Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0
DOI:
https://doi.org/10.2478/ecce-2018-0013Keywords:
Fault diagnosis, Induction motors, Inverse problemsAbstract
Digitalization of the industrial sector and Industry 4.0 have opened new horizons in many technical fields, including electrical machine diagnostics and operation, as well as machine condition monitoring. This paper addresses a selection of electrical machine diagnostics methods that are applicable for the use in the perspective of Industry 4.0, to be used in hand with cloud environments and the possibilities granted by the Internet of Things. The need for further research and development in the field is pointed out. Some potentially applicable future approaches are presented.References
M. E. H. Benbouzid, M. Vieira, and C. Theys, “Induction Motors’ Faults Detection and Localization Using Stator Current Advanced Signal Processing Techniques,” IEEE Trans. Power Electron., vol. 14, no. 1, pp. 14–22, 1999. https://doi.org/10.1109/63.737588
M. Hermann, T. Pentek, and B. Otto, “Design Principles for Industrie 4.0 Scenarios,” in 2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928–3937. https://doi.org/10.1109/hicss.2016.488
S. Nandi, H. A. Toliyat, and X. Li, “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719–729, Dec. 2005. https://doi.org/10.1109/tec.2005.847955
B. Asad, T. Vaimann, A. Belahcen, and A. Kallaste, “Broken Rotor Bar Fault Diagnostic of Inverter Fed Induction Motor Using FFT, Hilbert and Park’s Vector Approach,” in 2018 XIII International Conference on Electrical Machines (ICEM), 2018, pp. 2352–2358.
M. Karimi-Ghartemani, S. A. Khajehoddin, P. K. Jain, A. Bakhshai, and M. Mojiri, “Addressing DC Component in PLL and Notch Filter Algorithms,” IEEE Trans. Power Electron., vol. 27, no. 1, pp. 78–86, Jan. 2012. https://doi.org/10.1109/tpel.2011.2158238
Z. Xin, X. Wang, Z. Qin, M. Lu, P. C. Loh, and F. Blaabjerg, “An Improved Second-Order Generalized Integrator Based Quadrature Signal Generator,” IEEE Trans. Power Electron., vol. 31, no. 12, pp. 8068–8073, Dec. 2016. https://doi.org/10.1109/tpel.2016.2576644
M. Malekpour, B. T. Phung, and E. Ambikairajah, “Stator Current Envelope Extraction for Analysis of Broken Rotor Bar in Induction Motors,” in 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2017, pp. 240–246. https://doi.org/10.1109/demped.2017.8062393
B. Mirafzal and N. A. O. Demerdash, “Induction Machine Broken-Bar Fault Diagnosis Using the Rotor Magnetic Field Space-Vector Orientation,” IEEE Trans. Ind. Appl., vol. 40, no. 2, pp. 534–542, Mar. 2004. https://doi.org/10.1109/tia.2004.824433
R. Roy, A. Paulraj, and T. Kailath, “ESPRIT-A Subspace Rotation Approach to Estimation of Parameters of Cisoids in Noise,” IEEE Trans. Acoust., vol. 34, no. 5, pp. 1340–1342, Oct. 1986. https://doi.org/10.1109/tassp.1986.1164935
R. Roy and T. Kailath, “ESPRIT-Estimation of Signal Parameters via Rotational Invariance Techniques,” IEEE Trans. Acoust., vol. 37, no. 7, pp. 984–995, Jul. 1989. https://doi.org/10.1109/29.32276
B. Ottersten, M. Viberg, and T. Kailath, “Performance Analysis of the Total Least Squares ESPRIT Algorithm,” IEEE Trans. Signal Process., vol. 39, no. 5, pp. 1122–1135, May 1991. https://doi.org/10.1109/78.80967
X.-D. Zhang and Y.-C. Liang, “Prefiltering-Based ESPRIT for Estimating Sinusoidal Parameters in Non-Gaussian ARMA Noise,” IEEE Trans. Signal Process., vol. 43, no. 1, pp. 349–353, 1995. https://doi.org/10.1109/78.365327
V. F. Pisarenko, “The Retrieval of Harmonics from a Covariance Function,” Geophys. J. Int., vol. 33, no. 3, pp. 347–366, Sep. 1973. https://doi.org/10.1111/j.1365-246X.1973.tb03424.x
R. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation,” IEEE Trans. Antennas Propag., vol. 34, no. 3, pp. 276–280, Mar. 1986. https://doi.org/10.1109/TAP.1986.1143830
B. Xu, L. Sun, L. Xu, and G. Xu, “Improvement of the Hilbert Method via ESPRIT for Detecting Rotor Fault in Induction Motors at Low Slip,” IEEE Trans. Energy Convers., vol. 28, no. 1, pp. 225–233, Mar. 2013. https://doi.org/10.1109/TEC.2012.2236557
R. Puche-Panadero, M. Pineda-Sanchez, M. Riera-Guasp, J. Roger-Folch, E. Hurtado-Perez, and J. Perez-Cruz, “Improved Resolution of the MCSA Method via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip,” IEEE Trans. Energy Convers., vol. 24, no. 1, pp. 52–59, Mar. 2009. https://doi.org/10.1109/TEC.2008.2003207
E. Elbouchikhi, V. Choqueuse, and M. Benbouzid, “Induction Machine Bearing Faults Detection Based on a Multi-Dimensional MUSIC Algorithm and Maximum Likelihood Estimation,” ISA Trans., vol. 63, pp. 413–424, 2016. https://doi.org/10.1016/j.isatra.2016.03.007
S. Pan, T. Han, A. C. C. Tan, and T. R. Lin, “Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm,” Shock Vib., vol. 2016, no. January, 2016. https://doi.org/10.1155/2016/5836717
T. A. Garcia-Calva, D. Morinigo-Sotelo, and R. De Jesus Romero-Troncoso, “Non-Uniform Time Resampling for Diagnosing Broken Rotor Bars in Inverter-Fed Induction Motors,” IEEE Trans. Ind. Electron., vol. 64, no. 3, pp. 2306–2315, 2017. https://doi.org/10.1109/TIE.2016.2619318
Y. Trachi, E. Elbouchikhi, V. Choqueuse, and M. E. H. Benbouzid, “Induction Machines Fault Detection Based on Subspace Spectral Estimation,” IEEE Trans. Ind. Electron., vol. 63, no. 9, pp. 5641–5651, Sep. 2016. https://doi.org/10.1109/TIE.2016.2570741
A. Garcia-Perez, R. de J. Romero-Troncoso, E. Cabal-Yepez, and R. A. Osornio-Rios, “The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 2002–2010, May 2011. https://doi.org/10.1109/TIE.2010.2051398
A. Garcia-Perez, R. J. Romero-Troncoso, E. Cabal-Yepez, R. A. Osornio-Rios, J. de J. Rangel-Magdaleno, and H. Miranda, “Startup Current Analysis of Incipient Broken Rotor Bar in Induction Motors Using High-Resolution Spectral Analysis,” in 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, 2011, pp. 657–663. https://doi.org/10.1109/DEMPED.2011.6063694
E. H. El Bouchikhi, V. Choqueuse, M. Benbouzid, and J. F. Charpentier, “Induction Machine Fault Detection Enhancement Using a Stator Current High Resolution Spectrum,” in 38th Annual Conference on IEEE Industrial Electronics Society (IECON 2012), 2012, pp. 3913–3918. https://doi.org/10.1109/IECON.2012.6389267
B. Mirafzal and N. A. O. Demerdash, “Effects of Load Magnitude on Diagnosing Broken Bar Faults in Induction Motors Using the Pendulous Oscillation of the Rotor Magnetic Field Orientation,” IEEE Trans. Ind. Appl., vol. 41, no. 3, pp. 771–783, 2005. https://doi.org/10.1109/TIA.2005.847315
S. H. Kia, H. Henao, and G.-A. Capolino, “Diagnosis of Broken-Bar Fault in Induction Machines Using Discrete Wavelet Transform Without Slip Estimation,” IEEE Trans. Ind. Appl., vol. 45, no. 4, pp. 1395–1404, Jul. 2009. https://doi.org/10.1109/TIA.2009.2018975
A. Elez, S. Car, S. Tvoric, and B. Vaseghi, “Rotor Cage and Winding Fault Detection Based on Machine Differential Magnetic Field Measurement (DMFM),” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 3156–3163, May 2017. https://doi.org/10.1109/TIA.2016.2636800
R. A. Lizarraga-Morales, C. Rodriguez-Donate, E. Cabal-Yepez, M. Lopez-Ramirez, L. M. Ledesma-Carrillo, and E. R. Ferrucho-Alvarez, “Novel FPGA-Based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation, ” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1760–1769, Jul. 2017. https://doi.org/10.1109/TIM.2017.2664520
P. Karvelis, G. Georgoulas, I. P. Tsoumas, J. A. Antonino-Daviu, V. Climente-Alarcon, and C. D. Stylios, “A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors,” IEEE Trans. Ind. Informatics, vol. 11, no. 5, pp. 1028–1037, Oct. 2015. https://doi.org/10.1109/TII.2015.2463680
P. Shi, Z. Chen, Y. Vagapov, and Z. Zouaoui, “A New Diagnosis of Broken Rotor Bar Fault Extent in Three Phase Squirrel Cage Induction Motor,” Mech. Syst. Signal Process., vol. 42, no. 1–2, pp. 388–403, Jan. 2014. https://doi.org/10.1016/j.ymssp.2013.09.002
K. Yahia, A. J. Marques Cardoso, A. Ghoggal, and S.-E. Zouzou, “Induction Motors Broken Rotor Bars Diagnosis Through the Discrete Wavelet Transform of the Instantaneous Reactive Power Signal under Time-Varying Load Conditions,” Electr. Power Components Syst., vol. 42, no. 7, pp. 682–692, May 2014. https://doi.org/10.1080/15325008.2014.890966
N. R. Devi, D. V. S. S. Siva Sarma, and P. V. Ramana Rao, “Diagnosis and Classification of Stator Winding Insulation Faults on a Three-Phase Induction Motor Using Wavelet and MNN,” IEEE Trans. Dielectr. Electr. Insul., vol. 23, no. 5, pp. 2543–2555, Oct. 2016. https://doi.org/10.1109/TDEI.2016.7736811
T. Hong, M. T. C. Fang, and D. Hilder, “PD Classification by a Modular Neural Network Based on Task Decomposition,” IEEE Trans. Dielectr. Electr. Insul., vol. 3, no. 2, pp. 207–212, Apr. 1996. https://doi.org/10.1109/94.486772
M. Kang and J.-M. Kim, “Reliable Fault Diagnosis of Multiple Induction Motor Defects Using a 2-D Representation of Shannon Wavelets,” IEEE Trans. Magn., vol. 50, no. 10, pp. 1–13, Oct. 2014. https://doi.org/10.1109/TMAG.2014.2316474
J. Zarei, “Induction Motors Bearing Fault Detection Using Pattern Recognition Techniques,” Expert Syst. Appl., vol. 39, no. 1, pp. 68–73, Jan. 2012. https://doi.org/10.1016/j.eswa.2011.06.042
C. Rodriguez-Donate, R. Romero-Troncoso, E. Cabal-Yepez, A. Garcia-Perez, and R. Osornio-Rios, “Wavelet-Based General Methodology for Multiple Fault Detection on Induction Motors at the Startup Vibration Transient,” J. Vib. Control, vol. 17, no. 9, pp. 1299–1309, Aug. 2011. https://doi.org/10.1177/1077546310379141
Y. Lei, Z. He, and Y. Zi, “Application of an Intelligent Classification Method to Mechanical Fault Diagnosis,” Expert Syst. Appl., vol. 36, no. 6, pp. 9941–9948, Aug. 2009. https://doi.org/10.1016/j.eswa.2009.01.065
V. T. Do and U.-P. Chong, “Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain,” Strojniški Vestn. – J. Mech. Eng., vol. 57, no. 09, pp. 655–666, Sep. 2011. https://doi.org/10.5545/sv-jme.2010.162
P. E. William and M. W. Hoffman, “Identification of Bearing Faults Using Time Domain Zero-Crossings,” Mech. Syst. Signal Process., vol. 25, no. 8, pp. 3078–3088, Nov. 2011. https://doi.org/10.1016/j.ymssp.2011.06.001
Kyusung Kim and A. G. Parlos, “Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing,” IEEE/ASME Trans. Mechatronics, vol. 7, no. 2, pp. 201–219, Jun. 2002. https://doi.org/10.1109/TMECH.2002.1011258
A. Sapena-Bano, M. Pineda-Sanchez, R. Puche-Panadero, J. Martinez-Roman, and D. Matic, “Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current’s FFT,” IEEE Trans. Instrum. Meas., vol. 64, no. 11, pp. 3137–3146, Nov. 2015. https://doi.org/10.1109/TIM.2015.2444240
A. Sadeghian, Zhongming Ye, and Bin Wu, “Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks,” IEEE Trans. Instrum. Meas., vol. 58, no. 7, pp. 2253–2263, Jul. 2009. https://doi.org/10.1109/TIM.2009.2013743
B. Ayhan, M.-Y. Chow, and M.-H. Song, “Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors, ” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1298–1308, Jun. 2006. https://doi.org/10.1109/TIE.2006.878301
V. P. Mini, S. Setty, and S. Ushakumari, “Fault Detection and Diagnosis of an Induction Motor Using Fuzzy Logic,” in 2010 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON), 2010, pp. 459–464. https://doi.org/10.1109/SIBIRCON.2010.5555123
J. F. Bangura, R. J. Povinelli, N. A. O. Demerdash, and R. H. Brown, “Diagnostics of Eccentricities and Bar/End-Ring Connector Breakages in Polyphase Induction Motors Through a Combination Of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Techniques,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1005–1013, Jul. 2003. https://doi.org/10.1109/TIA.2003.814582
S. Abdellatif, S. Tahar, and Z. Boubakeur, “Diagnostic of the simultaneous of Dynamic Eccentricity and Broken Rotor Bars Using the Magnetic Field Spectrum of the Air-Gap for an Induction Machine,” in 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), 2015, pp. 1–6. https://doi.org/10.1109/CEIT.2015.7233158
J. Subramanian, S. Nandi, and T. Ilamparithi, “Detection and Severity Estimation of Static and Dynamic Eccentricity in Induction Motors Using Finite Element Analysis,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 366–372. https://doi.org/10.1109/DEMPED.2015.7303716
A. Bentounsi and A. Nicolas, “On Line Diagnosis of Defaults on Squirrel Cage Motors Using FEM,” IEEE Trans. Magn., vol. 34, no. 5, pp. 3511–3514, 1998. https://doi.org/10.1109/20.717828
T. Vaimann, A. Belahcen, and A. Kallaste, “Necessity for Implementation of Inverse Problem Theory in Electric Machine Fault Diagnosis,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 380–385. https://doi.org/10.1109/DEMPED.2015.7303718
J. F. Watson and D. G. Dorrell, “The Use of Finite Element Methods to Improve Techniques for the Early Detection of Faults in 3-Phase Induction Motors,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 655–660, 1999. https://doi.org/10.1109/60.790931
L. Weili, X. Ying, S. Jiafeng, and L. Yingli, “Finite-Element Analysis of Field Distribution and Characteristic Performance of Squirrel-Cage Induction Motor With Broken Bars,” IEEE Trans. Magn., vol. 43, no. 4, pp. 1537–1540, Apr. 2007. https://doi.org/10.1109/TMAG.2006.892086
O. A. Mohammed, N. Y. Abed, and S. Ganu, “Modeling and Characterization of Induction Motor Internal Faults Using Finite-Element and Discrete Wavelet Transforms,” IEEE Trans. Magn., vol. 42, no. 10, pp. 3434–3436, Oct. 2006. https://doi.org/10.1109/TMAG.2006.879091
T. Vaimann, J. Sobra, A. Belahcen, A. Rassõlkin, M. Rolak, and A. Kallaste, “Induction Machine Fault Detection Using Smartphone Recorded Audible Noise,” IET Sci. Meas. Technol., 2018.
M. Seera, Chee Peng Lim, D. Ishak, and H. Singh, “Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model,” IEEE Trans. Neural Networks Learn. Syst., vol. 23, no. 1, pp. 97–108, Jan. 2012. https://doi.org/10.1109/TNNLS.2011.2178443
M. Mneimneh and R. Povinelli, “An Electrophysiological Cardiac Model With Applications to Ischemia Detection and Infarction Localization,” in 2009 36th Annual Computers in Cardiology Conference (CinC), 2009.
J. Wang, Z. Zhao, Z. Nie, and Q.-H. Liu, “Electromagnetic Inverse Scattering Series Method for Positioning Three-Dimensional Targets in Near-Surface Two-Layer Medium With Unknown Dielectric Properties,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 299–303, Feb. 2015. https://doi.org/10.1109/LGRS.2014.2336983
C. Gilavert, S. Moussaoui, and J. Idier, “Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems Using MCMC,” IEEE Trans. Signal Process., vol. 63, no. 1, pp. 70–80, Jan. 2015. https://doi.org/10.1109/TSP.2014.2367457
A. Mohamed Abouelyazied Abdallh, “An Inverse Problem Based Methodology With Uncertainty Analysis for the Identification of Magnetic Material Characteristics of Electromagnetic Devices,” Dissertation, Ghent University, Department of Electrical energy, systems and automation, Ghent; Leuven, Belgium, 2012.
G. Crevecoeur, “Numerical Methods for Low Frequency Electromagnetic Optimization and Inverse Problems Using Multi-Level Techniques,” Ph. D. Dissertation, Ghent University, Ghent, Belgium, 2009.
A. Abou-Elyazied Abdallh, P. Sergeant, and L. Dupre, “A Non-Destructive Methodology for Estimating the Magnetic Material Properties of an Asynchronous Motor,” IEEE Trans. Magn., vol. 48, no. 4, pp. 1621–1624, Apr. 2012. https://doi.org/10.1109/TMAG.2011.2173171
A. A.-E. Abdallh, P. Sergeant, G. Crevecoeur, and L. Dupre, “An Inverse Approach for Magnetic Material Characterization of an EI Core Electromagnetic Inductor,” IEEE Trans. Magn., vol. 46, no. 2, pp. 622–625, Feb. 2010. https://doi.org/10.1109/TMAG.2009.2033353
A. A.-E. Abdallh, G. Crevecoeur, and L. Dupre, “Selection of Measurement Modality for Magnetic Material Characterization of an Electromagnetic Device Using Stochastic Uncertainty Analysis,” IEEE Trans. Magn., vol. 47, no. 11, pp. 4564–4573, Nov. 2011. https://doi.org/10.1109/TMAG.2011.2151870
V. P. Bui, O. Chadebec, L.-L. Rouve, and J.-L. Coulomb, “Noninvasive Fault Monitoring of Electrical Machines by Solving the Steady-State Magnetic Inverse Problem,” IEEE Trans. Magn., vol. 44, no. 6, pp. 1050–1053, Jun. 2008. https://doi.org/10.1109/TMAG.2007.916593
P. Rasilo, A. A.-E. Abdallh, A. Belahcen, A. Arkkio, and L. Dupre, “Identification of Synchronous Machine Magnetization Characteristics From Calorimetric Core-Loss and No-Load Curve Measurements,” IEEE Trans. Magn., vol. 51, no. 3, pp. 1–4, Mar. 2015. https://doi.org/10.1109/TMAG.2014.2354055
A. Kechroud, J. J. H. Paulides, and E. A. Lomonova, “B-Spline Neural Network Approach to Inverse Problems in Switched Reluctance Motor Optimal Design,” IEEE Trans. Magn., vol. 47, no. 10, pp. 4179–4182, Oct. 2011. https://doi.org/10.1109/TMAG.2011.2151183
J. Fouladgar and E. Chauveau, “The Influence of the Harmonics on the Temperature of Electrical Machines,” IEEE Trans. Magn., vol. 41, no. 5, pp. 1644–1647, May 2005. https://doi.org/10.1109/TMAG.2005.846113
M. Saif and W. Chen, “Observer-Based Strategies for Actuator Fault Detection, Isolation and Estimation for Certain Class of Uncertain Nonlinear Systems,” IET Control Theory Appl., vol. 1, no. 6, pp. 1672–1680, Nov. 2007. https://doi.org/10.1049/iet-cta:20060408
Q. Shen, B. Jiang, and V. Cocquempot, “Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults,” IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 652–665, Aug. 2012. https://doi.org/10.1109/TFUZZ.2011.2181181
L. M. Capisani, A. Ferrara, A. Ferreira de Loza, and L. M. Fridman, “Manipulator Fault Diagnosis via Higher Order Sliding-Mode Observers,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3979–3986, Oct. 2012. https://doi.org/10.1109/TIE.2012.2189534
M. N. Nguyen, C. Bao, K. L. Tew, S. D. Teddy, and X.-L. Li, “Ensemble Based Real-Time Adaptive Classification System for Intelligent Sensing Machine Diagnostics,” IEEE Trans. Reliab., vol. 61, no. 2, pp. 303–313, Jun. 2012. https://doi.org/10.1109/TR.2012.2194352
D. He, R. Li, and J. Zhu, “Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach,” IEEE Trans. Ind. Electron., pp. 1–1, 2012. https://doi.org/10.1109/TIE.2012.2192894
M. N. Uddin, W. Wang, and Z. R. Huang, “Modeling and Minimization of Speed Ripple of a Faulty Induction Motor With Broken Rotor Bars,” IEEE Trans. Ind. Appl., vol. 46, no. 6, pp. 2243–2250, Nov. 2010. https://doi.org/10.1109/TIA.2010.2070476
W. W. Tan and H. Huo, “A Generic Neurofuzzy Model-Based Approach for Detecting Faults in Induction Motors,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1420–1427, Oct. 2005. https://doi.org/10.1109/TIE.2005.855654
M. Hermann, T. Pentek, and B. Otto, “Design Principles for Industrie 4.0 Scenarios,” in 2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928–3937. https://doi.org/10.1109/hicss.2016.488
S. Nandi, H. A. Toliyat, and X. Li, “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 719–729, Dec. 2005. https://doi.org/10.1109/tec.2005.847955
B. Asad, T. Vaimann, A. Belahcen, and A. Kallaste, “Broken Rotor Bar Fault Diagnostic of Inverter Fed Induction Motor Using FFT, Hilbert and Park’s Vector Approach,” in 2018 XIII International Conference on Electrical Machines (ICEM), 2018, pp. 2352–2358.
M. Karimi-Ghartemani, S. A. Khajehoddin, P. K. Jain, A. Bakhshai, and M. Mojiri, “Addressing DC Component in PLL and Notch Filter Algorithms,” IEEE Trans. Power Electron., vol. 27, no. 1, pp. 78–86, Jan. 2012. https://doi.org/10.1109/tpel.2011.2158238
Z. Xin, X. Wang, Z. Qin, M. Lu, P. C. Loh, and F. Blaabjerg, “An Improved Second-Order Generalized Integrator Based Quadrature Signal Generator,” IEEE Trans. Power Electron., vol. 31, no. 12, pp. 8068–8073, Dec. 2016. https://doi.org/10.1109/tpel.2016.2576644
M. Malekpour, B. T. Phung, and E. Ambikairajah, “Stator Current Envelope Extraction for Analysis of Broken Rotor Bar in Induction Motors,” in 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2017, pp. 240–246. https://doi.org/10.1109/demped.2017.8062393
B. Mirafzal and N. A. O. Demerdash, “Induction Machine Broken-Bar Fault Diagnosis Using the Rotor Magnetic Field Space-Vector Orientation,” IEEE Trans. Ind. Appl., vol. 40, no. 2, pp. 534–542, Mar. 2004. https://doi.org/10.1109/tia.2004.824433
R. Roy, A. Paulraj, and T. Kailath, “ESPRIT-A Subspace Rotation Approach to Estimation of Parameters of Cisoids in Noise,” IEEE Trans. Acoust., vol. 34, no. 5, pp. 1340–1342, Oct. 1986. https://doi.org/10.1109/tassp.1986.1164935
R. Roy and T. Kailath, “ESPRIT-Estimation of Signal Parameters via Rotational Invariance Techniques,” IEEE Trans. Acoust., vol. 37, no. 7, pp. 984–995, Jul. 1989. https://doi.org/10.1109/29.32276
B. Ottersten, M. Viberg, and T. Kailath, “Performance Analysis of the Total Least Squares ESPRIT Algorithm,” IEEE Trans. Signal Process., vol. 39, no. 5, pp. 1122–1135, May 1991. https://doi.org/10.1109/78.80967
X.-D. Zhang and Y.-C. Liang, “Prefiltering-Based ESPRIT for Estimating Sinusoidal Parameters in Non-Gaussian ARMA Noise,” IEEE Trans. Signal Process., vol. 43, no. 1, pp. 349–353, 1995. https://doi.org/10.1109/78.365327
V. F. Pisarenko, “The Retrieval of Harmonics from a Covariance Function,” Geophys. J. Int., vol. 33, no. 3, pp. 347–366, Sep. 1973. https://doi.org/10.1111/j.1365-246X.1973.tb03424.x
R. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation,” IEEE Trans. Antennas Propag., vol. 34, no. 3, pp. 276–280, Mar. 1986. https://doi.org/10.1109/TAP.1986.1143830
B. Xu, L. Sun, L. Xu, and G. Xu, “Improvement of the Hilbert Method via ESPRIT for Detecting Rotor Fault in Induction Motors at Low Slip,” IEEE Trans. Energy Convers., vol. 28, no. 1, pp. 225–233, Mar. 2013. https://doi.org/10.1109/TEC.2012.2236557
R. Puche-Panadero, M. Pineda-Sanchez, M. Riera-Guasp, J. Roger-Folch, E. Hurtado-Perez, and J. Perez-Cruz, “Improved Resolution of the MCSA Method via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip,” IEEE Trans. Energy Convers., vol. 24, no. 1, pp. 52–59, Mar. 2009. https://doi.org/10.1109/TEC.2008.2003207
E. Elbouchikhi, V. Choqueuse, and M. Benbouzid, “Induction Machine Bearing Faults Detection Based on a Multi-Dimensional MUSIC Algorithm and Maximum Likelihood Estimation,” ISA Trans., vol. 63, pp. 413–424, 2016. https://doi.org/10.1016/j.isatra.2016.03.007
S. Pan, T. Han, A. C. C. Tan, and T. R. Lin, “Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm,” Shock Vib., vol. 2016, no. January, 2016. https://doi.org/10.1155/2016/5836717
T. A. Garcia-Calva, D. Morinigo-Sotelo, and R. De Jesus Romero-Troncoso, “Non-Uniform Time Resampling for Diagnosing Broken Rotor Bars in Inverter-Fed Induction Motors,” IEEE Trans. Ind. Electron., vol. 64, no. 3, pp. 2306–2315, 2017. https://doi.org/10.1109/TIE.2016.2619318
Y. Trachi, E. Elbouchikhi, V. Choqueuse, and M. E. H. Benbouzid, “Induction Machines Fault Detection Based on Subspace Spectral Estimation,” IEEE Trans. Ind. Electron., vol. 63, no. 9, pp. 5641–5651, Sep. 2016. https://doi.org/10.1109/TIE.2016.2570741
A. Garcia-Perez, R. de J. Romero-Troncoso, E. Cabal-Yepez, and R. A. Osornio-Rios, “The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 2002–2010, May 2011. https://doi.org/10.1109/TIE.2010.2051398
A. Garcia-Perez, R. J. Romero-Troncoso, E. Cabal-Yepez, R. A. Osornio-Rios, J. de J. Rangel-Magdaleno, and H. Miranda, “Startup Current Analysis of Incipient Broken Rotor Bar in Induction Motors Using High-Resolution Spectral Analysis,” in 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, 2011, pp. 657–663. https://doi.org/10.1109/DEMPED.2011.6063694
E. H. El Bouchikhi, V. Choqueuse, M. Benbouzid, and J. F. Charpentier, “Induction Machine Fault Detection Enhancement Using a Stator Current High Resolution Spectrum,” in 38th Annual Conference on IEEE Industrial Electronics Society (IECON 2012), 2012, pp. 3913–3918. https://doi.org/10.1109/IECON.2012.6389267
B. Mirafzal and N. A. O. Demerdash, “Effects of Load Magnitude on Diagnosing Broken Bar Faults in Induction Motors Using the Pendulous Oscillation of the Rotor Magnetic Field Orientation,” IEEE Trans. Ind. Appl., vol. 41, no. 3, pp. 771–783, 2005. https://doi.org/10.1109/TIA.2005.847315
S. H. Kia, H. Henao, and G.-A. Capolino, “Diagnosis of Broken-Bar Fault in Induction Machines Using Discrete Wavelet Transform Without Slip Estimation,” IEEE Trans. Ind. Appl., vol. 45, no. 4, pp. 1395–1404, Jul. 2009. https://doi.org/10.1109/TIA.2009.2018975
A. Elez, S. Car, S. Tvoric, and B. Vaseghi, “Rotor Cage and Winding Fault Detection Based on Machine Differential Magnetic Field Measurement (DMFM),” IEEE Trans. Ind. Appl., vol. 53, no. 3, pp. 3156–3163, May 2017. https://doi.org/10.1109/TIA.2016.2636800
R. A. Lizarraga-Morales, C. Rodriguez-Donate, E. Cabal-Yepez, M. Lopez-Ramirez, L. M. Ledesma-Carrillo, and E. R. Ferrucho-Alvarez, “Novel FPGA-Based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation, ” IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1760–1769, Jul. 2017. https://doi.org/10.1109/TIM.2017.2664520
P. Karvelis, G. Georgoulas, I. P. Tsoumas, J. A. Antonino-Daviu, V. Climente-Alarcon, and C. D. Stylios, “A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors,” IEEE Trans. Ind. Informatics, vol. 11, no. 5, pp. 1028–1037, Oct. 2015. https://doi.org/10.1109/TII.2015.2463680
P. Shi, Z. Chen, Y. Vagapov, and Z. Zouaoui, “A New Diagnosis of Broken Rotor Bar Fault Extent in Three Phase Squirrel Cage Induction Motor,” Mech. Syst. Signal Process., vol. 42, no. 1–2, pp. 388–403, Jan. 2014. https://doi.org/10.1016/j.ymssp.2013.09.002
K. Yahia, A. J. Marques Cardoso, A. Ghoggal, and S.-E. Zouzou, “Induction Motors Broken Rotor Bars Diagnosis Through the Discrete Wavelet Transform of the Instantaneous Reactive Power Signal under Time-Varying Load Conditions,” Electr. Power Components Syst., vol. 42, no. 7, pp. 682–692, May 2014. https://doi.org/10.1080/15325008.2014.890966
N. R. Devi, D. V. S. S. Siva Sarma, and P. V. Ramana Rao, “Diagnosis and Classification of Stator Winding Insulation Faults on a Three-Phase Induction Motor Using Wavelet and MNN,” IEEE Trans. Dielectr. Electr. Insul., vol. 23, no. 5, pp. 2543–2555, Oct. 2016. https://doi.org/10.1109/TDEI.2016.7736811
T. Hong, M. T. C. Fang, and D. Hilder, “PD Classification by a Modular Neural Network Based on Task Decomposition,” IEEE Trans. Dielectr. Electr. Insul., vol. 3, no. 2, pp. 207–212, Apr. 1996. https://doi.org/10.1109/94.486772
M. Kang and J.-M. Kim, “Reliable Fault Diagnosis of Multiple Induction Motor Defects Using a 2-D Representation of Shannon Wavelets,” IEEE Trans. Magn., vol. 50, no. 10, pp. 1–13, Oct. 2014. https://doi.org/10.1109/TMAG.2014.2316474
J. Zarei, “Induction Motors Bearing Fault Detection Using Pattern Recognition Techniques,” Expert Syst. Appl., vol. 39, no. 1, pp. 68–73, Jan. 2012. https://doi.org/10.1016/j.eswa.2011.06.042
C. Rodriguez-Donate, R. Romero-Troncoso, E. Cabal-Yepez, A. Garcia-Perez, and R. Osornio-Rios, “Wavelet-Based General Methodology for Multiple Fault Detection on Induction Motors at the Startup Vibration Transient,” J. Vib. Control, vol. 17, no. 9, pp. 1299–1309, Aug. 2011. https://doi.org/10.1177/1077546310379141
Y. Lei, Z. He, and Y. Zi, “Application of an Intelligent Classification Method to Mechanical Fault Diagnosis,” Expert Syst. Appl., vol. 36, no. 6, pp. 9941–9948, Aug. 2009. https://doi.org/10.1016/j.eswa.2009.01.065
V. T. Do and U.-P. Chong, “Signal Model-Based Fault Detection and Diagnosis for Induction Motors Using Features of Vibration Signal in Two-Dimension Domain,” Strojniški Vestn. – J. Mech. Eng., vol. 57, no. 09, pp. 655–666, Sep. 2011. https://doi.org/10.5545/sv-jme.2010.162
P. E. William and M. W. Hoffman, “Identification of Bearing Faults Using Time Domain Zero-Crossings,” Mech. Syst. Signal Process., vol. 25, no. 8, pp. 3078–3088, Nov. 2011. https://doi.org/10.1016/j.ymssp.2011.06.001
Kyusung Kim and A. G. Parlos, “Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing,” IEEE/ASME Trans. Mechatronics, vol. 7, no. 2, pp. 201–219, Jun. 2002. https://doi.org/10.1109/TMECH.2002.1011258
A. Sapena-Bano, M. Pineda-Sanchez, R. Puche-Panadero, J. Martinez-Roman, and D. Matic, “Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current’s FFT,” IEEE Trans. Instrum. Meas., vol. 64, no. 11, pp. 3137–3146, Nov. 2015. https://doi.org/10.1109/TIM.2015.2444240
A. Sadeghian, Zhongming Ye, and Bin Wu, “Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Artificial Neural Networks,” IEEE Trans. Instrum. Meas., vol. 58, no. 7, pp. 2253–2263, Jul. 2009. https://doi.org/10.1109/TIM.2009.2013743
B. Ayhan, M.-Y. Chow, and M.-H. Song, “Multiple Discriminant Analysis and Neural-Network-Based Monolith and Partition Fault-Detection Schemes for Broken Rotor Bar in Induction Motors, ” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1298–1308, Jun. 2006. https://doi.org/10.1109/TIE.2006.878301
V. P. Mini, S. Setty, and S. Ushakumari, “Fault Detection and Diagnosis of an Induction Motor Using Fuzzy Logic,” in 2010 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering (SIBIRCON), 2010, pp. 459–464. https://doi.org/10.1109/SIBIRCON.2010.5555123
J. F. Bangura, R. J. Povinelli, N. A. O. Demerdash, and R. H. Brown, “Diagnostics of Eccentricities and Bar/End-Ring Connector Breakages in Polyphase Induction Motors Through a Combination Of Time-Series Data Mining and Time-Stepping Coupled FE-State Space Techniques,” IEEE Trans. Ind. Appl., vol. 39, no. 4, pp. 1005–1013, Jul. 2003. https://doi.org/10.1109/TIA.2003.814582
S. Abdellatif, S. Tahar, and Z. Boubakeur, “Diagnostic of the simultaneous of Dynamic Eccentricity and Broken Rotor Bars Using the Magnetic Field Spectrum of the Air-Gap for an Induction Machine,” in 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), 2015, pp. 1–6. https://doi.org/10.1109/CEIT.2015.7233158
J. Subramanian, S. Nandi, and T. Ilamparithi, “Detection and Severity Estimation of Static and Dynamic Eccentricity in Induction Motors Using Finite Element Analysis,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 366–372. https://doi.org/10.1109/DEMPED.2015.7303716
A. Bentounsi and A. Nicolas, “On Line Diagnosis of Defaults on Squirrel Cage Motors Using FEM,” IEEE Trans. Magn., vol. 34, no. 5, pp. 3511–3514, 1998. https://doi.org/10.1109/20.717828
T. Vaimann, A. Belahcen, and A. Kallaste, “Necessity for Implementation of Inverse Problem Theory in Electric Machine Fault Diagnosis,” in 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 380–385. https://doi.org/10.1109/DEMPED.2015.7303718
J. F. Watson and D. G. Dorrell, “The Use of Finite Element Methods to Improve Techniques for the Early Detection of Faults in 3-Phase Induction Motors,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 655–660, 1999. https://doi.org/10.1109/60.790931
L. Weili, X. Ying, S. Jiafeng, and L. Yingli, “Finite-Element Analysis of Field Distribution and Characteristic Performance of Squirrel-Cage Induction Motor With Broken Bars,” IEEE Trans. Magn., vol. 43, no. 4, pp. 1537–1540, Apr. 2007. https://doi.org/10.1109/TMAG.2006.892086
O. A. Mohammed, N. Y. Abed, and S. Ganu, “Modeling and Characterization of Induction Motor Internal Faults Using Finite-Element and Discrete Wavelet Transforms,” IEEE Trans. Magn., vol. 42, no. 10, pp. 3434–3436, Oct. 2006. https://doi.org/10.1109/TMAG.2006.879091
T. Vaimann, J. Sobra, A. Belahcen, A. Rassõlkin, M. Rolak, and A. Kallaste, “Induction Machine Fault Detection Using Smartphone Recorded Audible Noise,” IET Sci. Meas. Technol., 2018.
M. Seera, Chee Peng Lim, D. Ishak, and H. Singh, “Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model,” IEEE Trans. Neural Networks Learn. Syst., vol. 23, no. 1, pp. 97–108, Jan. 2012. https://doi.org/10.1109/TNNLS.2011.2178443
M. Mneimneh and R. Povinelli, “An Electrophysiological Cardiac Model With Applications to Ischemia Detection and Infarction Localization,” in 2009 36th Annual Computers in Cardiology Conference (CinC), 2009.
J. Wang, Z. Zhao, Z. Nie, and Q.-H. Liu, “Electromagnetic Inverse Scattering Series Method for Positioning Three-Dimensional Targets in Near-Surface Two-Layer Medium With Unknown Dielectric Properties,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 2, pp. 299–303, Feb. 2015. https://doi.org/10.1109/LGRS.2014.2336983
C. Gilavert, S. Moussaoui, and J. Idier, “Efficient Gaussian Sampling for Solving Large-Scale Inverse Problems Using MCMC,” IEEE Trans. Signal Process., vol. 63, no. 1, pp. 70–80, Jan. 2015. https://doi.org/10.1109/TSP.2014.2367457
A. Mohamed Abouelyazied Abdallh, “An Inverse Problem Based Methodology With Uncertainty Analysis for the Identification of Magnetic Material Characteristics of Electromagnetic Devices,” Dissertation, Ghent University, Department of Electrical energy, systems and automation, Ghent; Leuven, Belgium, 2012.
G. Crevecoeur, “Numerical Methods for Low Frequency Electromagnetic Optimization and Inverse Problems Using Multi-Level Techniques,” Ph. D. Dissertation, Ghent University, Ghent, Belgium, 2009.
A. Abou-Elyazied Abdallh, P. Sergeant, and L. Dupre, “A Non-Destructive Methodology for Estimating the Magnetic Material Properties of an Asynchronous Motor,” IEEE Trans. Magn., vol. 48, no. 4, pp. 1621–1624, Apr. 2012. https://doi.org/10.1109/TMAG.2011.2173171
A. A.-E. Abdallh, P. Sergeant, G. Crevecoeur, and L. Dupre, “An Inverse Approach for Magnetic Material Characterization of an EI Core Electromagnetic Inductor,” IEEE Trans. Magn., vol. 46, no. 2, pp. 622–625, Feb. 2010. https://doi.org/10.1109/TMAG.2009.2033353
A. A.-E. Abdallh, G. Crevecoeur, and L. Dupre, “Selection of Measurement Modality for Magnetic Material Characterization of an Electromagnetic Device Using Stochastic Uncertainty Analysis,” IEEE Trans. Magn., vol. 47, no. 11, pp. 4564–4573, Nov. 2011. https://doi.org/10.1109/TMAG.2011.2151870
V. P. Bui, O. Chadebec, L.-L. Rouve, and J.-L. Coulomb, “Noninvasive Fault Monitoring of Electrical Machines by Solving the Steady-State Magnetic Inverse Problem,” IEEE Trans. Magn., vol. 44, no. 6, pp. 1050–1053, Jun. 2008. https://doi.org/10.1109/TMAG.2007.916593
P. Rasilo, A. A.-E. Abdallh, A. Belahcen, A. Arkkio, and L. Dupre, “Identification of Synchronous Machine Magnetization Characteristics From Calorimetric Core-Loss and No-Load Curve Measurements,” IEEE Trans. Magn., vol. 51, no. 3, pp. 1–4, Mar. 2015. https://doi.org/10.1109/TMAG.2014.2354055
A. Kechroud, J. J. H. Paulides, and E. A. Lomonova, “B-Spline Neural Network Approach to Inverse Problems in Switched Reluctance Motor Optimal Design,” IEEE Trans. Magn., vol. 47, no. 10, pp. 4179–4182, Oct. 2011. https://doi.org/10.1109/TMAG.2011.2151183
J. Fouladgar and E. Chauveau, “The Influence of the Harmonics on the Temperature of Electrical Machines,” IEEE Trans. Magn., vol. 41, no. 5, pp. 1644–1647, May 2005. https://doi.org/10.1109/TMAG.2005.846113
M. Saif and W. Chen, “Observer-Based Strategies for Actuator Fault Detection, Isolation and Estimation for Certain Class of Uncertain Nonlinear Systems,” IET Control Theory Appl., vol. 1, no. 6, pp. 1672–1680, Nov. 2007. https://doi.org/10.1049/iet-cta:20060408
Q. Shen, B. Jiang, and V. Cocquempot, “Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults,” IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 652–665, Aug. 2012. https://doi.org/10.1109/TFUZZ.2011.2181181
L. M. Capisani, A. Ferrara, A. Ferreira de Loza, and L. M. Fridman, “Manipulator Fault Diagnosis via Higher Order Sliding-Mode Observers,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3979–3986, Oct. 2012. https://doi.org/10.1109/TIE.2012.2189534
M. N. Nguyen, C. Bao, K. L. Tew, S. D. Teddy, and X.-L. Li, “Ensemble Based Real-Time Adaptive Classification System for Intelligent Sensing Machine Diagnostics,” IEEE Trans. Reliab., vol. 61, no. 2, pp. 303–313, Jun. 2012. https://doi.org/10.1109/TR.2012.2194352
D. He, R. Li, and J. Zhu, “Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach,” IEEE Trans. Ind. Electron., pp. 1–1, 2012. https://doi.org/10.1109/TIE.2012.2192894
M. N. Uddin, W. Wang, and Z. R. Huang, “Modeling and Minimization of Speed Ripple of a Faulty Induction Motor With Broken Rotor Bars,” IEEE Trans. Ind. Appl., vol. 46, no. 6, pp. 2243–2250, Nov. 2010. https://doi.org/10.1109/TIA.2010.2070476
W. W. Tan and H. Huo, “A Generic Neurofuzzy Model-Based Approach for Detecting Faults in Induction Motors,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1420–1427, Oct. 2005. https://doi.org/10.1109/TIE.2005.855654
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Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A., & Belahcen, A. (2018). Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0. Electrical, Control and Communication Engineering, 14(2), 108-116. https://doi.org/10.2478/ecce-2018-0013
