The paper discusses problems that occur in robotic arm control. The specific problems arise due to the wear of various types of gears (in the presented case, belt gear and worm gear). It is important to note that such errors need to be diagnosed in time, and the way of their elimination has to be determined, which should be resource-intensive and cost-effective. This article describes the basic types of robotic manipulators (robotic arm, telpher and Cartesian), presenting a review and study of the possibilities of errors in the movement of a robot, adjustment of a mechanical system, and determination of a strategy for solving the emerging problems. A comparison between various types of gear faults is also provided. Different ways of diagnosing faults are discussed, based on the advantages and disadvantages of the methods. The main objective of this study is to provide a complete overview of the mechanical areas where disturbances occur, their diagnosing, and methods of their elimination.
1. Karabegović, I. Industrial Robots: Design, Applications and Technology. Nova Science Publishers, New York, NY, 2020.
2. Vullo, V. Gears. Volume 1: Geometric and Kinematic Design. Springer, Cham, 2020.
https://doi.org/10.1007/978-3-030-36502-8_1
3. Davis, J. R. (ed.) Gear Materials, Properties, and Manufacture. ASM International, Materials Park, OH, 2005.
https://doi.org/10.31399/asm.tb.gmpm.9781627083454
4. Dudley, D. W. Handbook of Practical Gear Design. CRC Press, Lancaster, PA, 1994.
5. Radzevich, S. P. Dudley’s Handbook of Practical Gear Design and Manufacture. 3rd ed. CRC Press, FL, 2016.
https://doi.org/10.1201/9781315368122
6. Rassõlkin, A., Orosz, T., Demidova, G. L., Rjabtšikov, V., Vaimann, T. and Kallaste, A. Implementation of Digital Twins for electrical energy conversion systems in selected case studies. Proc. Estonian Acad. Sci., 2021, 70(1), 19–39.
https://doi.org/10.3176/proc.2021.1.03
7. Kudelina, K., Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A. and Lukichev, D. V. Main faults and diagnostic possibilities of BLDC motors. In Proceedings of the 2020 27th International Workshop on Electric Drives: MPEI Department of Electric Drives 90th Anniversay (IWED), Moscow, Russia, 27–30 January 2020. IEEE, 1–6.
https://doi.org/10.1109/IWED48848.2020.9069553
8. Huat, L. K. (ed.) Industrial Robotics: Programming, Simulation and Applications. InTech, 2016.
9. Mortimer, J. and Rooks, B. Industrial robot specifications. In The International Robot Industry Report. Springer, Berlin, Heidelberg, 1987, 217–231.
https://doi.org/10.1007/978-3-662-13174-9_3
10. Pires, J. N. Industrial Robots Programming: Building Applications for the Factories of the Future. Springer, New York, NY, 2007.
11. Colestock, H. Industrial Robotics: Selection, Design, and Maintenance. McGraw-Hill, New York, NY, 2005.
12. Miller, R. K. Industrial Robot Handbook. Springer, New York, NY, 1989.
https://doi.org/10.1007/978-1-4684-6608-9
13. Voss, W. A Comprehensible Guide to J1939. Copperhill Technologies Corporation, Greenfield, MA, 2008.
14. Stuart, S. DC Motors, Speed Controls, Servo Systems: An Engineering Handbook. Elsevier, 2013.
15. Acarnley, P. Stepping Motors: A Guide to Theory and Practice. IET, 2002.
https://doi.org/10.1049/PBCE063E
16. Arduino Self Balancing Robot. www.askix.com (accessed 2021-06-05).
17. Firoozian, R. Servo Motors and Industrial Control Theory. Springer, New York, NY, 2009.
https://doi.org/10.1007/978-0-387-85460-1
18. Tohid, Z. F. B. M. Automatic control valve using servo motor. Report. University Malaysia Pahang, Malaysia, June 2013.
19. Mangudi, A. Design of a Stepper Motor Driver. 3rd ed. Arizona State University, Tempe, AZ, 2013.
20. Dudás, I. The Theory and Practice of Worm Gear Drives. Penton Press, London, 2004.
21. Boner, C. J. Gear and transmission lubricants. Ind. Lubr. Tribol., 1998, 50(1), 121–131.
https://doi.org/10.1108/ilt.1998.01850aad.001
22. Tang, Z., Wang, M., Hu, Y., Mei, Z., Sun, J. and Yan, L. Optimal design of traction gear modification of high-speed EMU based on radial basis function neural network. IEEE Access, 2020, 8, 134619–134629.
https://doi.org/10.1109/ACCESS.2020.3007449
23. Xu, X. and Luo, Y. Modeling and analysis of gear shifting process of non-synchronizer AMT based on collision model. IEEE Access, 2021, 9, 13354–13367.
https://doi.org/10.1109/ACCESS.2021.3052089
24. Shen, Y., Zhang, X., Jiang, H., Zhou, J., Qiao, S., Wang, C. and Ma, T. Comparative study on dynamic characteristics of two-stage gear system with gear and shaft cracks considering the shaft flexibility. IEEE Access, 2020, 8, 133681–133699.
https://doi.org/10.1109/ACCESS.2020.3009398
25. Ozawa, R., Mishima, Y. and Hirano, Y. Design of a transmission with gear trains for underactuated mechanisms. IEEE Trans. Robot., 2016, 32(6), 1399–1407.
https://doi.org/10.1109/TRO.2016.2597319
26. Katsioula, A. G., Karnavas, Y. L. and Boutalis, Y. S. An enhanced simulation model for DC motor belt drive conveyor system control. In Proceedings of the 2018 7th International Conference on Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 7–9 May 2018. IEEE, 1–4.
https://doi.org/10.1109/MOCAST.2018.8376636
27. Ma, K., Wang, X. and Shen, D. Design and experiment of robotic belt grinding system with constant grinding force. In Proceedings of the 2018 25th International Conference on Mechatronics and Machine Vision Practice (M2VIP), Stuttgart, Germany, 20–22 November 2018. IEEE, 2019.
https://doi.org/10.1109/M2VIP.2018.8600899
28. Zhang, S. Model predictive control of operation efficiency of belt conveyor. In Proceedings of the 29th Chinese Control Conference CCC’10, Bejing, China, 29–31 July 2010. IEEE, 1854–1858.
29. Cao, X., Zhang, X., Zhou, Z., Fei, J., Zhang, G. and Jiang, W. Research on the monitoring system of belt conveyor based on suspension inspection robot. In Proceedings of the 2018 IEEE International Conference on Real-Time Computing and Robotics (RCAR), Kandima, Maldives, 1–5 August 2018. IEEE, 2019, 657–661.
https://doi.org/10.1109/RCAR.2018.8621649
30. Kozłowski, T., Wodecki, J., Zimroz, R., Błazej, R. and Hardygóra, M. A diagnostics of conveyor belt splices. Appl. Sci., 2020, 10(18), 6259.
https://doi.org/10.3390/APP10186259
31. Shoaib, M., Kim, M. and Cheong, J. Friction modeling of a robot driven by worm gear transmission. In Proceedings of the 2018 18th International Conference on Control, Automation and Systems (ICCAS), October 2018, 183–187.
32. Henson, P. and Marais, S. The utilization of duplex worm gears in robot manipulator arms: A design, build and test approach. In Proceedings of the 2012 5th Robotics and Mechatronics Conference of South Africa (ROBMECH), Johannesburg, South Africa, 26–27 November 2012. IEEE, 2013.
https://doi.org/10.1109/ROBOMECH.2012.6558461
33. Tadakuma, R., Tadakuma, K., Takagi, M., Onishi, S., Matsui, G., Ioka, K. et al. The gear mechanism with passive rollers: The input mechanism to drive the omnidirectional gear and worm gearing. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6–10 May 2013. IEEE, 1520–1527.
https://doi.org/10.1109/ICRA.2013.6630772
34. Ma, B., Li, H., Zahrai, S. and Zhang, H. Virtual prototyping for drive chain optimization in an industrial robot. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011. IEEE, 3–6.
https://doi.org/10.1109/ICRA.2011.5980595
35. Yukawa, T., Takahashi, T., Satoh, Y. and Ohshima, S. Development of combined-type continuous variable transmission with quadric crank chains and one-way clutches. In Proceedings of SICE Annual Conference, 2012, 2151–2156.
36. Liu, W. and Gao, Y. Compensation of variable pitch roller chains for the polygon effect. In Proceedings of the 2011 International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT), Harbin, China, 12–14 August 2011. IEEE, 2900–2903.
https://doi.org/10.1109/EMEIT.2011.6023654
37. Ingvast, J., Wikander, J. and Ridderström, C. The PVT, an elastic conservative transmission. Int. J. Robot. Res., 2006, 25(10), 1013–1032.
https://doi.org/10.1177/0278364906069188
38. Prakash, S. and Hofman, T. Clamping strategies for belt-type continuously variable transmissions: an overview. In Proceedings of the 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), Belfort, France, 11–14 December 2017. IEEE, 2018, 1–6.
https://doi.org/10.1109/VPPC.2017.8330938
39. Litvin, F. L. Development of Gear Technology and Theory of Gearing. National Aeronautics and Space Administration, Lewis Research Center, Cleveland, OH, 1997.
40. Kudelina, K., Asad, B., Vaimann, T., Belahcen, A., Rassõlkin, A., Kallaste, A. and Lukichev, D. V. Bearing fault analysis of BLDC motor for electric scooter application. Designs, 2020, 4(4), 42.
https://doi.org/10.3390/designs4040042
41. Toma, R. N., Kim, C. and Kim, J.-M. Bearing fault classification using ensemble empirical mode decomposition and convolutional neural network. Electronics, 2021, 10(11), 1248.
https://doi.org/10.3390/electronics10111248
42. Cao, L., Shen, Y., Shan, T., Xia, Y., Wang, J. and Lin, Z. Bearing fault diagnosis method based on GMM and Coupled Hidden Markov model. In Proceedings of the 2018 Prognostics and System Health Management Conference (PHM), Chongqing, China, 26–28 October 2018. IEEE, 2019, 932–936.
https://doi.org/10.1109/PHM-Chongqing.2018.00166
43. Dasgupta, A. and Pecht, M. Material failure mechanisms and damage models. IEEE Trans. Reliab., 1991, 40(5), 531–536.
https://doi.org/10.1109/24.106769
44. Shijie, S., Kai, W., Xuliang, Q., Dan, Z., Xueqing, D. and Jiale, S. Investigation on bearing weak fault diagnosis under colored noise. In Proceedings of the 32nd Chinese Control and Decision Conference (CCDC), Hefei, China, 22–24 August 2020. IEEE, 5097–5101.
https://doi.org/10.1109/CCDC 49329.2020.9164548
45. Das, A. and Ray, S. A review on diagnostic techniques of bearing fault and its modeling in induction motor. In Proceedings of the 2020 IEEE Calcutta Conference (CALCON), Kolkata, India, 28–29 February 2020. IEEE, 502–505.
https://doi.org/10.1109/CALCON49167.2020.9106511
46. Chatterton, S., Pennacchi, P. and Vania, A. Electrical pitting of tilting-pad thrust bearings: Modelling and experimental evidence. Tribol. Int., 2016, 103, 475–486.
https://doi.org/10.1016/j.triboint.2016.08.003
47. Jnifene, A. and Andrews, W. Experimental study on active vibration control of a single-link flexible manipulator using tools of fuzzy logic and neural networks. IEEE Trans. Instrum. Meas., 2005, 54(3), 1200–1208.
https://doi.org/10.1109/TIM.2005.847136
48. Murtadho, M., Prasetyono, E. and Anggriawan, D. O. Detection of parallel arc fault on photovoltaic system based on fast Fourier Transform. In Proceedings of the 2020 International Electronics Symposium, Surabaya, Indonesia, 29–30 September 2020, 21–25.
https://doi.org/10.1109/IES50839.2020.9231780
49. Bishop, T. Dealing with shaft and bearing currents. Kentucky Service Co., Lexington, KY, 2017.
50. Tygert, M. Fast algorithms for spherical harmonic expansions, III. J. Comput. Phys., 2010, 229(18), 6181–6192.
https://doi.org/10.1016/j.jcp.2010.05.004
51. Syafiri, M. H. R. A., Prasetyono, E., Khafidli, M. K., Anggriawan, D. O. and Tjahjono, A. Real time series DC arc fault detection based on Fast Fourier Transform. In Proceedings of the 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), Bali, Indonesia, 29–30 October 2018. IEEE, 2019, 25–30.
https://doi.org/10.1109/ELECSYM.2018.8615525
52. Dehina, W., Boumehraz, M. and Kratz, F. Diagnosis of rotor and stator faults by Fast Fourier Transform and discrete wavelet in induction machine. In Proceedings of the 2018 3rd International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), Algiers, Algeria, 28–31 October 2018. IEEE, 2019, 6–11.
https://doi.org/10.1109/CISTEM.2018.8613311
53. Fitrianto, M. I., Wahjono, E. D., Anggriawan, O., Prasetyono, E., Mubarok, R. H. and Tjahjono, A. Identification and protection of series DC arc fault for photovoltaic systems based on Fast Fourier transform. In Proceedings of the 2019 International Electronics Symposium, Surabaya, Indonesia, 27–28 September 2019. IEEE, 159–163.
https://doi.org/0.1109/ELECSYM.2019.8901605
54. Balamurugan, R., Al-Janahi, F., Bouhali, O., Shukri, S., Abdulmawjood, K. and Balog, R. S. Fourier Transform and Short-Time Fourier Transform decomposition for photovoltaic arc fault detection. In Proceedings of the 2020 47th IEEE Photovoltaic Specialists Conference, Calgary, Canada, 15 June – 21 August 2020. IEEE, 2021, 2737–2742.
https://doi.org/10.1109/PVSC45281.2020.9300455
55. Burriel-Valencia, J., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A. and Pineda-Sanchez, M. Short-frequency Fourier Transform for fault diagnosis of induction machines working in transient regime. IEEE Trans. Instrum. Meas., 2017, 66(3), 432–440.
https://doi.org/10.1109/TIM.2016. 2647458
56. Bajpeyee, B. and Sharma, S. N. Detection of bearing faults in induction motors using short time approximate discrete Zolotarev transform. In Proceedings of the International Conference on Signal Processing (ICSP 2016), Vidisha, India, 7–9 November 2016.
https://doi.org/10.1049/cp.2016.1467
57. Vippala, S. R., Bhat, S. and Reddy, A. A. Condition monitoring of BLDC motor using Short Time Fourier Transform. In Proceedings of the 2021 IEEE 2nd International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, India, 8–10 January 2021. IEEE, 110–115.
https://doi.org/10.1109/CMI50323.2021.9362938
58. Yu, L. Bearing fault diagnosis using time-frequency synchrosqueezing transform. In Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China, 6–8 November 2020. IEEE, 2021, 4260–4264.
https://doi.org/10.1109/CAC51589.2020.9327232
59. Bentrah, W., Bessous, N., Sbaa, S., Pusca, R. and Romary, R. A comparative study between the adaptive wavelet transform and DWT methods applied to a outer raceway fault detection in induction motors based on the frequencies analysis. In Proceedings of the 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey, 25–27 September 2020. IEEE, 1–7.
https://doi.org/10.1109/ICEE49691.2020.9249925
60. Merainani, B., Bouzid, A. A., Ratni, A. and Benazzouz, D. Detection of bearing fault using empirical wavelet transform and S transform methods. In Proceedings of the 2020 1st International Conference on Communications, Control Systems and Signal Processing, El Oued, Algeria, 16–17 May 2020. IEEE, 446–453.
https://doi.org/10.1109/CCSSP49278.2020.9151834
61. Wang, X. and Zhang, R. A sensor fault diagnosis method research based on wavelet transform and Hilbert–Huang transform. In Proceedings of the 2013 5th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Hong Kong, China, 16–17 January 2013. IEEE, 81–84.
https://doi.org/10.1109/ICMTMA.2013.32
62. Patwary, R., Chatterjee, H. S., Roy, D. and Choudhury, A. B. Fault diagnosis of a passive magnetic fault current limiter using reverse biorthogonal wavelet transform. In Proceed-ings of the 2017 IEEE Calcutta Conference (CALCON), Kolkata, India, 2–3 December 2017. IEEE, 2018, 407–411.
https://doi.org/10.1109/CALCON.2017.8280765
63. Zaman, S. M. K., Marma, H. U. M. and Liang, X. Broken rotor bar fault diagnosis for induction motors using power spectral density and complex continuous wavelet transform methods. In Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engneering (CCECE), Edmonton, Canada, 5–8 May 2019. IEEE, 1–4.
https://doi.org/10.1109/CCECE.2019.8861517
64. Chen, Q., Nicholson, G., Ye, J. and Roberts, C. Fault diagnosis using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for a railway switch. In Proceedings of the 2020 Prognostics and Health Management Conference (PHM-Besancon), Besancon, France, 4–7 May 2020. IEEE, 67–71.
https://doi.org/10.1109/PHM-Besancon49106.2020.00018
65. Park, B., Kim, D. and Kim, G. Using wavelet transform. IEEE Trans. Plasma Sci., 2004, 32(2), 355–361.
https://doi.org/10.1109/TPS.2004.828123
66. Wang, H., Kang, Y., Yao, L., Wang, H. and Gao, Z. Fault diagnosis and fault tolerant control for T-S fuzzy stochastic distribution systems subject to sensor and actuator faults. IEEE Trans. Fuzzy Syst., 2021, 29(11), 3561–3569.
https://doi.org/10.1109/tfuzz.2020.3024659
67. Bhatnagar, M. and Yadav, A. Fault detection and classification in transmission line using fuzzy inference system. In Proceedings of the 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, 1–3 December 2020. IEEE, 2021, 1–6.
https://doi.org/10.1109/ICRAIE51050.2020.9358386
68. Yang, S., Sun, X. and Chen, D. Bearing fault diagnosis of two-dimensional improved Att-CNN2D neural network based on Attention mechanism. In Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China, 20–22 March 2020. IEEE, 81–85.
https://doi.org/10.1109/ICAIIS49377.2020.9194871
69. Feng, J., Xian, R. and Xie, Y. Fault diagnosis of rotating machinery based on deep learning. In Proceedings of the 2020 International Conference on Aviation Safety and Information Technology, Weihai City, China, 14–16 October 2020. Association for Computing Machinery, 388–392.
https://doi.org/10.1145/3434581.3434730
70. Djalab, A., Nekbil, N., Laouid, A. A., Kouzou, A. and Kadiri, K. An intelligent technique to diagnosis and detection the partial shading based on fuzyy logic for PV system. In Proceedings of the 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), Monastir, Tunisia, 20–23 July 2020. IEEE, 2021, 235–238.
https://doi.org/10.1109/SSD49366.2020.9364109
71. Wang, X., Guo, F. and Xu, W. DGA fuzzy logic diagnostic method based on subordinating function. In Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 12–14 June 2020. IEEE, 1381–1384.
https://doi.org/10.1109/ITOEC49072.2020.9141578
72. Lukichev, D. V., Demidova, G. L. and Brock, S. Fuzzy adaptive PID control for two-mass servo-drive system with elasticity and friction. In Proceedings of the 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24–26 June 2015. IEEE, 443–448.
https://doi.org/10.1109/CYBConf.2015.7175975
73. Zheng, Z., Shao, X. and Yu, D. Fault diagnosis of a wheel loader by artificial neural networks and fuzzy logic. In Proceedings of the 2006 IEEE Conference on Robotics Automation and Mechatronics, Bangkok, Thailand, 1–3 June 2006. IEEE, 1–5.
https://doi.org/10.1109/RAMECH.2006.252704
74. Yu, Y. and Yang, J. The development of fault diagnosis system for diesel engine based on fuzzy logic. In Proceedings of the 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, China, 26–28 July 2011. IEEE, 472–475.
https://doi.org/10.1109/FSKD.2011.6019556
75. Xie, L., Zhou, L., Tong, X.-J. and Chen, M.-Y. Fault diagnosis of power transformer insulation based on fuzzy normal partition and logic reasoning. In Proceedings of the 2007 International Conference on Machine Learning and Cybernetics (ICMLC), Hong Kong, 19–22 August 2007. IEEE, 1081–1085.
https://doi.org/10.1109/ICMLC.2007.4370304
76. Lukichev, D. V., Demidova, G. L. and Brock, S. Comparison of adaptive fuzzy PID and ANFIS controllers for precision positioning of complex object with nonlinear disturbance – study and experiment. In Proceedings of the 2018 20th European Conference on Power Electronics and Applications (EPE’18 ECCE Europe), Riga, Latvia, 17–21 September 2018. IEEE, P.1–P.9.
77. Tayebihaghighi, S. and Koo, I. Fault diagnosis of rotating machine using an indirect observer and machine learning. In Proceedings of the 2020 International Conference on Information amd Communication Technology (ICTC), Jeju, South Korea, 21–23 October 2020. IEEE, 277–282.
https://doi.org/10.1109/ICTC49870.2020.9289590
78. Lim, H., Kim, T. H., Kim, S. and Kang, S. Diagnosis of scan chain faults based-on machine-learning. In Proceedings of the 2020 International SoC Design Conference (ISOCC), Yeosu, South Korea, 21–24 October 2020. IEEE, 2021, 57–58.
https://doi.org/10.1109/ISOCC50952.2020.9333074
79. Gu, J., Luo, Z., Wang, J. and Shen, Y. Research on bearing cross-domain fault diagnosis based on invariant subspace learning with tensor alignment. In Proceedings of the 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), Jinan, China, 23–25 October 2020. IEEE, 461–465.
https://doi.org/10.1109/PHM-Jinan48558.2020.00089
80. Zhang, C., Xu, L., Li, X. and Wang, H. A method of fault diagnosis for rotary equipment based on deep learning. In Proceedings of the 2018 Prognostics and System Health Management Conference (PHM-Chongqing), Chongqing, China, 26–28 October 2018. IEEE, 2019, 958–962.
https://doi.org/10.1109/PHM-Chongqing.2018.00171
81. Shi, W.-W., Yan, H.-S. and Ma, K.-P. A new method of early fault diagnosis based on machine learning. In Proceedings of the 2005 International Conference on Machine Learning and Cybernetics (ICMLC), Guangzhou, China, 18–21 August 2005. IEEE, 3271–3276.
https://doi.org/10.1109/icmlc.2005.1527507
82. Su, C. Q. A new fuzzy logic method for transformer incipient fault diagnosis. In Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, 24–29 July 2016. IEEE, 324–327.
https://doi.org/10.1109/FUZZ-IEEE.2016.7737704
83. Leahy, K., Hu, R. L., Konstantakopoulos, I. C., Spanos, C. J. and Agogino, A. M. Diagnosing wind turbine faults using machine learning techniques applied to operational data. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, Canada, 20–22 June 2016. IEEE, 1–8.
https://doi.org/10.1109/ICPHM.2016.7542860
84. Bangalore, P. and Tjernberg, L. B. An artificial neural network approach for early fault detection of gearbox bearings. IEEE Trans. Smart Grid, 2015, 6(2), 980–987.
https://doi.org/10.1109/TSG.2014.2386305
85. Kudelina, K., Vaimann, T., Asad, B., Rassõlkin, A., Kallaste, A. and Demidova, G. Trends and challenges in intelligent condition monitoring of electrical machines using machine learning. Appl. Sci., 2021, 11(6), 2761.
https://doi.org/10.3390/app11062761
86. Wang, X., Li, L., He, K. and Liu, C. Dual-loop self-learning fuzzy control for AMT gear engagement: design and experiment. IEEE Trans. Fuzzy Syst., 2018, 26(4), 1813–1822.
https://doi.org/10.1109/TFUZZ.2017.2779102
87. Timings, R. L. Newnes Mechanical Engineer’s Pocket Book. Elsevier, 2005.