Non-invasive on-line detection of winding faults in induction motors—A review

This paper is a review and evaluation of different on-line methods for diagnosing winding fault in induction motors presented in literature. Many methods can be found in literature; in some references, frequency analysis of motor signals such as current, speed, instantaneous power, Parkpsilas vector modulus and so on are introduced. Evaluation of negative sequence of current is also among the proposed methods. Supply voltage unbalance and some other phenomena may be confused with the winding fault. Therefore, the monitoring and fault detection of electrical machines have moved in recent years from traditional techniques to artificial intelligence based techniques. There are many other techniques that will be introduced and investigated in the detailed paper. A comparison of different methods introduced in literature is the main objective of this paper.

[1]  S. P. Verma,et al.  Vibration behaviour of stators of electrical machines, part I: Theoretical study , 1987 .

[2]  Hamid A. Toliyat,et al.  Diagnosis of stator, rotor and airgap eccentricity faults in three-phase induction motors based on the multiple reference frames theory , 2003, 38th IAS Annual Meeting on Conference Record of the Industry Applications Conference, 2003..

[3]  A. Cardoso,et al.  Diagnosis of stator inter-turn short circuits in DTC induction motor drives , 2004, IEEE Transactions on Industry Applications.

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

[5]  Vincent Cocquempot,et al.  A model of asynchronous machines for stator fault detection and isolation , 2003, IEEE Trans. Ind. Electron..

[6]  D. K. Perovic,et al.  Online stator fault diagnosis in induction motors , 2001 .

[7]  Hamid A. Toliyat,et al.  Transient analysis of cage induction machines under stator, rotor bar and end ring faults , 1995 .

[8]  M. Arkan,et al.  Induction motor fault detection by space vector angular fluctuation , 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).

[9]  Alexander G. Parlos,et al.  Induction motor fault diagnosis based on neuropredictors and wavelet signal processing , 2002 .

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

[11]  M. M. Morcos,et al.  Application of AI tools in fault diagnosis of electrical machines and drives-an overview , 2003 .

[12]  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).

[13]  Sun Li-ling,et al.  Detection of stator winding inter-turn short circuit fault in induction motors , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[14]  Hamid A. Toliyat,et al.  Novel frequency domain based technique to detect incipient stator inter-turn faults in induction machines , 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).

[15]  H. G. Sedding,et al.  Current monitoring for detecting inter-turn short circuits in induction motors , 2001 .

[16]  Humberto Henao,et al.  A frequency-domain detection of stator winding faults in induction machines using an external flux sensor , 2002 .

[17]  J. Penman,et al.  Detection and location of interturn short circuits in the stator windings of operating motors , 1994 .

[18]  J. Penman,et al.  Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines , 1994 .

[19]  G. Joksimovic,et al.  The detection of interturn short circuits in the stator windings of operating motors , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[20]  S. Williamson,et al.  Analysis of Cage Induction Motors with Stator Winding Faults , 1985, IEEE Power Engineering Review.

[21]  Xu-hong Wang,et al.  Fuzzy Model based On-line Stator Winding Turn Fault Detection for Induction Motors , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[22]  Peter Tavner,et al.  Monitoring generators and large motors , 1986 .

[23]  R. M. Tallam,et al.  A robust, on-line turn-fault detection technique for induction machines based on monitoring the sequence component impedance matrix , 2003 .

[24]  Mohamed Benbouzid,et al.  Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system , 2003 .

[25]  J.L. Kohler,et al.  Alternatives for assessing the electrical integrity of induction motors , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[26]  Gojko Joksimovic,et al.  The detection of inter-turn short circuits in the stator windings of operating motors , 2000, IEEE Trans. Ind. Electron..

[27]  T. G. Habetler,et al.  Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics , 2002 .

[28]  F. Filippetti,et al.  Induction machine stator fault on-line diagnosis based on LabVIEW environment , 1996, Proceedings of 8th Mediterranean Electrotechnical Conference on Industrial Applications in Power Systems, Computer Science and Telecommunications (MELECON 96).

[29]  C.J. Dister,et al.  Using temperature, voltage, and/or speed measurements to improve trending of induction motor RMS currents in process control and diagnostics , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[30]  Hamid A. Toliyat,et al.  Novel frequency-domain-based technique to detect stator interturn faults in induction machines using stator-induced voltages after switch-off , 2002 .

[31]  Sang Bin Lee,et al.  An on-line technique for monitoring the insulation condition of AC machine stator windings , 2005, IEEE International Conference on Electric Machines and Drives, 2005..

[32]  Fiorenzo Filippetti,et al.  Recent developments of induction motor drives fault diagnosis using AI techniques , 2000, IEEE Trans. Ind. Electron..

[33]  J. Kapler,et al.  Stator winding monitoring , 1998 .

[34]  Peter Tavner,et al.  Condition monitoring of electrical machines , 1987 .

[35]  F. Briz,et al.  On-line stator winding fault diagnosis in inverter-fed AC machines using high frequency signal injection , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).

[36]  Thomas G. Habetler,et al.  An unsupervised, on-line system for induction motor fault detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[37]  S. P. Verma,et al.  Vibration behaviour of stators of electrical machines, part II: Experimental study , 1987 .

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

[39]  William James Premerlani,et al.  A new approach to on-line turn fault detection in AC motors , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

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

[41]  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).

[42]  T. G. Habetler,et al.  Experimental testing of a neural-network-based turn-fault detection scheme for induction machines under accelerated insulation failure conditions , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[43]  Jim Penman,et al.  Induction machine condition monitoring with higher order spectra , 2000, IEEE Trans. Ind. Electron..

[44]  Sang Bin Lee,et al.  An online technique for monitoring the insulation condition of AC machine stator windings , 2005, IEEE Transactions on Energy Conversion.

[45]  F. Filippetti,et al.  Neural networks aided on-line diagnostics of induction motor rotor faults , 1993 .

[46]  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.

[47]  R. Yacamini,et al.  Experimental study of the vibrational behaviour of machine stators , 1996 .

[48]  F. Filippetti,et al.  AI techniques in induction machines diagnosis including the speed ripple effect , 1996 .

[49]  S. H. Chetwani,et al.  Online condition monitoring of induction motors using signature analysis , 2009 .