Induction motor windings faults detection using flux-error based MRAS estimators
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Wrocław University of Science and Technology
Submission date: 2019-02-12
Final revision date: 2019-04-29
Acceptance date: 2019-04-30
Online publication date: 2019-05-10
Publication date: 2019-05-10
Corresponding author
Szymon Antoni Bednarz   

Wrocław University of Science and Technology
Diagnostyka 2019;20(2):87-96
The paper is concerned with detection of a stator and rotor winding faults in a squirrel-cage induction motor. The idea of the fault detection is based on a hypothesis that each of windings faults results in a sharp increase or decrease of internal parameters’ values of the machine, therefore it can be treated as a suitable fault symptom. Resistances of the stator and rotor windings seem to be adequate quantities due to their direct relationship with the machine windings. An observation and analysis of the parameters’ changes in a real-time domain enables to an incipient detection of the fault. It is evident that internal parameters of the machine can’t be measured directly during operation on the drive system thus the only way is an estimation by specialized algorithms. In the paper two estimators based on Model Reference Adaptive System (MRAS) were utilized to achieve this goal. Two simple algorithms for faults detection are proposed as well. Detailed description of fault detection systems is included in the paper. Proposed systems were tested on computer simulations performed by MATLAB/Simulink software. Then, experimental tests were carried out on the laboratory setup to confirm usefulness of proposed approaches.
Bose BK. Power electronics and motor drives: advances and trends. Elsevier; 2006.
Campos-Delgado DU, Espinoza-Trejo DR, Palacios E. Fault-tolerant control in variable speed drives: a survey. IET Electric Power Applications. 2008; 2(2): 121–134.
Riera-Guasp M, Antonino-Daviu JA, Capolino GA. Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art. IEEE Transactions on Industrial Electronics. 2015; 62(3): 1746–1759.
Bellini A, Filippetti F, Tassoni C, Capolino GA. Advances in Diagnostic Techniques for Induction Machines. IEEE Transactions on Industrial Electronics. 2008; 55(12): 4109–4126.
Zhang P, Du Y, Habetler TG, Lu B. A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors. IEEE Transactions on Industry Applications. 2010; 47(1): 34–46.
Gandhi A, Corrigan T, Parsa L. Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors. IEEE Transactions on Industrial Electronics. 2010; 58(5): 1564–1575.
Siddique A, Yadava GS, Singh B. A review of stator fault monitoring techniques of induction motors. IEEE Transactions on Energy Conversion. 2005;20(1):106–14.
Ewert P. Application of neural networks and axial flus for the stator and rotor fault detection of induction motor. Power Electronics and Drives. 2018;1–13.
Bellini A, Filippetti F, Franceschini G, Tassoni C. Closed-loop control impact on the diagnosis of induction motors faults. IEEE Transactions on Industry Applications. 2000;36(5):1318–29.
Cheng S, Zhang P, Habetler TG. An impedance identification approach to sensitive detection and location of stator turn-to-turn faults in a closed-loop multiple-motor drive. IEEE Transactions on Industrial Electronics. 2011;58(5):1545–54.
Tallam RM, Habetler TG, Harley RG. Stator winding turn-fault detection for closed-loop induction motor drives. IEEE Transactions on Industry Applications. 2003; 39(3):1553–7.
Cruz SMA, Cardoso AJM. Diagnosis of Stator Inter-Turn Short Circuits in DTC Induction Motor Drives. IEEE Transactions on Industry Applications. 2004; 40(5):1349–60.
Cruz SMA, Cardoso AJM. Fault Indicators for the Diagnosis of Rotor Faults in FOC Induction Motor Drives. In: 2007 IEEE International Electric Machines & Drives Conference; 1136–41.
Wolkiewicz M, Tarchała G, Orłowska-Kowalska T, Kowalski CT. Online Stator Interturn Short Circuits Monitoring in the DFOC Induction-Motor Drive. IEEE Transactions on Industrial Electronics. 2016;63(4):2517–28.
Wolkiewicz M, Tarchała G, Kowalski T, Orłowska-Kowalska T. Advanced Control of Electrical Drives and Power Electronic Converters. 2017; 75:169-191.
Wolkiewicz M, Tarchała G, Orłowska-Kowalska T. Diagnosis of stator and rotor faults of an induction motor in closed-loop control structure. In: 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM); 2018. 196–201.
Iserman R. Fault-Diagnosis Applications, Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems. Springer-Verlag Berlin Heidelberg; 2011.
Gao Z, Cecati C, Ding SX. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics. 2015;62(6):3757–67.
Cho KR, Lang JH, Umans SD. Detection of broken rotor bars in induction motors using state and parameter estimation. IEEE Transactions on Industry Applications.1992;28(3):702–709.
Bachir S, Tnani S, Trigeassou JC, Champenois G. Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transactions on Industrial Electronics. 2006; 53(3):963–73.
Said MSN, Benbouzid MEH, Benchaib A. Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation. IEEE Transactions on Energy Conversion. 2000;15(1):66–70.
Bachir S, Trigeassou JC, Tnani S. On-Line Stator Faults Diagnosis By Parameter Estimation. In: European Conference on Power Electronics and Applications (EPE); 2003. p. 209–19.
Kowalski CT, Wierzbicki R, Wolkiewicz M. Stator and Rotor Faults Monitoring of the Inverter-Fed Induction Motor Drive using State Estimators. Automatika. 2013;54(3):348–55.
Bazine IBA, Tnani S, Poinot T, Champenois G, Jelassi K. On-line detection of stator and rotor faults occurring in induction machine diagnosis by parameters estimation. In: 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives; 2011: 105–12.
Karami F, Poshtan J, Poshtan M. Detection of broken rotor bars in induction motors using nonlinear Kalman filters. ISA Transactions. 2010;49(2):189–95.
Nguyen V, Wang D, Seshadrinath J, Nadarajan S, Vaiyapuri V. Fault severity estimation using nonlinear Kalman filter for induction motors under inter-turn fault. In: 42nd Annual Conference of the IEEE Industrial Electronics Society (IECON); 2016: 1488–93.
Nguyen V, Wang D, Seshadrinath J, Ukil A, Krishna MS, Nadarajan S. A Method for Incipient Interturn Fault Detection and Severity Estimation of Induction Motors Under Inherent Asymmetry and Voltage Imbalance. IEEE Transactions on Transportation Electrification. 2017;3(3):703–15.
Kallesøe CS, Izadi-Zamanabadi R, Vadstrup PP, Rasmussen H. Observer-based estimation of stator-winding faults in delta-connected induction motors: A linear matrix inequality approach. IEEE Transactions on Industry Applications. 2007;43(4):1022–31.
De Angelo CH, Bossio GR, Giaccone SJ, Valla MI, Solsona JA, Garcia GO. Online Model-Based Stator-Fault Detection and Identification in Induction Motors. IEEE Transactions on Industrial Electronics. 2009;56(11):4671–80.
Abdallah H, Benatman K. Stator winding inter-turn short-circuit detection in induction motors by parameter identification. IET Electric Power Applications. 2017;11(2):272–88.
Chouiref H, Boussaid B, Abdelkrim MN, Aubrun C. Nonlinear fault tolerant control-based parameter estimation diagnosis: Application to induction motors. In: 10th International Multi-Conferences on Systems, Signals & Devices (SSD); 2013:1–6.
Treetrong J. Electric Motor Fault Diagnosis Based on Parameter Estimation Approach Using Genetic Algorithm. In: International Multi Conference of Engineers and Computer Scientist (IMECS); 2010.
Duan F, Zivanovic R. Condition Monitoring of an Induction Motor Stator Windings Via Global Optimization Based on the Hyperbolic Cross Points. IEEE Transactions on Industrial Electronics. 2015;62(3):1826–34.
Sellami T, Berriri H, Jelassi S, Mimouni MF. Sliding mode observer-based fault-detection of inter-turn short-circuit in induction motor. In: 14th International Conference on Sciences and Techniques of Automatic Control & Computer Engineering - STA’2013; 2013:524–529.
Toliyat HA, Levi E, Raina M. A review of RFO induction motor parameter estimation techniques. IEEE Transactions on Energy Conversion. 2003;18(2):271–83.
Syam P, Kumar R, Das S, Chattopadhyay AK. Review on model reference adaptive system for sensorless vector control of induction motor drives. IET Electric Power Applications. 2015;9(7):496–511.
Zorgani Y, Jouili M, Koubaa Y. A very low speed sensorless control induction motor drive with online rotor resistance using MRAS Scheme. Power Electronics and Drives. 2018;3(38):171–186.
Zorgani YA, Koubaa Y, Boussak M. Sensorless speed control with MRAS for induction motor drive. In: XXth International Conference on Electrical Machines (ICEM); 2012:2259–65.
Bednarz SA, Dybkowski M. On-line detection of the rotor faults in the induction motor drive using parameter estimator. In: 2018 International Symposium on Electrical Machines (SME); 2018. p. 1–5.
Vasic V, Vukosavic SN, Levi E. A stator resistance estimation scheme for speed sensorless rotor flux-oriented induction motor drives. IEEE Trans Energy Convers. 2003 Dec;18(4):476–83.
Bednarz SA, Dybkowski M, Wolkiewicz M. Identification of the Stator Faults in the Induction Motor Drives Using Parameter Estimator. In: IEEE 18th International Power Electronics and Motion Control Conference (PEMC); 2018. p. 688–93.
Pawlak M, Orłowska-Kowalska T. Application of the simplified two axial model for rotor faults modeling of the induction motor. Przegląd Elektrotechniczny. 2006;82(10):48–53. Polish.
Kaźmierkowski MP, Krishnan R, Blaabjerg F. Control in power electronics: selected problems. Elsevier; 2003.
Dybkowski M. Universal Speed and Flux Estimator for Induction Motor. Power Electron Drives. 2018;3(1):157–69.
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