Experimental evaluation of a broken rotor bar fault detection scheme based on Uncertainty Bounds violation

This paper proposed a new technique for an experimental evaluation of a broken rotor bar fault detection based on Uncertainty Bounds violation. The novelty of this article stems from the establishment and the experimental evaluation of fault detection scheme being able to detect faults at the beginning of its occurrence, based on Set Membership Identification and novel proposed boundary violation rules for the identified motor's parameters. By the utilization of the SMI technique, the simplified equivalent model of the induction motor is being identified during the steady state operation (non-fault case), while at the same time safety bounds for the identified variables are being provided, based on an a priori defined corrupting additive noise. On the event of a fault, specific fault detection conditions are being proposed that can capture the fault of a broken bar. Detailed analysis of the proposed approach as also extended experimental results are being presented that prove the efficiency of the proposed scheme.

[1]  Mohamed Benbouzid,et al.  Induction Motors Bearing Failures Detection and Diagnosis Using a RBF ANN Park Pattern Based Method , 2006 .

[2]  R. Zivanovic,et al.  A novel high-resolution technique for induction machine broken bar detection , 2007, 2007 Australasian Universities Power Engineering Conference.

[3]  Anthony Tzes,et al.  Fault Detection based on Orthotopic Set Membership Identification for Robot Manipulators , 2008 .

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

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

[6]  George Nikolakopoulos,et al.  Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines , 2013, Expert Syst. Appl..

[7]  George Nikolakopoulos,et al.  A Fault diagnosis scheme for three phase induction motors based on uncertainty bounds , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[8]  F. Filippetti,et al.  Neural networks aided on-line diagnostics of induction motor rotor faults , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[9]  Gerardo G. Acosta,et al.  A current monitoring system for diagnosing electrical failures in induction motors , 2006 .

[10]  Zdravko Valter.  Electrical machines and drives with Matlab , 2009 .

[11]  Hamid A. Toliyat,et al.  Condition monitoring and fault diagnosis of electrical machines-a review , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[12]  Thomas Gustafsson,et al.  Broken Bar Fault Detection based on Set Membership Identification for Three Phase Induction Motors , 2012, ICINCO.

[13]  B. Mirafzal,et al.  On innovative methods of induction motor interturn and broken-bar fault diagnostics , 2006, IEEE Transactions on Industry Applications.

[14]  Mohamed Benbouzid,et al.  Monitoring and diagnosis of induction motors electrical faults using a current Park's vector pattern learning approach , 2000 .

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

[16]  Norman Mariun,et al.  Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review , 2011 .

[17]  G. Nikolakopoulos,et al.  Stator winding short circuit fault detection based on set membership identification for three phase induction motors , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[18]  M.F. Abdel-Magied,et al.  Fault detection of rotating machinery using model-based techniques , 1997, Proceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066).

[19]  Robert X. Gao,et al.  Broken-Rotor-Bar Diagnosis for Induction Motors , 2011 .

[20]  Rastko Zivanovic,et al.  Modelling and simulation of stator and rotor fault conditions in induction machines for testing fault diagnostic techniques , 2009 .

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

[22]  A.J. Marques Cardoso,et al.  Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park's vector approach , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[23]  J. Ilonen,et al.  Diagnosis tool for motor condition monitoring , 2005, IEEE Transactions on Industry Applications.

[24]  W. T. Thomson,et al.  Current And Vibration Monitoring For Fault Diagnosis And Root Cause Analysis Of Induction Motor Drives. , 2002 .

[25]  M. Milanese,et al.  Set membership identification of nonlinear systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[26]  O. Ondel,et al.  A method to detect broken bars in induction machine using pattern recognition techniques , 2006, IEEE Transactions on Industry Applications.

[27]  Fredrik Gustafsson,et al.  Adaptive Filtering and Change Detection: Gustafsson: Adaptive , 2001 .

[28]  W. T. Thomson,et al.  Fault detection in induction motors as a result of transient analysis , 1989 .

[29]  J. Deller.  Set membership identification in digital signal processing , 1989, IEEE ASSP Magazine.

[30]  Cursino B. Jacobina,et al.  A Simplified Induction Machine Model to Study Rotor Broken Bar Effects and for Detection , 2006 .