosis using ANFIS based on vibration signal spectrum analysis , 2017, 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP).

[7]  V. K. Giri,et al.  Feature selection and classification of mechanical fault of an induction motor using random forest classifier , 2016 .

[8]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

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

[10]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[11]  Bo-Suk Yang,et al.  Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference , 2009, Expert Syst. Appl..

[12]  Ruoyu Li,et al.  Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.

[13]  W. K. Ngui,et al.  Blade fault diagnosis using artificial neural network , 2017 .

[14]  Bong-Hwan Kwon,et al.  Online Diagnosis of Induction Motors Using MCSA , 2006, IEEE Transactions on Industrial Electronics.

[15]  Peter Vas,et al.  Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

[16]  Mohamed El Hachemi Benbouzid.  A review of induction motors signature analysis as a medium for faults detection , 2000, IEEE Trans. Ind. Electron..

[17]  Ye Zhongming,et al.  A review on induction motor online fault diagnosis , 2000, Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference (IEEE Cat. No.00EX435).

[18]  Bo-Suk Yang,et al.  Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors , 2007, Expert Syst. Appl..

[19]  Alessandro Goedtel,et al.  A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors , 2015 .