Fault Diagnosis of Induction Motors: An Architecture for Real-Time Assessment as a Cyber-Physical System
Abstract
Induction motors (IMs) have a crucial and significant role in various industrial sectors. With the prolonged operation of IMs, faults tend to develop that can be classified into five major categories, i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and load torque fluctuations. If the faults go undetected, it may lead to catastrophic failure. Hence, the predictive-based condition monitoring technique has evolved as a real-time fault diagnosis that exploits the revolutionary idea of cyber-physical system (CPS). Furthermore, motor current signature analysis (MCSA) is a non-invasive fault diagnosis technique of a motor that can be used to investigate the presence of five fault types . However, the major constraint that industries face today is the on-field implementation of MCSA-based fault diagnosis involving CPS-based architecture, executed in an automated manner. Hence, the present article depicts algorithms that aim at real-time monitoring of IMs through a CPS framework. The proposed methodology is automated, does not involve any human intervention, and has been validated with real-time experiments, depicting its effectiveness and practicality.
Keywords:
cyber-physical system, fault diagnosis, induction motors, motor current signature analysisReferences
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24. Ge L., Fan W., Xiao X., Gan F., Lai X., Deng H., Huang Q., Rolling bearing fault diagnosis method based on improved variational mode decomposition and information entropy, Engineering Transactions, 70(1): 23–51, 2022, https://doi.org/10.24423/EngTrans.1390.20220207
25. Akbari A., Danesh M., Khalili K., A method based on spindle motor current harmonic distortion measurements for tool wear monitoring, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(12): 5049–5055, 2017, https://doi.org/10.1007/s40430-017-0762-4
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27. García-Sanz-Calcedo J., Salgado D.R., González A.G., Drilling projects by tool condition monitoring system (TCMS), Engineering Transactions, 64(4): 555–561, 2016, https://doi.org/10.24423/EngTrans.674.2016
28. Mohanty A.R., KarC., Routray, A., An electromagnetic device for fault detection and identification in rotating mechanical components, The Patent Office Journal, No. 43/2017, Part 2, Patent No. 288510, 2005.
29. Singh S., Kumar A., Kumar N., Motor current signature analysis for bearing fault detection in mechanical systems, Procedia Materials Science, 6: 171–77, 2014, https://doi.org/10.1016/j.mspro.2014.07.021
30. Benbouzid M., A review of induction motors signature analysis as a medium for faults detection, IEEE Transactions on Industrial Electronics, 47(5): 984–993, 2000, https://doi.org/10.1109/41.873206
31. Kar C., Mohanty A.R. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform, Journal of Sound and Vibration, 311(1–2): 109–132, 2008, https://doi.org/10.1016/j.jsv.2007.08.023
32. Bonaldi E.L., de Lacerda de Oliveira L.E., Borges da Silva J.G., Lambert-Torresm G., Borges da Silva L.E., Predictive maintenance by electrical signature analysis to induction motors, [In:] Induction Motors – Modelling and Control, Esteves Araújo R [Ed.], IntechOpen, 2012, https://doi.org/10.5772/48045
33. Miljković D., Brief review of motor current signature analysis, HDKBR INFO Magazin, 5(1): 14–26, 2015.
34. Saruhan H., Saridemir S., Çiçek A., Uygur I., Vibration analysis of rolling element bearings defects, Journal of Applied Research and Technology, 12(3): 384–395, 2014, https://doi.org/10.1016/S1665-6423%2814%2971620-7
35. Humayed A., Lin J., Li F., Luo B., Cyber-physical systems security – a survey, IEEE Internet of Things Journal, 4(6): 1802–1831, 2017, https://doi.org/10.1109/JIOT.2017.2703172
36. Benbouzid M.E.H., Kliman G.B., What stator current processing-based technique to use for induction motor rotor faults diagnosis?, IEEE Transactions on Energy Conversion, 18(2): 238–244, 2003, https://doi.org/10.1109/TEC.2003.811741
37. Pal R.S.C., Mohanty A.R., Dynamical modelling of three-phase induction motor with broken rotor bars, National Conference on Condition Monitoring, 20–21 September 2019.
38. Cruz S.M.A., Toliyat H.A., Cardoso A.J.M., DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive, IEEE Transactions on Energy Conversion, 20(2): 329–335, 2005, https://doi.org/10.1109/TEC.2005.845531
39. Pal R.S.C., Mohanty A.R., A simplified dynamical model of mixed eccentricity fault in a three-phase induction motor, IEEE Transactions on Industrial Electronics, 68(5): 4341–4350, 2021, https://doi.org/10.1109/TIE.2020.2987274
40. Pal R.S.C., Mohanty A.R., Bearing fault detection in permanent magnet synchronous motors using vibration and motor current signature analysis, 28th International Congress on Sound and Vibration (ICSV), 24–28 July 2022.
41. Kar C., Mohanty A.R., Monitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical Systems and Signal Processing, 20(1): 158–187, 2006, https://doi.org/10.1016/j.ymssp.2004.07.006
42. Mohanty A.R., Kar C., Fault detection in a multistage gearbox by demodulation of motor current waveform, IEEE Transactions on Industrial Electronics, 53(4):1285–1297, 2006, https://doi.org/10.1109/TIE.2006.878303
43. ISO 20958:2013 Condition monitoring and diagnostics of machine systems – Electrical signature analysis of three-phase induction motors, http://www.iso.org 2013.
2. Liu Y., Miao C., Li X., Ji J., Meng D., Research on the fault analysis method of belt conveyor idlers based on sound and thermal infrared image features, Measurement, 186: Article number: 110177, 2021, https://doi.org/10.1016/j.measurement.2021.110177
3. Mohanty A.R., Machinery condition monitoring: Principles and Practices, Boca Raton, FL, USA: CRC Press, 2014.
4. Abdel-Basset M., Imran M., Special issue on industrial internet of things for automotive industry – new directions, challenges and applications, Mechanical Systems and Signal Processing, 142: Article number: 106751, 2020, https://doi.org/10.1016/j.ymssp.2020.106751
5. Geismann J., Bodden E., A systematic literature review of model-driven security engineering for cyber–physical systems, Journal of Systems and Software, 169: Article number: 110697, 2020, https://doi.org/10.1016/j.jss.2020.110697
6. Lewis A.J., Campbell M., Stavroulakis P., Performance evaluation of a cheap, open source, digital environmental monitor based on the Raspberry Pi, Measurement, 87:228–235, 2016, https://doi.org/10.1016/j.measurement.2016.03.023
7. Goundar S.S., Pillai M.R., Mamun K.A., Islam F.R., Deo R., Real time condition monitoring system for industrial motors, 2015 2nd Asia–Pacific World Congress on Computer Science and Engineering, APWC on CSE 2015, 1–9, 2015, https://doi.org/10.1109/APWCCSE.2015.7476232
8. Civerchia F., Bocchino S., Salvadori C., Rossi E., Maggiani L., Petracca M., Industrial internet of things monitoring solution for advanced predictive maintenance applications, Journal of Industrial Information Integration, 7: 4–12, 2017, https://doi.org/10.1016/j.jii.2017.02.003
9. Wu D., Liu S., Zhang L., Terpenny J., Gao R. X., Kurfess T., Guzzo J.A., A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, Journal of Manufacturing Systems, 43(1): 25–34, 2017, https://doi.org/10.1016/j.jmsy.2017.02.011
10. Ganga D., Ramachandran V., IoT-based vibration analytics of electrical machines, IEEE Internet of Things Journal, 5(6): 4538–4549, 2018, https://doi.org/10.1109/JIOT.2018.2835724
11. Jung D., Zhang Z., Winslett M., Vibration analysis for IoT enabled predictive maintenance, Proceedings – 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 1271–1282, 2017, https://doi.org/10.1109/ICDE.2017.170
12. Xenakis A., Karageorgos A., Lallas E., Chis A.E., González-Vélez H., Towards distributed IoT/Cloud based fault detection and maintenance in industrial automation, Procedia Computer Science, 151: 683–690, 2019, https://doi.org/10.1016/j.procs.2019.04.091
13. Magadán L., Suárez F.J., Granda J.C., García D.F., Low-cost real-time monitoring of electric motors for the Industry 4.0, Procedia Manufacturing, 42: 393–398, 2020, https://doi.org/10.1016/j.promfg.2020.02.057
14. Firmansah A., Aripriharta, Mufti N., Affandi A.N., Zaeni I.A.E., Self-powered IoT based vibration monitoring of induction motor for diagnostic and prediction failure, IOP Conference Series: Materials Science and Engineering, 588(1): 012016 2019, https://doi.org/10.1088/1757-899X/588/1/012016
15. Kunthong J., Sapaklom T., Konghirun M, Prapanavarat C., Ayudhya P.N.N., Mujjalinvimut E., Boonjeed S., IoT-based traction motor drive condition monitoring in electric vehicles: Part 1, Proceedings of the IEEE 12th International Conference on Power Electronics and Drive Systems, pp. 1184–1188, 2017, https://doi.org/10.1109/PEDS.2017.8289143
16. Choudhary A., Jamwal S., Goyal D., Dang R.K., Sehgal S., Condition monitoring of induction motor using Internet of Things (IoT), [In:] Kumar H., Jain P. (Eds.), Recent Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering, Springer, Singapore, pp. 353–365, 2020, https://doi.org/10.1007/978-981-15-1071-7_30
17. Khan N., Rafiq F., Abedin F., Khan F.U., IoT based health monitoring system for electrical motors, 15th International Conference on Emerging Technologies (ICET), pp. 1–6, 2019, https://doi.org/10.1109/ICET48972.2019.8994398
18. Ashmitha M., Dhanusha D.J., Vijitlin M.S., George G.B., Real time monitoring IoT based methodology for fault detection in induction motor, Irish Interdisciplinary Journal of Science & Research (IIJSR), 5(2): 72–83, 2021, https://ssrn.com/abstract=3849600
19. Zhang H.F., Kang W., Design of the data acquisition system based on STM32, Procedia Computer Science, 17: 222–228, 2013, https://doi.org/10.1016/j.procs.2013.05.030
20. Liboni L.H.B., Flauzino R.A., da Silva I.N., Marques C.E.C., Efficient feature extraction technique for diagnosing broken bars in three-phase induction machines, Measurement, 134: 825–834, 2019, https://doi.org/10.1016/j.measurement.2018.12.005
21. Lamim Filho P.C.M., Pederiva R., Brito J.N., Detection of stator winding faults in induction machines using flux and vibration analysis, Mechanical Systems and Signal Processing, 42(1–2): 377–387, 2014, https://doi.org/10.1016/j.ymssp.2013.08.033
22. Tang G.-J., He Y.-L., Wan S.-T., Xiang L., Investigation on stator vibration characteristics under air-gap eccentricity and rotor short circuit composite faults, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 36(3): 511–522, 2014, https://doi.org/10.1007/s40430-013-0072-4
23. Chen R., Tang L., Hu X., Wu H., Fault diagnosis method of low-speed rolling bearing based on acoustic emission signal and subspace embedded feature distribution alignment, IEEE Transactions on Industrial Informatics, 17(8): 5402–5410, 2021, https://doi.org/10.1109/TII.2020.3028103
24. Ge L., Fan W., Xiao X., Gan F., Lai X., Deng H., Huang Q., Rolling bearing fault diagnosis method based on improved variational mode decomposition and information entropy, Engineering Transactions, 70(1): 23–51, 2022, https://doi.org/10.24423/EngTrans.1390.20220207
25. Akbari A., Danesh M., Khalili K., A method based on spindle motor current harmonic distortion measurements for tool wear monitoring, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(12): 5049–5055, 2017, https://doi.org/10.1007/s40430-017-0762-4
26. Mohanty A.R., Pradhan P.K., Mahalik N.P., Dastidar S.G., Fault detection in a centrifugal pump using vibration and motor current signature analysis, International Journal of Automation and Control, 6(3–4): 261–276, 2012, https://doi.org/10.1504/IJAAC.2012.051884
27. García-Sanz-Calcedo J., Salgado D.R., González A.G., Drilling projects by tool condition monitoring system (TCMS), Engineering Transactions, 64(4): 555–561, 2016, https://doi.org/10.24423/EngTrans.674.2016
28. Mohanty A.R., KarC., Routray, A., An electromagnetic device for fault detection and identification in rotating mechanical components, The Patent Office Journal, No. 43/2017, Part 2, Patent No. 288510, 2005.
29. Singh S., Kumar A., Kumar N., Motor current signature analysis for bearing fault detection in mechanical systems, Procedia Materials Science, 6: 171–77, 2014, https://doi.org/10.1016/j.mspro.2014.07.021
30. Benbouzid M., A review of induction motors signature analysis as a medium for faults detection, IEEE Transactions on Industrial Electronics, 47(5): 984–993, 2000, https://doi.org/10.1109/41.873206
31. Kar C., Mohanty A.R. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform, Journal of Sound and Vibration, 311(1–2): 109–132, 2008, https://doi.org/10.1016/j.jsv.2007.08.023
32. Bonaldi E.L., de Lacerda de Oliveira L.E., Borges da Silva J.G., Lambert-Torresm G., Borges da Silva L.E., Predictive maintenance by electrical signature analysis to induction motors, [In:] Induction Motors – Modelling and Control, Esteves Araújo R [Ed.], IntechOpen, 2012, https://doi.org/10.5772/48045
33. Miljković D., Brief review of motor current signature analysis, HDKBR INFO Magazin, 5(1): 14–26, 2015.
34. Saruhan H., Saridemir S., Çiçek A., Uygur I., Vibration analysis of rolling element bearings defects, Journal of Applied Research and Technology, 12(3): 384–395, 2014, https://doi.org/10.1016/S1665-6423%2814%2971620-7
35. Humayed A., Lin J., Li F., Luo B., Cyber-physical systems security – a survey, IEEE Internet of Things Journal, 4(6): 1802–1831, 2017, https://doi.org/10.1109/JIOT.2017.2703172
36. Benbouzid M.E.H., Kliman G.B., What stator current processing-based technique to use for induction motor rotor faults diagnosis?, IEEE Transactions on Energy Conversion, 18(2): 238–244, 2003, https://doi.org/10.1109/TEC.2003.811741
37. Pal R.S.C., Mohanty A.R., Dynamical modelling of three-phase induction motor with broken rotor bars, National Conference on Condition Monitoring, 20–21 September 2019.
38. Cruz S.M.A., Toliyat H.A., Cardoso A.J.M., DSP implementation of the multiple reference frames theory for the diagnosis of stator faults in a DTC induction motor drive, IEEE Transactions on Energy Conversion, 20(2): 329–335, 2005, https://doi.org/10.1109/TEC.2005.845531
39. Pal R.S.C., Mohanty A.R., A simplified dynamical model of mixed eccentricity fault in a three-phase induction motor, IEEE Transactions on Industrial Electronics, 68(5): 4341–4350, 2021, https://doi.org/10.1109/TIE.2020.2987274
40. Pal R.S.C., Mohanty A.R., Bearing fault detection in permanent magnet synchronous motors using vibration and motor current signature analysis, 28th International Congress on Sound and Vibration (ICSV), 24–28 July 2022.
41. Kar C., Mohanty A.R., Monitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical Systems and Signal Processing, 20(1): 158–187, 2006, https://doi.org/10.1016/j.ymssp.2004.07.006
42. Mohanty A.R., Kar C., Fault detection in a multistage gearbox by demodulation of motor current waveform, IEEE Transactions on Industrial Electronics, 53(4):1285–1297, 2006, https://doi.org/10.1109/TIE.2006.878303
43. ISO 20958:2013 Condition monitoring and diagnostics of machine systems – Electrical signature analysis of three-phase induction motors, http://www.iso.org 2013.
