Mathematical Modelling of Three Phase Squirrel Cage Induction Motor and Related Signal Processing for Fault Diagnostics

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Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2021-08-27
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
103 + app. 153
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 87/2021
Abstract
This thesis aims to study different analytical methods to model a squirrel cage inductionmotor, which should have minimal simulation time than the corresponding finiteelement method (FEM) based models. The purpose of doing so is to develop a modelsuitable to simulate all major faults and be used for advanced model-dependent faultdiagnostic algorithms, such as parameters estimation and inverse problem theory. Thisthesis’s second key objective is to study various signal-processing techniques for theirpros and cons to detect fault at the embryonic stage and investigate the entire currentharmonic spectrum of induction motors both in transient and steady-state regions. Thus,the motor under healthy and broken rotor bar (BRB) conditions are simulated, andexperimental measurements are investigated for validation. The dynamic d-q model with the inclusion of non-linear magnetization inductance wasconsidered as a starting point. This model helps understand the machine's basic conceptsbecause of its comprehensiveness and ability to produce compact equations, which canbe used for drives as general and in observers and state estimators as particular.However, this model was found to be less suitable to simulate machine faults because ofthe considered approximations. To address the d-q model limitations, the winding function analysis (WFA) basedmodel was prepared. In this model, the analytical equations to calculate variousinductances, resistances, currents, fluxes, torque, and speed are derived for the motorunder investigation. These equations were simulated in MATLAB, giving results near tothe practical measurements. The model is suitable for implementing some faults, suchas BRB and broken end rings. Still, the consideration of constant air gap makes it lessideal for the implementation of eccentricity and saturation-related faults. Moreover, thespatial harmonics, which are very important for fault diagnostics and sensor-less speedestimation, cannot be simulated. Those approximations can be reduced with Fouriersummation of higher-order harmonics (winding) and Taylor series to include inverse airgap functions but at the cost of the self-defined number and amplitude of harmonics.To get more realistic results, the modified winding function analysis (MWFA) basedmodel was prepared to ensure that all winding functions and air gap were defined as afunction of stator and rotor individual and respective angles. The geometry of stator androtor slots is considered to calculate the leakage inductances and various resistances.The self and mutual inductances between rotor and stator are computed with a steppingrotor. The results at each rotor position are saved in offline 3D lookup tables. During theonline simulation, all pre-saved matrices are used as a rotor position function using theirindex value, and the performance parameters, such as currents, fluxes, torque, andspeed, are calculated. The FEM and hybrid FEM-analytical models of the machineunder investigation are prepared using commercial software to validate the results.The comparison of results shows an excellent agreement with a minimal simulation timeand least ill-posedness for the proposed model compared to the corresponding FEMmodel. Both analytical and hybrid FEM-analytical models are divided into online, offlineportions and compatible for the solution on cluster computation. Their division in the online and offline portions reduces the complexity and gives the model the freedom to simulate faults in the online portion without doing unnecessary offline calculations again.Moreover, the compatibility with cluster computation is excellent for exploitingdistributed computational resources such as cloud computation, an integral part ofindustry 4.0 standards. Towards the signal processing side, the fast Fourier transform (FFT) and wavelettransform (WT) are used extensively to study the steady-state and transient regimesignals. The infinite impulse response (IIR) based digital filters are used to improve themotor’s current spectrum’s legibility. In this way, the total harmonics are segregatedaccording to their cause of production. Moreover, the spectrum of current simulatedfrom the proposed model is compared with that simulated using the FEM model and thetest rig measurements. The comparison is made until a wide bandwidth of frequenciesfor further validation of the proposed model. Moreover, the WFA based model is also investigated during the transient regime bydoing the time-frequency analysis of the stator current. The recovered non-stationarysignal’s pattern is in good agreement with the one obtained from the practicalmeasurements. The specific fault-related pattern during the transient interval canfurther enhance the model’s effectiveness.
Description
Defence is held on 27.8.2021 14:00 – 17:00 https://zoom.us/j/95915675538?pwd=MVE3SXBkUVhEV3Rxa0ZFT3JjN0tvZz09 The full text document with articles can be downloaded here: https://digikogu.taltech.ee/en/Item/8b5869c8-de30-4380-a0c6-8a6a2d6b6ff3
Supervising professor
Vaimann, Toomas, Dr., Tallinn University of Technology, Estonia; Belahcen, Anouar, Prof., Aalto University, Dept. Electrical Engineering and Automation, Finland; Kallaste, Ants, Prof., Tallinn University of Technology, Estonia
Thesis advisor
Kallaste, Ants, Prof., Tallinn University of Technology, Estonia
Keywords
electrical engineering, mechanical engineering
Other note
Parts
  • [Publication 1]: Asad, B.; Vaimann, T.; Belahcen, A.; Kallaste, A.; Rassõlkin, A.; Ghafarokhi, P. S.; Kudelina, K.; “Transient Modeling and Recovery of non-Stationary Fault Signature for Condition Monitoring of Induction Motors,” Appl. Sci. 2021, 11, 2806.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202104206204
    DOI: 10.3390/app11062806 View at publisher
  • [Publication 2]: Asad, B.; Vaimann, T.; Belahcen, A.; Kallaste, A.; Rassõlkin, A.; Iqbal, M. N. “The Cluster Computation-Based Hybrid FEM–Analytical Model of Induction Motor for Fault Diagnostics,” Appl. Sci. 2020, 10, 7572.
    DOI: 10.3390/app10217572 View at publisher
  • [Publication 3]: Asad, B.; Vaimann, T.; Belahcen, A.; Kallaste, A.; Rassõlkin, A.; Heidari, H.; “The Low Voltage Start-up Test of Induction Motor for the Detection of Broken Bars,” International conference on electrical machines (ICEM 2020), Gothenburg, Sweden, August 23-26. IEEE, 1−8.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202101251496
    DOI: 10.1109/ICEM49940.2020.9271018 View at publisher
  • [Publication 4]: B. Asad, T. Vaimann, A. Belahcen, A. Kallaste, A. Rassõlkin, M. Naveed Iqbal, “Modified Winding Function-based Model of Squirrel Cage Induction Motor for Fault Diagnostics,” IET Electric Power Applications, May 2020.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202010165839
    DOI: 10.1049/iet-epa.2019.1002 View at publisher
  • [Publication 5]: B. Asad, T. Vaimann, A. Belahcen, A. Kallaste, A. Rassõlkin, M. Naveed Iqbal, “Broken rotor bar fault detection of the grid and inverter fed induction motor by effective attenuation of the fundamental component,” IET Electric Power Applications, vol. 13, pp. 2005−2014, Dec. 2019.
    DOI: 10.1049/iet-epa.2019.0350 View at publisher
  • [Publication 6]: B. Asad, T. Vaimann, A. Kallaste, A. Rassõlkin, A. Belahcen, M. Naveed Iqbal, “Improving Legibility of Motor Current Spectrum for Broken Rotor Bars FaultDiagnostics,” Electrical, Control, and Communication Engineering, vol. 15 (1), pp. 1−8, Sep. 2019.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201911076171
    DOI: 10.2478/ecce-2019-0001 View at publisher
  • [Publication 7]: B. Asad, T. Vaimann, A. Kallaste, A. Rassõlkin, A. Belahcen, “A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor,” Electrical, Control and Communication Engineering, vol. 14 (2), pp. 117−124, Mar. 2019.
    DOI: 10.2478/ecce-2018-0014 View at publisher
  • [Publication 8]: B. Asad, T. Vaimann, A. Kallaste, A. Rassõlkin, A. Belahcen, “Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0,” Electrical, Control, and Communication Engineering, vol. 14 (2), pp. 108-116, Mar. 2019.
    DOI: 10.2478/ecce-2018-0013 View at publisher
  • [Publication 9]: B. Asad, T. Vaimann, A. Belahcen, A. Kallaste, A. Rassõlkin, “Rotor Fault Diagnostic of Inverter Fed Induction Motor Using Frequency Analysis,” 12th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED) Toulouse, France, 2019, pp. 127−133.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002122206
    DOI: 10.1109/DEMPED.2019.8864903 View at publisher
  • [Publication 10]: B. Asad, T. Vaimann, A. Belahcen, A. Kallaste, A. Rassõlkin, “Winding Function-Based Analytical Model of Squirrel Cage Induction Motor for Fault Diagnostics,” 26th International Workshop on Electric Drives: Improvement in Efficiency of Electric Drives (IWED), Mar. 2019, pp. 1−6.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202002122192
    DOI: 10.1109/IWED.2019.8664314 View at publisher
  • [Publication 11]: B. Asad, T. Vaimann, A. Belahcen, A. Kallaste, “Broken Rotor Bar Fault Diagnostic of Inverter Fed Induction Motor Using FFT, Hilbert and Park’s Vector Approach,” IEEE XXIIIrd International Conference on Electrical Machines (ICEM'2018) Alexandroupoli - Greece September 3-6, 2018, pp. 2352−2358.
    DOI: 10.1109/ICELMACH.2018.8506957 View at publisher
  • [Publication 12]: B. Asad, T. Vaimann, A. Rassõlkin, A. Belahcen, “Dynamic State-Space Model-Based Analysis of a Three-Phase Induction Motor Using Nonlinear Magnetization Inductance,” IEEE 19th International Scientific Conference on Electric Power Engineering (EPE) Brno, Czech Republic, June 2018, pp. 260−265.
    DOI: 10.1109/EPE.2018.8396039 View at publisher
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