Abstract
One of the key tasks for achieving improved energy consumption and energy efficiency in the operation of electromechanical systems with induction motor drive is the development of reliable systems for diagnostics and assessment of the electromechanical equipment technical condition. Diagnostics based on intelligent approaches using machine learning tools allows building a proactive system for early detection of equipment defects and reducing operating costs. The paper presents a sequential application of Singular Spectrum Analysis (SSA) and Singular Value Decomposition (SVD) for induction motor fault detection. This approach is chosen on the basis of the conducted research in the field of current diagnostics methods and time series analysis methods. The description of the mathematical apparatus of the proposed method with the development of an algorithm for processing current and voltage signals was performed. Detection of defect occurrence at early stages allows expanding the range of detectable defects and faults. The methods were validated on the experimental laboratory bench of electric drive with the developed data recording system. A total of four conditions of the machine under load variation are considered in the work, signal processing of which is performed in the Python programming language. To evaluate the results of the generalized current decomposition, the level of contribution and the migration estimation of the characteristic components separated categorically were investigated. Changes in the behavior and contributions of the cumulative contribution of the two categories demonstrate the onset of the fault occurrence and make it possible to differentiate the machine states. Thus, the results of the experiment on the loosening of electric motor mounting bolts prove the effectiveness of this diagnostic technique.
Keywords
induction motor, current diagnostics, fault diagnostics, singular value decomposition, SVD, signal components, technical condition, monitoring.
1. Gangsar P., Tiwari R. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing. 2020, vol. 144. doi: 10.1016/j.ymssp.2020.10690
2. Sintoni M., Macrelli E., Bellini A., Bianchini C. Condition. Monitoring of Induction Machines: Quantitative Analysis and Comparison. Sensors. 2023, vol. 23. doi: 10.3390/s23021046
3. Koteleva N., Buslaev G.V., Valnev V., Kunshin A. Augmented Reality System and Maintenance of Oil Pumps. International Journal of Engineering, Transactions B: Applications. 2020, vol. 33(8), pp. 1620-1628. doi: 10.5829/ije.2020.33.08b.20
4. Tiwari D., Miscandlon J., Tiwari A., Jewell G.W. A Review of Circular Economy Research for Electric Motors and the Role of Industry 4.0 Technologies. Sustainability. 2021, vol. 13. doi: 10.3390/su13179668
5. Ewert P., Kowalski C.T., Orlowska-Kowalska T. Low-Cost Monitoring and Diagnosis System for Rolling Bearing Faults of the Induction Motor Based on Neural Network Approach. Electronics. 2020, vol. 9(9). doi: 10.3390/electronics9091334
6. Kozyaruk A.E., Tung l.V., Vasilev B.U. Improving the torque direct control method of the asynchronous motor in the converter using the active rectifier. J. Phys.: Conf. Ser. 2021, vol. 1753. doi: 10.1088/1742-6596/1753/1/012025
7. Shibanov D.A., Ivanov S.L., Ivanov A.A. Digital doubles in mining engineering as a tool to improve the efficiency of mining machinery operation. Gornyi informatsionno-analiticheskiy bulleten. [MIAB. Mining Information and Analytical Bulletin], 2022, no. S3-1. doi: 10.25018/0236_1493_2022_5_3_13 (In Russian)
8. Sankararaman S. Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing. 2015, vol. 52-53, pp. 228-247. doi: 10.1016/j.ymssp.2014.05.029
9. Reyes-Malanche J.A., Villalobos-Pina F.J., Ramırez-Velasco E., Cabal-Yepez E., Hernandez-Gomez G., Lopez-Ramirez M. Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic. Energies. 2023, vol. 16(1), p. 516. doi: 10.3390/en16010516
10. Muthukumaran S., Rammohan A., Sekar S., Maiti M., Bingi K. Bearing Fault Detection in Induction Motors Using Line Currents. ECTI Transactions on Electrical Engineering, Electronics, and Communications. 2021, vol. 19(2), pp. 209-219. doi: 10.37936/ecti-eec.2021192.244163
11. Fournier E., Picot A., Regnier J., Yamdeu M. T., Andrejak J.-M., Maussion P. Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis With Reference. IEEE Transactions on Industrial Electronics. 2015, vol. 62(3), pp. 1879-1887. doi: 10.1109/tie.2014.2341561
12. Kompella K.D., Rao M.V.G., Rao R.S. Bearing fault detection in a 3 phase induction motor using stator current frequency spectral subtraction with various wavelet decomposition techniques. Ain Shams Engineering Journal. 2018, vol. 9(4), pp. 2427-2439. doi: 10.1016/j.asej.2017.06.002
13. Silva José L.H., Marques Cardoso A.J. Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach. 31st Annual Conference of IEEE Industrial Electronics Society. 2005, pp. 2591-2596. doi: 10.1109/IECON.2005.1569315
14. Koteleva N., Korolev N., Zhukovskiy Y., Baranov G. A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage. Sensors. 2021, vol. 21(23), p. 7900. doi: 10.3390/s21237900
15. Merizalde Y., Hernández-Callejo L., Duque-Perez O. State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors. Energies. 2017, vol. 10(7), p. 1056. doi: 10.3390/en10071056
16. Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I. IEEE Transactions on Industry Applications. 1985, vol. IA-21(4), pp. 853-864. doi:10.1109/tia.1985.349532
17. State Standard ISO 10816-1-97. Mechanical vibration. Evaluation of machine vibration by measurements on non-rotating parts. Moscow, 1997. (In Russian)
18. Thomson W. Vibration Monitoring of Induction Motors and Case Histories on Shaft Misalignment and Soft Foot. In Vibration Monitoring of Induction Motors: Practical Diagnosis of Faults via Industrial Case Studies. Cambridge, Cambridge University Press, 2020, pp. 1-46. doi: 10.1017/9781108784887.002
19. Garcia-Perez A., Romero-Troncoso R. de J., Cabal-Yepez E., Osornio-Rios R.A. The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors. IEEE Transactions on Industrial Electronics. 2011, vol. 58(5), pp. 2002-2010. doi: 10.1109/tie.2010.2051398
20. Hachemi Benbouzid M. El. A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics. 2000, vol. 47(5), pp. 984-993. doi: 10.1109/41.873206
21. Knyazkina V.I., Ivanov S.L. Improvement of the system of maintenance and repair of mining machines according to the actual state. Gornyi informatsionno-analiticheskiy bulleten. [MIAB. Mining Information and Analytical Bulletin], 2022, no. 6-2, pp. 223-236. doi: 10.25018/0236_1493_2022_62_0_223 (In Russian)
22. Kuzmin O.V., Kedrin V.S. Analysis of the structure of harmonic series of dynamics based on singular decomposition algorithm. Problemy upravleniya [Control Sciences], 2013, no. 1. Available at: https://cyberleninka.ru/article/n/analiz-struktury-garmonicheskih-ryadov-dinamiki-na-baze-algoritma-singulyarnogo-razlozheniya (accessed 22 April 2022). (In Russian)
23. Golyandina N., Korobeynikov A.Basic Singular Spectrum Analysis and forecasting with R. Computational Statistics & Data Analysis. 2014, vol. 71, pp. 934-954. doi: 10.1016/j.csda.2013.04.009
24. Golyandina N. Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing. WIREs Comput Stat. 2020, vol. 12. doi: 10.1002/wics.1487
25. Gospodarikov A.P., Revin I.E., Morozov K.V. Composite model of seismic monitoring data analysis during mining operations on the example of the Kukisvumchorrskoye deposit of AO Apatit. Zapiski gornogo instituta [Journal of Mining Institute], 2023, vol. 262, pp. 571-580. doi: 10.31897/PMI.2023.9. (In Russian)
26. Zhukovskiy Y., Buldysko A., Revin I. Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current. Energies. 2023, vol. 16(8). doi: 10.3390/en1608330
27. Liang L., Liu C., Liu F..Bearing fault diagnosis based on singular value distribution of impulse response segment. ISA Transactions. 2023, vol. 134, pp. 511-528. doi:1016/j.isatra.2022.08.015
28. Miao Y., Zhang B., Yi Y., Lin J. Application of improved reweighted singular value decomposition for gearbox fault diagnosis based on built-in encoder information. Measurement. 2021, vol. 168. doi: 10.1016/j.measurement.2020.108295
29. Zhao X., Ye B. Feature frequency extraction algorithm based on the singular value decomposition with changed matrix size and its application in fault diagnosis. Journal of Sound and Vibration. 2022, vol. 526. doi: 10.1016/j.jsv.2022.116848
30. Cui L., Liu Y., Zhao D. Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference. Measurement Science and Technology. 2022, vol. 3(9), doi: 10.1088/1361-6501/ac672b
31. Liang L., Ding X., Liu F., Chen Y., Wen H. Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis. Sensors. 2021, vol. 21(11), p. 3680. doi: 10.3390/s21113680
32. Current Sensor LA 25-NP/SP44. Technical passport. Available at: https://www.lem.com/images/stories/files/RU/ru/Datasheets/la_25_np_sp44.pdf (accessed 14 May 2023). (In Russian)
33. Voltage Sensor LV 25-P/SP5. Technical passport. Available at: https://doc.platan.ru/pdf/datasheets/lem/lv25-p-sp5.pdf (accessed 14 May 2023). (In Russian)
34. Mais J. Spectrum Analysis. The key features of analyzing spectra. SKF. 2002. Available at: https://www.skf.com/binaries/pub12/Images/0901d1968024acef-CM5118-EN-Spectrum-Analysis_tcm_12-113997.pdf (accessed 14 May 2023)
35. Kuzhekov S.L., Rogachev V.A. Comparative analysis of stator current module spectra of the generalized stator current vector and instantaneous values of phase currents when recognizing the eccentricity of the induction motor rotor. Izvestiya vuzov. Severo-Kavkazskiy region. Tekhnicheskie Nauki. [Bulletin of Higher Educational Institutions. North Caucasus region. Technical Sciences], 2008, no. 4, pp. 101-104. (In Russian)
Korolev N.A., Buldysko A.D., Zhukovskiy Yu.L. Detection of Early-Stage Mechanical Weakening of Induction Motor Based on Consumption Current Singular Value Decomposition. Elektrotekhnicheskie sistemy i kompleksy [Electrotechnical Systems and Complexes], 2023, no. 3(60), pp. 72-80. (In Russian). https://doi.org/10.18503/2311-8318-2023-3(60)-72-80