Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform

This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. In this paper, we will study the problem of broken rotor bars, end-ring segment, and loss of stator phase during operation.

[1]  M. Riera-Guasp,et al.  Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines , 2006, IEEE Transactions on Industry Applications.

[2]  V. Fernão Pires,et al.  Unsupervised Neural-Network-Based Algorithm for an On-Line Diagnosis of Three-Phase Induction Motor Stator Fault , 2007, IEEE Transactions on Industrial Electronics.

[3]  M. E. H. Benbouzid,et al.  What Stator Current Processing Based Technique to Use for Induction Motor Rotor Faults Diagnosis , 2002, IEEE Power Engineering Review.

[4]  J. Faiz,et al.  A criterion function for broken bar fault diagnosis in induction motor under load variation using wavelet transform , 2007, 2007 International Conference on Electrical Machines and Systems (ICEMS).

[5]  Arturo Garcia-Perez,et al.  Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[6]  Pedro Vicente,et al.  Current-, force-, and vibration-based techniques for induction motor condition monitoring , 2007 .

[7]  Colin H. Hansen,et al.  Detection of broken rotor bars in induction motor using starting-current analysis and effects of loading , 2006 .

[8]  Luis Romeral,et al.  Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition , 2008, IEEE Transactions on Industrial Electronics.

[9]  N.Y. Abed,et al.  Modeling and characterization of induction motor internal faults using finite element and discrete wavelet transform , 2006, INTERMAG 2006 - IEEE International Magnetics Conference.

[10]  Kil To Chong,et al.  Induction Machine Condition Monitoring Using Neural Network Modeling , 2007, IEEE Transactions on Industrial Electronics.

[11]  Guy Clerc,et al.  Classification of Induction Machine Faults by Optimal Time–Frequency Representations , 2008, IEEE Transactions on Industrial Electronics.

[12]  Jian-Kang Zhang,et al.  Efficient design of orthonormal wavelet bases for signal representation , 2004, IEEE Transactions on Signal Processing.

[13]  Jose A. Antonino-Daviu,et al.  Diagnosis of Induction Motor Faults in the Fractional Fourier Domain , 2010, IEEE Transactions on Instrumentation and Measurement.

[14]  Tapan K. Sarkar,et al.  Wavelet applications in engineering electromagnetics , 2002 .

[15]  T. S. Radwan,et al.  Real-Time Implementation of Wavelet Packet Transform-Based Diagnosis and Protection of Three-Phase Induction Motors , 2007, IEEE Transactions on Energy Conversion.

[16]  T. Tarasiuk Hybrid wavelet-Fourier spectrum analysis , 2004, IEEE Transactions on Power Delivery.

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

[18]  Thomas G. Habetler,et al.  Analytic-Wavelet-Ridge-Based Detection of Dynamic Eccentricity in Brushless Direct Current (BLDC) Motors Functioning Under Dynamic Operating Conditions , 2007, IEEE Transactions on Industrial Electronics.

[19]  Tommy W. S. Chow,et al.  Induction machine fault diagnostic analysis with wavelet technique , 2004, IEEE Transactions on Industrial Electronics.

[20]  J. Cusido,et al.  Wavelet and PSD as a Fault Detection Techniques , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[21]  M. Victor Wickerhauser,et al.  Adapted wavelet analysis from theory to software , 1994 .

[22]  J. Antonino-Daviu,et al.  Application and Optimization of the Discrete Wavelet Transform for the Detection of Broken Rotor Bars in Induction Machines , 2006 .

[23]  Mohamed Benbouzid,et al.  Induction motors' faults detection and localization using stator current advanced signal processing techniques , 1999 .

[24]  Hamid A. Toliyat,et al.  Low Order PWM Inverter Harmonics Contributions to the Inverter-Fed Induction Machine Fault Diagnosis , 2008, IEEE Transactions on Industrial Electronics.

[25]  Martin Vetterli,et al.  Discrete-time wavelet extrema representation: design and consistent reconstruction , 1995, IEEE Trans. Signal Process..

[26]  Jin Huang,et al.  Rotor broken bars fault diagnosis for induction machines based on the wavelet ridge energy spectrum , 2005, 2005 International Conference on Electrical Machines and Systems.

[27]  Alireza Sadeghian,et al.  Current signature analysis of induction motor mechanical faults by wavelet packet decomposition , 2003, IEEE Trans. Ind. Electron..

[28]  Girish Kumar Singh,et al.  Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques , 2003 .

[29]  H. Henao,et al.  Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation , 2007, 2007 IEEE Industry Applications Annual Meeting.