A novel non-invasive method for detecting missing wedges in an induction machine

This paper presents a newly developed algorithm for evaluating the health of an induction machine. The proposed algorithm is based on spectrum analysis of an impedance calculated using measured stator current and voltage signals. The main idea is to calculate the frequency spectrum of the impedance for each power phase and compare specific differences between phases. Experimental investigations show that the method yields very accurate results and can form an important part of a machine monitoring system. In particular the presented method is shown to be successful in detecting missing wedges in electric motors.

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

[2]  V. Jain,et al.  High-Accuracy Analog Measurements via Interpolated FFT , 1979, IEEE Transactions on Instrumentation and Measurement.

[3]  Principal Consultant,et al.  Problems and Solutions with Magnetic Stator Wedges , 2007 .

[4]  R.A. Hanna,et al.  Failure Analysis of Three Slow-Speed Induction Motors for Reciprocating Load Application , 2007, IEEE Transactions on Industry Applications.

[5]  D. R. Rankin.  The industrial application of phase current analysis to detect rotor winding faults in squirrel cage induction motors , 1995 .

[6]  H. Razik,et al.  Fault detection of broken rotor bars in induction motor using a global fault index , 2006, IEEE Transactions on Industry Applications.

[7]  G.C. Soukup,et al.  Cause and analysis of stator and rotor failures in 3-phase squirrel cage induction motors , 1991, Conference Record of 1991 Annual Pulp and Paper Industry Technical Conference.

[8]  Robert Boorstyn,et al.  Single tone parameter estimation from discrete-time observations , 1974, IEEE Trans. Inf. Theory.

[9]  Giovanni Franceschini,et al.  On-field experience with online diagnosis of large induction motors cage failures using MCSA , 2002 .

[10]  S. Ahmed,et al.  Detection of Rotor Slot and Other Eccentricity-Related Harmonics in a Three-Phase Induction Motor with Different Rotor Cages , 2001, IEEE Power Engineering Review.

[11]  T. Grandke.  Interpolation Algorithms for Discrete Fourier Transforms of Weighted Signals , 1983, IEEE Transactions on Instrumentation and Measurement.

[12]  Peter J. Kootsookos,et al.  Threshold behavior of the maximum likelihood estimator of frequency , 1994, IEEE Trans. Signal Process..

[13]  Peter Vas,et al.  Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines , 1993 .

[14]  Barry G. Quinn,et al.  Estimating frequency by interpolation using Fourier coefficients , 1994, IEEE Trans. Signal Process..

[15]  Yoon-Ho Kim,et al.  Neural network based speed sensorless induction motor drives with Kalman filter approach , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[16]  Long Wu.  Separating Load Torque Oscillation and Rotor Faults in Stator Current Based-Induction Motor Condition Monitoring , 2006 .

[17]  Maciej Orman,et al.  Parameter identification and slip estimation of induction machine , 2011 .

[18]  A. H. Bonnett,et al.  Analysis of rotor failures in squirrel-cage induction motors , 1988 .

[19]  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..

[20]  Xiaohui Xie.  Threshold behaviour of the maximum likelihood method in population decoding , 2002, Network.