Intelligent Diagnostic of Induction Machine for Faults Detection and Classification Using Wavelet and Fuzzy Inference

An intelligent diagnostic method based on 3-D plot continuous wavelet transform (3-D plot CWT) and fuzzy inference system is presented to investigate the detectability and classification of rotor broken bars faults in induction machine (IM) and to overcome the limitation of classical Fourier Transform (FT). This approach is successfully used with Motor Current Signature Analysis (MCSA) and suitable developed model of IM in healthy and faulty mode using Matlab environment. As first step we performed new results using 3-D plot CWT to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficients .Then as second and last step a fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no fault conditions. It is observed that the results are satisfactory.

[1]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[2]  David A. Cartes,et al.  Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines , 2013 .

[3]  Richard J. Povinelli,et al.  Rotor Bar Fault Monitoring Method Based on Analysis of Air-Gap Torques of Induction Motors , 2013, IEEE Transactions on Industrial Informatics.

[4]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[5]  Vicente Climente-Alarcon,et al.  Induction Motor Diagnosis Based on a Transient Current Analytic Wavelet Transform via Frequency B-Splines , 2011, IEEE Transactions on Industrial Electronics.

[6]  Jaya Sil,et al.  Fault diagnosis of induction motor using CWT and rough-set theory , 2013, 2013 IEEE Symposium on Computational Intelligence in Control and Automation (CICA).

[7]  Chul-Hwan Kim,et al.  Wavelet transforms in power systems Part 2 Examples of application to actual power system transients , 2001 .

[8]  N.A.O. Demerdash,et al.  Analysis and Diagnostics of Adjacent and Nonadjacent Broken-Rotor-Bar Faults in Squirrel-Cage Induction Machines , 2009, IEEE Transactions on Industrial Electronics.

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

[10]  Athanasios N. Safacas,et al.  Detection of Induction Motor Faults in Inverter Drives Using Inverter Input Current Analysis , 2011, IEEE Transactions on Industrial Electronics.

[11]  Richard J. Povinelli,et al.  Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-Phase Stator Current Envelopes , 2008, IEEE Transactions on Industrial Electronics.

[12]  Huseyin Eristi,et al.  Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system , 2013 .

[13]  A. Khezzar,et al.  Experimental investigation and comparative study of interturn short-circuits and unbalanced voltage supply in induction machines , 2013 .

[14]  Athanasios N. Safacas,et al.  Open-Circuit Fault Diagnosis for Matrix Converter Drives and Remedial Operation Using Carrier-Based Modulation Methods , 2014, IEEE Transactions on Industrial Electronics.

[15]  Mohamed A. Awadallah.  Fuzzy-based On-line Detection and Prediction of Switch Faults in the Brushless Excitation System of Synchronous Generators , 2010 .

[16]  Huaqing Wang,et al.  Intelligent Diagnosis Method for Rotating Machinery Using Wavelet Transform and Ant Colony Optimization , 2012, IEEE Sensors Journal.