Fault diagnosis of induction motor using CWT and rough-set theory

The paper proposes a Rough-Set CWT based algorithm for multi-class fault diagnosis of induction motor. Use of powerful signal processing technique like CWT drastically reduces the hardware (sensor) requirement of the diagnostic system. Only axial vibration signal is enough to classify seven different types of motor faults. Moreover, successful application of Rough Set theory has enabled to select most relevant CWT scales and corresponding coefficients. Thus, the inherent deficiencies and limitations of CWT are eliminated. Consequently, the computational efficiency has also improved to a great extend. With reduction of attributes by 65% the classification accuracy of the classifiers is very consistent even in presence of high level of noise and with a low frequency sampling frequency of 5120 Hz.

[1]  Ngoc-Tu Nguyen,et al.  Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor , 2008 .

[2]  Sankar K. Pal,et al.  Case generation using rough sets with fuzzy representation , 2004, IEEE Transactions on Knowledge and Data Engineering.

[3]  A. Ramachandra Rao,et al.  Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series , 2008 .

[4]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[5]  R. K. Patel,et al.  Induction motor bearing fault identification using vibration measurement , 2012, 2012 Students Conference on Engineering and Systems.

[6]  Sujan Rajbhandari,et al.  The use of linear projections in the visual analysis of signals in an indoor optical wireless link , 2010, 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010).

[7]  C. P. Chandran,et al.  Feature selection from protein primary sequence database using Enhanced QuickReduct Fuzzy-Rough set , 2008, 2008 IEEE International Conference on Granular Computing.

[8]  Subrata Karmakar,et al.  Novel diagnosis technique of mass unbalance in rotor of induction motor by the analysis of motor starting current at no load through wavelet transform , 2010, International Conference on Electrical & Computer Engineering (ICECE 2010).

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

[10]  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..

[11]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..

[12]  Bo-Suk Yang,et al.  Fault Diagnosis System of Induction Motors Using Feature Extraction, Feature Selection and Classification Algorithm , 2006 .

[13]  Ieee Staff.  2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) , 2014 .

[14]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[15]  Aldo W. Morales,et al.  Aplicaciones de wavelets en la detección de fallas de máquinas de inducción , 2010 .

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

[17]  Bo-Suk Yang,et al.  Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors , 2007, Expert Syst. Appl..

[18]  Moumita Saha,et al.  Dimensionality reduction using genetic algorithm and fuzzy-rough concepts , 2011, 2011 World Congress on Information and Communication Technologies.

[19]  Aldo W. Morales,et al.  Applications of wavelets in induction machine fault detection , 2008 .

[20]  Qiang Shen,et al.  Rough set-based dimensionality reduction for supervised and unsupervised learning , 2001 .

[21]  Khalaf Salloum Gaeid,et al.  Wavelet fault diagnosis and tolerant of induction motor: A review , 2011 .

[22]  Girish Kumar Singh,et al.  Induction machine drive condition monitoring and diagnostic research—a survey , 2003 .

[23]  Hui Li,et al.  Application of Hermitian wavelet to crack fault detection in gearbox , 2011 .

[24]  Yuchang Lu,et al.  Feature ranking in rough sets , 2003, AI Commun..

[25]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.