Induction machine model with space harmonics for the diagnosis of rotor eccentricity, based on the convolution theorem

Abstract Condition based maintenance (CBM) systems of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults. Such models are needed to develop new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, these models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. However, the development of a fast, analytical model of the eccentric IM is a challenging task, due to the non-uniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, first enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position, taking into account the non-uniform air-gap length and the actual position of all the stator and rotor conductors. Once these inductances are calculated, they are used in a coupled circuits analytical model of the IM, which in this way is able to calculate the time evolution of the electrical and mechanical quantities that characterize the machine functioning, under any type of eccentricity. Specifically, the model is able to reproduce accurately the characteristic eccentricity fault related harmonics in the spectrum of the stator current. The proposed approach is validated through two different methods. First, using a finite elements (FEM) model, in order to validate the correctness of the proposed method for calculating self and mutual inductances, taking into account the non-uniform air-gap. Finally, through an experimental test-bed using a commercial induction motor with a forced mixed eccentricity fault, in order to validate that the full model correctly reproduces the phase currents in such a way that their spectra accurately show the harmonics related with the eccentricity fault, which are the basis of many MCSA diagnostic approaches.

[1]  Jawad Faiz,et al.  Eccentricity fault detection – From induction machines to DFIG—A review , 2016 .

[2]  J. Penman,et al.  Dynamic simulation of dynamic eccentricity in induction machines-winding function approach , 2000 .

[3]  Ranganath Muthu,et al.  Model Based Fault Detection and Diagnosis of Doubly Fed Induction Generators – A Review , 2017 .

[4]  A. Arkkio,et al.  Numerical Analysis of the Power Balance of an Electrical Machine With Rotor Eccentricity , 2016, IEEE Transactions on Magnetics.

[5]  Hubert Razik,et al.  An improved model of induction motors for diagnosis purposes – Slot skewing effect and air–gap eccentricity faults , 2009 .

[6]  C. J. Slavik,et al.  Effects Rotor Eccentricity and Parallel Windings on Induction Machine Behavior: A study Using Finite element Analysis , 1992, Digest of the Fifth Biennial IEEE Conference on Electromagnetic Field Computation.

[7]  Jawad Faiz,et al.  Detection of mixed eccentricity fault in doubly-fed induction generator based on reactive power spectrum , 2017 .

[8]  Gérard-André Capolino,et al.  Advances in Diagnostic Techniques for Induction Machines , 2008, IEEE Transactions on Industrial Electronics.

[9]  David G. Dorrell,et al.  A Review of the Monitoring and Damping Unbalanced Magnetic Pull in Induction Machines Due to Rotor Eccentricity , 2019, IEEE Transactions on Industry Applications.

[10]  S. Nandi,et al.  Performance Analysis of a Three-Phase Induction Machine With Inclined Static Eccentricity , 2007, IEEE Transactions on Industry Applications.

[11]  Alon Kuperman,et al.  Simple mechanical parameters identification of induction machine using voltage sensor only , 2015 .

[12]  Jawad Faiz,et al.  Finite Element Transient Analysis of an On-Load Three-Phase Squirrel-Cage Induction Motor with Static Eccentricity , 2007 .

[13]  Jawad Faiz,et al.  An evaluation of inductances of a squirrel-cage induction motor under mixed eccentric conditions , 2003 .

[14]  B. Haque,et al.  The principles of electromagnetism applied to electrical machines , 1962 .

[15]  Martin Riera-Guasp,et al.  Induction machine model with space harmonics for fault diagnosis based on the convolution theorem , 2018, International Journal of Electrical Power & Energy Systems.

[16]  Xiaohua Bao,et al.  Current analysis of large submersible motor under curved eccentricity by multi-loop method , 2017 .

[17]  Gurmeet Singh,et al.  Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis , 2018, Mechanical Systems and Signal Processing.

[18]  Karim Abbaszadeh,et al.  Magnet Defect and Rotor Eccentricity Modeling in Axial-Flux Permanent-Magnet Machines via 3-D Field Reconstruction Method , 2016, IEEE Transactions on Energy Conversion.

[19]  A. Khezzar,et al.  Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor , 2017 .

[20]  Julio E. Normey-Rico,et al.  Fault Analysis, Detection and Estimation for a Microgrid via H2/H∞ LPV Observers , 2019, International Journal of Electrical Power & Energy Systems.

[21]  Martin Valtierra-Rodriguez,et al.  Incipient Broken Rotor Bar Detection in Induction Motors Using Vibration Signals and the Orthogonal Matching Pursuit Algorithm , 2018, IEEE Transactions on Instrumentation and Measurement.

[22]  Nicola Bianchi,et al.  Eccentricity in Synchronous Reluctance Motors—Part I: Analytical and Finite-Element Models , 2015, IEEE Transactions on Energy Conversion.

[23]  C. Di,et al.  Modeling and Analysis of Unbalanced Magnetic Pull in Cage Induction Motors With Curved Dynamic Eccentricity , 2015, IEEE Transactions on Magnetics.

[24]  Q. Han,et al.  A general electromagnetic excitation model for electrical machines considering the magnetic saturation and rub impact , 2018 .

[25]  Fushuan Wen,et al.  A fuzzy Petri net based approach for fault diagnosis in power systems considering temporal constraints , 2016 .

[26]  Gerasimos Rigatos,et al.  Power transformers’ condition monitoring using neural modeling and the local statistical approach to fault diagnosis , 2016 .

[27]  Rajiv Tiwari,et al.  A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case , 2019, Measurement.

[28]  Subhojit Ghosh,et al.  Enhancing resilience of PV-fed microgrid by improved relaying and differentiating between inverter faults and distribution line faults , 2019, International Journal of Electrical Power & Energy Systems.

[29]  Subhasis Nandi,et al.  Comparison of results for eccentric cage induction motor using Finite Element method and Modified Winding Function Approach , 2010, 2010 Joint International Conference on Power Electronics, Drives and Energy Systems & 2010 Power India.

[30]  B. Heller,et al.  Harmonic field effects in induction machines , 1977 .

[31]  J. Faiz,et al.  Comprehensive Eccentricity Fault Diagnosis in Induction Motors Using Finite Element Method , 2009, IEEE Transactions on Magnetics.

[32]  Jawad Faiz,et al.  Modeling and Diagnosing Eccentricity Fault Using Three-dimensional Magnetic Equivalent Circuit Model of Three-phase Squirrel-cage Induction Motor , 2015 .

[33]  Greg Heins,et al.  Computationally Efficient Method for Identifying Manufacturing Induced Rotor and Stator Misalignment in Permanent Magnet Brushless Machines , 2016 .

[34]  J. Faiz,et al.  Extension of winding function theory for nonuniform air gap in electric machinery , 2002 .

[35]  Quan Yin,et al.  A novel method based on self-sensing motor drive system for misalignment detection , 2019 .

[36]  Jawad Faiz,et al.  Unified winding function approach for dynamic simulation of different kinds of eccentricity faults in cage induction machines , 2009 .

[37]  H. A. Toliyat,et al.  Performance Analysis of a Three-Phase Induction Motor under Mixed Eccentricity Condition , 2002, IEEE Power Engineering Review.

[38]  B ParvathiSangeetha,et al.  Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor , 2019, IEEE Trans. Ind. Informatics.

[39]  Ming Yang,et al.  A novel approach to transformer fault diagnosis using IDM and naive credal classifier , 2019 .

[40]  Ian Culbert,et al.  Signature Analysis for Online Motor Diagnostics: Early Detection of Rotating Machine Problems Prior to Failure , 2017, IEEE Industry Applications Magazine.

[41]  Mohamed Benbouzid,et al.  Induction machine faults detection using stator current parametric spectral estimation , 2015 .

[42]  Roque A. Osornio-Rios,et al.  Induction Motor Failure Analysis: An Automatic Methodology Based on Infrared Imaging , 2018, IEEE Access.

[43]  Aurobinda Routray,et al.  Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length , 2017, IEEE Transactions on Instrumentation and Measurement.

[44]  M.I. Valla,et al.  A 2-D model of the induction machine: an extension of the modified winding function approach , 2004, IEEE Transactions on Energy Conversion.

[45]  Martin Riera-Guasp,et al.  Partial Inductance Model of Induction Machines for Fault Diagnosis , 2018, Sensors.

[46]  Xin Xia,et al.  A novel method for fault diagnosis of hydro generator based on NOFRFs , 2015 .

[47]  Jan Staszak,et al.  Determination of slot leakage inductance for three-phase induction motor winding using an analytical method , 2013 .

[48]  Mohammad Farshad,et al.  Detection and classification of internal faults in bipolar HVDC transmission lines based on K-means data description method , 2019, International Journal of Electrical Power & Energy Systems.

[49]  Jawad Faiz,et al.  Instantaneous-Power Harmonics as Indexes for Mixed Eccentricity Fault in Mains-Fed and Open/Closed-Loop Drive-Connected Squirrel-Cage Induction Motors , 2009, IEEE Transactions on Industrial Electronics.

[50]  Galina Mirzaeva,et al.  Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement , 2018, IEEE Transactions on Industry Applications.

[51]  Antero Arkkio,et al.  A 2D FEM analysis of electromechanical signatures in induction motors under dynamic eccentricity , 2014 .

[52]  Chandrabhan Sharma,et al.  A review of induction motor fault modeling , 2016 .

[53]  Peyman Naderi,et al.  Eccentricity fault diagnosis in three‐phase‐wound‐rotor induction machine using numerical discrete modeling method , 2016 .

[54]  Ratna Dahiya,et al.  Current signature analysis and its application in the condition monitoring of wind turbine for rotor faults , 2017 .

[55]  C. Di,et al.  Analysis of Dynamic Unbalanced Magnetic Pull in Induction Motor With Dynamic Eccentricity During Starting Period , 2016, IEEE Transactions on Magnetics.

[56]  Thomas A. Lipo,et al.  Analysis of a concentrated winding induction machine for adjustable speed drive applications. I. Motor analysis , 1991 .

[57]  D G Dorrell,et al.  Sources and Characteristics of Unbalanced Magnetic Pull in Three-Phase Cage Induction Motors With Axial-Varying Rotor Eccentricity , 2011, IEEE Transactions on Industry Applications.

[58]  A. Ghoggal,et al.  A Fast Inductance Computation Devoted to the Modeling of Healthy, Eccentric, and Saturated Induction Motors , 2013 .

[59]  K. P. Vittal,et al.  Comprehensive study of mixed eccentricity fault diagnosis in induction motors using signature analysis , 2012 .

[60]  Mohamed Yazid Kaikaa,et al.  Effects of the simultaneous presence of static eccentricity and broken rotor bars on the stator current of induction machine , 2014, IEEE Transactions on Industrial Electronics.

[61]  M. Ikeda,et al.  Simulation Studies of the Transients of Squirrel-Cage Induction Motors , 2007, IEEE Transactions on Energy Conversion.

[62]  Mansour Ojaghi,et al.  Model-based exact technique to identify type and degree of eccentricity faults in induction motors , 2016 .

[63]  Yiqi Liu,et al.  A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. , 2017, ISA transactions.