Demagnetization diagnosis in permanent magnet synchronous motors under non-stationary speed conditions

Permanent magnet synchronous motors (PMSMs) are applied in high performance positioning and variable speed applications because of their enhanced features with respect to other AC motor types. Fault detection and diagnosis of electrical motors for critical applications is an active field of research. However, much research remains to be done in the field of PMSM demagnetization faults, especially when running under non-stationary conditions. This paper presents a time–frequency method specifically focused to detect and diagnose demagnetization faults in PMSMs running under non-stationary speed conditions, based on the Hilbert Huang transform. The effectiveness of the proposed method is proven by means of experimental results.

[1]  J. Sleigh,et al.  Analysis of depth of anesthesia with Hilbert–Huang spectral entropy , 2008, Clinical Neurophysiology.

[2]  C. Guedes Soares,et al.  Analysis of Abnormal Wave Records by the Hilbert–Huang Transform Method , 2007 .

[3]  T.G. Habetler,et al.  Dynamic Eccentricity and Demagnetized Rotor Magnet Detection in Trapezoidal Flux (Brushless DC) Motors Operating Under Different Load Conditions , 2007, IEEE Transactions on Power Electronics.

[4]  Hubert Razik,et al.  An induction machine model including interbar currents for studying performances during transients and steady state , 2009 .

[5]  Ruqiang Yan,et al.  A Tour of the Tour of the Hilbert-Huang Transform: An Empirical Tool for Signal Analysis , 2007, IEEE Instrumentation & Measurement Magazine.

[6]  Alireza Sadeghian,et al.  Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System , 2006 .

[7]  P. Flandrin,et al.  Empirical Mode Decomposition , 2012 .

[8]  M. Riera-Guasp,et al.  An Analytical Comparison between DWT and Hilbert-Huang-Based Methods for the Diagnosis of Rotor Asymmetries in Induction Machines , 2007, 2007 IEEE Industry Applications Annual Meeting.

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  T.G. Habetler,et al.  Detecting faults in rotors of PM drives , 2008, IEEE Industry Applications Magazine.

[11]  Bruno Allard,et al.  A predictive current control applied to a permanent magnet synchronous machine, comparison with a classical direct torque control , 2008 .

[12]  N. Senroy,et al.  Generator Coherency Using the Hilbert–Huang Transform , 2008, IEEE Transactions on Power Systems.

[13]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[14]  Humberto Henao,et al.  Induction machine fault detection using stray flux EMF measurement and neural network-based decision , 2008 .

[15]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[16]  John F. Doherty,et al.  Signal Feature Extraction From Microbarograph Observations Using the Hilbert–Huang Transform , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[17]  R.B. Perez,et al.  Structural integrity monitoring of steam generator tubing using transient acoustic signal analysis , 2005, IEEE Transactions on Nuclear Science.

[18]  Yu Yang,et al.  Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis , 2007 .

[19]  T.G. Habetler,et al.  Detecting Rotor Faults in Low Power Permanent Magnet Synchronous Machines , 2007, IEEE Transactions on Power Electronics.

[20]  T. Sebastian,et al.  Fault analysis of a PM brushless DC Motor using finite element method , 2005, IEEE Transactions on Energy Conversion.

[21]  Abdülkadir Çakır,et al.  Rotor bar fault diagnosis in three phase induction motors by monitoring fluctuations of motor current zero crossing instants , 2007 .

[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]  Chung-Shi Tseng,et al.  Robust PID control design for permanent magnet synchronous motor: A genetic approach , 2008 .

[24]  Henry C. Thacher,et al.  Applied and Computational Complex Analysis. , 1988 .

[25]  D. M. McFarland,et al.  Toward a Fundamental Understanding of the Hilbert-Huang Transform in Nonlinear Structural Dynamics , 2006 .

[26]  Y. Gritli,et al.  The combined use of the instantaneous fault frequency evolution and frequency sliding for advanced rotor fault diagnosis in DFIM under time-varying condition , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[27]  A.R. Messina,et al.  Identification of instantaneous attributes of torsional shaft signals using the Hilbert transform , 2004, IEEE Transactions on Power Systems.

[28]  S. Mallat A wavelet tour of signal processing , 1998 .

[29]  Domenico Casadei,et al.  Magnets faults characterization for Permanent Magnet Synchronous Motors , 2009, 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[30]  M. Gabsi,et al.  Inverse modelling and pulsating torque minimization of salient pole non-sinusoidal synchronous machines , 2008 .

[31]  Asok Ray,et al.  Early detection of stator voltage imbalance in three-phase induction motors , 2009 .