Stator fault diagnosis of BLDC motor at varying speed operation using least square support vector machine

• Autorzy: Susilo Didik Djoko , Ubaidillah Ubaidillah , Prabowo Aditya Rio

Identyfikatory

DOI

10.29354/diag/191426

• Języki publikacji: EN

Abstrakty

EN

In BLDC motor applications, stator failure is a common occurrence. Therefore, this study presents a method to diagnose stator failure in BLDC motor when it is operated at a different speed. Furthermore, this study examined the motor in normal condition and the motor with a stator fault. The vibration and current signals are measured from BLDC motor operating at 400 rpm, 450 rpm and 480 rpm. The signals are recorded at a sampling rate of 10 kHz, and the time and frequency domain features are extracted from the sample signals. The distance evaluation technique is used to select the features with the highest effectiveness factor, and a combination of features in the time and frequency domains is used as a predictor in the Least Square Support Vector (LSSVM) model. The results show that the LSSVM model performs very well in diagnosing BLDC stator failure at different speeds using both vibration and current signals. The classification accuracy is 96.5% and 98.83% for vibration and current data, respectively. With its high prediction accuracy, the proposed method has the potential to be developed as a maintenance tool in the industry.

Słowa kluczowe

EN

BLDC motor; stator faults; distance evaluation technique; LSSVM

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 3
• Strony: art. no. 2024308
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Susilo Didik Djoko

• Mechanical Engineering Department of Sebelas Maret University, Surakarta, Central Java, Indonesia

• https://orcid.org/0000-0001-7286-4017

autor

Ubaidillah Ubaidillah

• Mechanical Engineering Department of Sebelas Maret University, Surakarta, Central Java, Indonesia

• https://orcid.org/0000-0002-7190-5849

autor

Prabowo Aditya Rio

• Mechanical Engineering Department of Sebelas Maret University, Surakarta, Central Java, Indonesia

• https://orcid.org/0000-0001-5217-5943

Bibliografia

• 1. Sutar AR, Bhide GG, Mane JJ. Ijesrt international journal of engineering sciences & research technology implementation and study of Bldc motor drive system. International Journal of Engineering Sciences & Research Technology 2016; 5(5): 57-64.
• 2. Tashakori A, Ektesabi M. Inverter switch fault diagnosis system for BLDC motor drives. Engineering Letters 2014; 22(3): 118-24.
• 3. Da Y, Shi X, Krishnamurthy M. Health monitoring, fault diagnosis and failure prognosis techniques for brushless permanent magnet machines. 2011 IEEE Vehicle Power and Propulsion Conference, VPPC 2011. https://doi.org/10.1109/VPPC.2011.6043248.
• 4. Chen Y, Liang S, Li W, Liang H, Wang C. Faults and diagnosis methods of permanent magnet synchronous motors: A review. Applied Sciences. 2019; 9(10). https://doi.org/10.3390/app9102116.
• 5. Usman A, Rajpurohit BS. Modeling and classification of stator inter-turn fault and demagnetization effects in BLDC motor using rotor back-EMF and radial magnetic flux analysis. IEEE Access 2020; 8: 118030-49. https://doi.org/10.1109/ACCESS.2020.3005038.
• 6. Blesa J, Quevedo J, Puig V, Nejjari F, Zaragoza R, Rolán A. Fault diagnosis and prognosis of a brushless DC motor using a Mmodel-based Approach. Phme 2020 2020: 1-8.
• 7. Roczek K, Rogala T. Induction motor diagnosis with use of electric parameters. Diagnostyka 2019; 20(4): 65-74. https://doi.org/10.29354/diag/113000.
• 8. Rajagopalan S, Aller JM, Restrepo JA, Habetler TG, Harley RG. A novel analytic wavelet ridge detector for dynamic eccentricity detection in BLDC motors under dynamic operating conditions. IECON Proceedings (Industrial Electronics Conference) 2005; 2005: 1443-8. https://doi.org/10.1109/IECON.2005.1569117.
• 9. Rajagopalan S, Aller JM, Restrepo JA, Habetler TG, Harley RG. Detection of rotor faults in brushless DC motors operating under nonstationary conditions. IEEE Transactions on Industry Applications 2006; 42(6):1464-77. https://doi.org/10.1109/TIA.2006.882613.
• 10. Jafari A, Faiz J, Jarrahi MA. A simple and efficient current-based method for interturn fault detection in BLDC motors. IEEE Transactions on Industrial Informatics 2021; 17(4): 2707-15. https://doi.org/10.1109/TII.2020.3009867.
• 11. Kumar V. A review of fundamental shaft failure analysis. International Research Journal of Engineering and Technology 2016: 389-95.
• 12. Tabasi M, Ojaghi M, Mostafavi M. Vibration analysis as useful domain for detection of bearing fault signals in induction motors. International Journal of Engineering, Transactions B: Applications 2021; 34(8): 2010-20. https://doi.org/10.5829/ije.2021.34.08b.22.
• 13. Kanović Ž, Matić D, Jeličić Z, Petković M. Induction motor fault diagnosis based on vibration analysis: A case study. Journal on Processing and Energy in Agriculture 2013; 17(1): 47-50.
• 14. Shifat TA, Hur J wook. An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals. 2020(June). https://doi.org/10.1109/ACCESS.2020.3000856.
• 15. Zurita G, Sánchez V, Cabrera D. a Review of Vibration Machine Diagnostics By Using Artificial Intelligence Methods. Investigacion & Desarrollo 2016; 16(1): 102-14. https://doi.org/10.23881/idupbo.016.1-8i.
• 16. Bhattacharyya S, Sen D, Adhvaryyu S, Mukherjee C. Induction motor fault diagnosis by motor current signature analysis and neural network techniques. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 -2804) 2015; 3(1): 12-8.
• 17. Chouhan A, Gangsar P, Porwal R, Mechefske CK. Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions. Vibroengineering Procedia 2020; 30: 55-60. https://doi.org/10.21595/vp.2020.21334.
• 18. Lee CY, Wen MS, Zhuo GL, Le TA. Application of ANN in induction-motor fault-detection system established with MRA and CFFS. Mathematics 2022; 10(13). https://doi.org/10.3390/math10132250.
• 19. Yang BS, Di X, Han T. Random forests classifier for machine fault diagnosis. Journal of Mechanical Science and Technology 2008; 22(9): 1716-25. https://doi.org/10.1007/s12206-008-0603-6.
• 20. Cabrera D, Sancho F, Sánchez RV, Zurita G, Cerrada M, Li C, et al. Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition. Frontiers of Mechanical Engineering 2015; 10(3): 277-86. https://doi.org/10.1007/s11465-015-0348-8.
• 21. Mishra RK, Choudhary A, Mohanty AR, Fatima S. Multi-domain Bearing Fault Diagnosis using support vector machine. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies, GUCON 2021. https://doi.org/10.1109/GUCON50781.2021.9573613.
• 22. YANG BS, WIDODO A. Support vector machine for machine fault diagnosis and prognosis. Journal of System Design and Dynamics 2008; 2(1): 12-23. https://doi.org/10.1299/jsdd.2.12.
• 23. Widodo A, Kim EY, Son JD, Yang BS, Tan ACC, Gu DS, et al. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications 2009; 36(3 PART 2): 7252-61. https://doi.org/10.1016/j.eswa.2008.09.033.
• 24. Glowacz A. Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED. Archives of Electrical Engineering 2016; 65(4): 733-44. https://doi.org/10.1515/aee-2016-0051.
• 25. Tuerxun W, Chang X, Hongyu G, Zhijie J, Huajian Z. Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm. IEEE Access 2021; 9: 69307-15. https://doi.org/10.1109/ACCESS.2021.3075547.
• 26. Fan Y, Zhang C, Xue Y, Wang J, Gu F. A Bearing fault diagnosis using a support vector machine optimised by the self-regulating particle swarm. Shock and Vibration 2020. https://doi.org/10.1155/2020/9096852.
• 27. Shao SY, Sun WJ, Yan RQ, Wang P, Gao RX. A Deep learning approach for fault diagnosis of induction motors in manufacturing. Chinese Journal of Mechanical Engineering (English Edition) 2017; 30(6): 1347-56. https://doi.org/10.1007/s10033-017-0189-y.
• 28. Jiang W, Wang C, Zou J, Zhang S. Application of deep learning in fault diagnosis of rotating machinery. Processes 2021; 9(6). https://doi.org/10.3390/pr9060919.
• 29. Techane AW, Wang YF, Weldegiorgis BH. Rotating machinery prognostics and application of machine learning algorithms: Use of deep learning with similarity index measure for health status prediction. Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM 2018: 1-7.
• 30. Alshorman O, Alshorman A. A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines. International Journal of Electrical and Computer Engineering 2021;11(4):2820-9. https://doi.org/10.11591/ijece.v11i4.pp2820-2829.
• 31. Patel JP, Upadhyay SH. Comparison between artificial neural network and support vector method for a fault diagnostics in rolling element bearings. Procedia Engineering. 2016;144:390-7. https://doi.org/10.1016/j.proeng.2016.05.148.
• 32. Tyagi S, Panigrahi SK. A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks. Journal of Applied and Computational Mechanics 2017; 3(1): 80-91. https://doi.org/10.22055/jacm.2017.21576.1108.
• 33. Saberi M, Azadeh A, Nourmohammadzadeh A, Pazhoheshfar P. Comparing performance and robustness of SVM and ANN for fault diagnosis in a centrifugal pump. MODSIM 2011-19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty. 2011:433-9. https://doi.org/10.36334/modsim.2011.a5.saberi.
• 34. Birame M, Taibi D, Bessedik Sa, Benkhoris MF. Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor. Diagnostyka 2020;21(4):35-41. https://doi.org/10.29354/diag/130283.
• 35. Dong S, Luo T. Bearing degradation process prediction based on the PCA and optimized LS-SVM model. Measurement: Journal of the International Measurement Confederation 2013; 46(9): 3143-52. https://doi.org/10.1016/j.measurement.2013.06.038.
• 36. Shifat TA, Hur JW. ANN assisted multi sensor information fusion for BLDC motor fault diagnosis. IEEE Access 2021;9:9429-41. https://doi.org/10.1109/ACCESS.2021.3050243.
• 37. Susilo DD, Widodo A, Prahasto T, Nizam M. Fault diagnosis of roller bearing using parameter evaluation technique and multi-class support vector machine. AIP Conference Proceedings 2017;1788. https://doi.org/10.1063/1.4968334.
• 38. Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters 1999; 9(3): 293-300. https://doi.org/10.1023/A:1018628609742.