Modern Approaches to Prognostics and Health Management of an Aircraft Electromechanical Actuator

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Abstract

In connection with implementing the concept of electric airplanes, it is necessary to ensure the high reliability of electromechanical actuators (EMAs) as important components of aviation systems. The structural composition of an EMA and the types of its faults are considered. Fault diagnosis methods based either on EMA modeling or the analysis of signals received during EMA operation, as well as hybrid methods combining both these approaches, are reviewed. The advantages, disadvantages, and difficulties in applying these approaches are investigated. Special attention is paid to EMA diagnosis methods based on deep learning, which process signals in automatic mode and implement complex fault diagnosis. The concept of aircraft equipment health management (in particular, EMA health management) is presented based on assessing the technical condition and prognosticating the remaining useful life in order to prevent faults before their occurrence. Several hybrid approaches with prognostics are highlighted to solve the aircraft health management problem. Finally, Russian R&D results in the field of machine learning-based aviation health management are reviewed.

About the authors

E. L Kulida

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: elena-kulida@yandex.ru
Moscow, Russia

V. G Lebedev

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: lebedev-valentin@yandex.ru
Moscow, Russia

References

  1. Garcia Garriga, A., Ponnusamy, S. S., Mainini, L. A Multi-fidelity Framework to Support the Design of More-Electric Actuation // AIAA Aviation Forum. Multidisciplinary Analysis and Optimization Conference. – Atlanta, 2018. – doi: 10.2514/6.2018-3741.
  2. Adu-Gyamfi, B.A., Good, C. Electric aviation: a Review of Concepts and Enabling Technologies // Transportation Engineering. – 2022. – Vol. 9, no. 4 – doi: 10.1016/j.treng. 2022.100134.
  3. Baldo, L., Querques, I., Dalla Vedova, M.D.L., et al. A Model-Based Prognostic Framework for Electromechanical Actuators Based on Metaheuristic Algorithms // Aerospace. – 2023. – Vol. 10, no. 3. – doi: 10.3390/aerospace10030293.
  4. Li, J., Yu, Z., Huang, Y., Li, Z. A Review of Electromechanical Actuation System for More Electric Aircraft // Proceedings of the IEEE International Conference on Aircraft Utility Systems (AUS). – Beijing, 2016. – P. 490–497.
  5. Annaz, F.Y., Kaluarachchi, M.M. Progress in Redundant Electromechanical Actuators for Aerospace Applications // Aerospace. – 2023. – Vol. 10(9). – doi: 10.3390/aerospace10090787.
  6. Fu, S., Avdelidis, N.P. Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview // Sensors. – 2023. – Vol. 23, no. 19. – doi: 10.3390/s23198124.
  7. Bertolino, A.C., De Martin, A., Jacazio, G., Sorli, M. Towards a PHM System for Electromechanical Flight Control Actuators // Proceedings of I-RIM 2020. – Torino, 2020. – doi: 10.5281/zenodo.4781001.
  8. Bertolino, A.C., De Martin, A., Jacazio, G., Sorli, M. Design and Preliminary Performance Assessment of a PHM System for Electromechanical Flight Control Actuators // Aerospace. – 2023. – Vol. 10. – DOI: 10.3390/ aerospace10040335.
  9. Baldo L., Berri P.C., Matteo D. L., et al. Experimental Validation of Multi-fidelity Models for Prognostics of Electromechanical Actuators // Proceedings of the 7th European Conference of the Prognostics and Health Management Society. – Torino, 2022. – Vol. 7, no.1. – P. 32–42. – DOI: 10.36001.phme.2022.v7i1.3347.
  10. Cai, B., Zhao Y., Liu, H., Xie, M. A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems // IEEE Transaction on Power Electronics. – 2017. – Vol. 32, no. 7. – P. 5590–5600.
  11. Sudhawiyangkul, T., Isarakorn, D. Design and Realization of an Energy Autonomous Wireless Sensor System for Ball Screw Fault Diagnosis // Sensors and Actuators A Physical. – 2017. – Vol. 258. – P. 49–58.
  12. Chirico, A., Kolodziej, J.R. Fault Detection and Isolation for Electro-Mechanical Actuators Using a Data-Driven Bayesian Classification // SAE International Journal of Aerospace. – 2012. – Vol. 5, no. 2. – P. 494–502.
  13. Chirico, A.J., Kolodziej, J.R. A Data-driven Methodology for Fault Detection in Electromechanical Actuators // Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME. – 2014. – Vol. 136, no. 4. – doi: 10.1115/1.4026835.
  14. Song, Y., Du, J., Li, S. Multi-Scale Feature Fusion Convolutional Neural Networks for Fault Diagnosis of Electromechanical Actuator // Applied Sciences. – 2023. – Vol. 13, no. 15. – Art. no. 8689. – doi: 10.3390/app13158689.
  15. Wang, J., Zhang, Y., Luo, C., Miao, Q. Deep Learning Domain Adaptation for Electro-Mechanical Actuator Fault Diagnosis Under Variable Driving Waveforms // IEEE Sensors Journal. – 2022. – Vol. 22, no. 11. – P. 10783–10793.
  16. Derrien, J.-C., Securite, S.D. Electromechanical Actuator (EMA) Advanced Technologies for Flight Controls // 28-th International Congress of the Aeronautical Sciences (ICAS 2012). – Brisbane, 2012. – URL: https://icas.org/ICAS_ARCHIVE/ICAS2012/PAPERS/291.PDF.
  17. Qiao, G. Liu, G. Shi, Z., et al. A Review of Electromechanical Actuators for More/All Electric Aircraft Systems // Proceedings of Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science. – 2018. – Vol. 232, no. 22. – P. 4128–4151. – doi: 10.1177/0954406217749869.
  18. Yin, Z, Hu, N., Chen, J., et al. A Review of Fault Diagnosis, Prognosis and Health Management for Aircraft Electromechanical Actuators // IET Electronic Power Applications. – 2022. – Vol. 16, no. 11. – P. 1249–1272. – doi: 10.1049/elp2.12225.
  19. Jian, F., Mare, J.-C., Yongling, F. Modelling and Simulation of Flight Control Electromechanical Actuators with Special Focus on Model Architecting, Multidisciplinary Effects and Power Flows // Chinese Journal of Aeronautics. – 2017. – No. 30 (1). – P. 47–65.
  20. Cui, Z., Jing, B., Jiao, X., et al. The Integrated-Servo-Actuator Degradation Prognosis Based on the Physical Model Combined With Data-Driven Approach // IEEE Sensors Journal. – 2023. – Vol. 23, no. 9. – P. 9370–9381. – doi: 10.1109/JSEN.2023.3248323.
  21. Randall, R.B. The Use of Simulation Models to Generate Data Corresponding to Faults in Machines // 8th International Conference on Reliability, Maintainability and Safety (ICRMS-2009). – Chendgu, 2009. – doi: 10.1109/ICRMS.2009.5269971.
  22. Mazzoleni, M., Di Rito, G., Previdi, F. Electromechanical Actuators for the More Electric Aircraft. – Cham: Springer, 2021. – 239 p. – doi: 10.1007/978-3-030-61799-8.
  23. Hopf, K., Reifenrath, S. Filter Methods for Feature Selection in Supervised Machine Learning Applications – Review and Benchmark // Cornell University. – arXiv: 2111.12140v1[cs.LG]. – 2021. – doi: 10.48550/arXiv2111.12140.
  24. Chirico, A.J., Kolodziej, J.R., Hall, L. A Data Driven Frequency Based Feature Extraction and Classification Method for EMA Fault Detection and Isolation // 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. – Houston, 2012. – doi: 10.1115/DSCC2012-MOVIC2012-8749.
  25. Jing, J., Liu, H., Lu, C. Fault Diagnosis of the Aircraft Electromechanical Actuation System Based on WPD-STFT Time Frequency Entropy and PNN // Vibroengineering Procedia. – Oct. 2017. – Vol. 14. – doi: 10.21595/vp.2017.19247.
  26. Li, J., Meng, G., Xie, G., Jia, Y. Sensor Fault Diagnosis Based on Wavelet Packet and SOM Neural Network // Chinese Journal of Sensors and Actuators. – 2017. – Vol. 30, no. 7. – P. 1035–1039. – DOI: 103969/j.issn.1004-1699.2017.07.011.
  27. Ruiz-Cárcel, C., Starr, A. Data-Based Detection and Diagnosis of Faults in Linear Actuators // IEEE Transactions on Instrumentation and Measurement. – 2018. – Vol. 67. – Iss. 9. – P. 2035–2047. – doi: 10.1109/TIM.2018.2814067.
  28. Narasimhan, S., Roychoudhury, I., Balaban, E., Saxena, A. Combining Model-Based and Feature-Driven Diagnosis Approaches-A Case Study on Electromechanical Actuators // Proceeding of the 21-st International Workshop on Principles of Diagnosis Held Jointly with Annual Conference of the PHM Society. – Portland, 2010. – Vol. 2, no. 2. – doi: 10.36001/phmconf.2010.v2i2.1936.
  29. Wang, C., Ding, Y., Ma, J. An Adversarial Model for Electromechanical Actuator Fault Diagnosis under Nonideal Data Conditions // Neural Computing and Applications. – 2022. – Vol. 34, no. 1115. – P. 5883–5904. – doi: 10.1007/s00521-021-06732-x.
  30. Yang, J., Guo, Y., Zhao, W. Long Short‐Term Memory Neural Network Based Fault Detection and Isolation for Electromechanical Actuators // Neurocomputing. – 2019. – Vol. 360. – P. 85–96. – doi: 10.1016/j.neucom.2019.06.029.
  31. Yang, N., Shen, J., Jia, Y., Zhang, J. Fault Diagnosis of Electromechanical Actuator Based on Deep Learning Network // 39th Chinese Control Conference (CCC). – Shenyang, 2020. – P. 4002–4006. – doi: 10.23919/CCC50068.2020.9189666.
  32. Riaz, N., Shah, S.I.A., Rehman, F., et al. A Novel 2-D Current Signal-based Residual Learning with Optimized Softmax to Identify Faults in Ball Screw Actuators // IEEE Access. – 2020. – Vol. 8. – P. 115 299–115 313. – doi: 10.1109/access.2020.3004489.
  33. Siahpour, S., Li, X., Lee, J. Deep Learning-based Cross-sensor Domain Adaptation for Fault Diagnosis of Electromechanical Actuators // International Journal of Dynamics and Control. – 2020. – Vol. 8. – P. 1054–1062. – doi: 10.1007/s40435-020-00669-0.
  34. Ding, Y., Ma, L., Ma, J., et al. A Generative Adversarial Network-based Intelligent Fault Diagnosis Method for Rotating Machinery under Small Sample Size Conditions // IEEE Access. – Vol. 7. – P. 149 736–149 749. – doi: 10.1109/ACCESS.2019.2947194.
  35. Wang, Y., Sun, G., Jin, Q. Imbalanced Sample Fault Diagnosis of Rotating Machinery Using Conditional Variational Auto-encoder Generative Adversarial Network // Applied Soft Computing Journal. – 2020. – Vol. 92, no. 7. – Art. no. 106333. – doi: 10.1016/j.asoc.2020.106333.
  36. Zhang, L., Gao, H., Wen, J., et al. A Deep Learning‐Based Recognition Method for Degradation Monitoring of Ball Screw with Multi‐sensor Data Fusion // Microelectronics Reliability. – 2017. – Vol. 75. – P. 215–222. – doi: 10.1016/j.microrel.2017.03.038.
  37. Yang, J., Guo, Y., Wanti, Z. An Intelligent Fault Diagnosis Method for an Electromechanical Actuator Based on Sparse Feature and Long Short‐Term Network // Measurement Science Technology. – 2021. – Vol. 32, no. 9. – doi: 10.1088/1361-6501/abfbab.
  38. Zhang, X., Tang, L., Chen, J. Fault Diagnosis for Electro‐Mechanical Actuators Based on STL‐HSTA‐GRU and SM // IEEE Transaction on Instrumentation and Measurement. – 2021. – Vol. 70. – doi: 10.1109/tim.2021.3127641.
  39. Tao, X., Guo, Y., Zhao, W., et al. Fault Detection and Isolation of Electromechanical Actuator Based on SAE-BiLSTM // The 13th Asia Conference on Mechanical and Aerospace Engineering (ACMAE 2022). Journal of Physics: Conference Series. – 2023. – Vol. 2472. – Art. no. 012031. – doi: 10.1088/1742-6596/2472/1/012031.
  40. Zhang, W., Peng, G., Li, C. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals // Sensors. – 2017. – Vol. 17, no. 3. – doi: 10.3390/s17020425.
  41. Jiang, G., He, H., Yan, J., Xie, P. Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox // IEEE Transaction on Industrial Electronics. – 2018. – Vol. 66, no 4. – P. 3196–3207. – doi: 10.1109/TIE.2018.2844805.
  42. Kordestani, M., Orchard, M.E., Khorasani, K., Saif, M. An Overview of the State of the Art in Aircraft Prognostic and Health Management Strategies // IEEE Transactions on Instrumentation and Measurement. – 2023. – Vol. 72. – Art. no. 3505215. – doi: 10.1109/TIM.2023.3236342.
  43. Rezaeianjouybari, B., Shang, Y. Deep Learning for Prognostics and Health Management: State of the Art, Challenges, and Opportunities // Measurement. – 2020. – Vol. 163, no. 4. – doi: 10.1016/j.measurement.2020.107929.
  44. Lei, Y., Li, N., Guo, L., et al. Machinery Health Prognostics: a Systematic Review from Data Acquisition to RUL Prediction // Mechanical Systems and Signal Processing. – 2018. – Vol. 104. – P. 799–834. – DOI: 10.1016.j.ymssp.2017.11.016.
  45. Berri, P.C., Dalla Vedova, M.D.L., Mainini, L. Computational Framework for Real-time Diagnostics and Prognostics of Aircraft Actuation Systems // Computers in Industry. – 2021. – Vol. 132. – doi: 10.1016/compind.2021.103523.
  46. Stanton, I., Munir, K., Ikram, A. Predictive Maintenance Analytics and Implementation for Aircraft: Challenges and Opportunities // System Engineering. – 2022. – Vol. 26, no. 137. – doi: 10.1002/sys.21651.
  47. Scott, M.J., Verhagen, W.J.C., Bier, M.T., Marzocca, P. A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations // Sensors. – 2022. – Vol. 22, no. 18. – doi: 10.3390/s22187070.
  48. Ismail, M.A.A., Balaban, E., Windelberg, J. Spall Fault Quantification Method for Flight Control Electromechanical Actuator // Actuators. – 2022. – Vol. 11, no. 29. – doi: 10.3390/act11020029.
  49. Belmonte, D., Dalla Vedova, M.D.L., Maggiore, P. Prognostics of Onboard Electromechanical Actuators: a New Approach Based on Spectral Analysis Techniques // International Review of Aerospace Engineering. – 2018. – Vol. 11, no. 3. – doi: 10.15866/irease.v11i3.13796.
  50. Zhang, J., Tian, J., Li, M., et al. A Parallel Hybrid Neural Network with Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics // IEEE Transaction on Instrumentation and Measurement. – 2023. – Vol. 72. – doi: 10.1109/TIM.2022.3227956.
  51. Dangut, M. D., Jennions, I.K., King, S., Skaf, Z. A Rare Failure Detection Model for Aircraft Predictive Maintenance Using a Deep Hybrid Learning Approach // Neural Computing and Applications. – 2023. – Vol. 35, no. 4. – P. 2991–3009. – doi: 10.1007/s00521-022-07167-8.
  52. Hasib, A.A., Rahman, A., Khabir, M., Shawon, M.T. An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine // Cornell University. – arXiv:2309.13310v1[cs.LG]. – 2023. – doi: 10.48550/arXiv.2309.13310.
  53. Khan, K., Sohaib, M., Rashid, A. Recent Trends and Challenges in Predictive Maintenance of Aircraft’s Engine and Hydraulic System // Journal of the Brazilian Society of Mechanical Sciences and Engineering. – 2021. – Vol. 43, no. 8. – doi: 10.1007/s40430-021-03121-2.
  54. Boujamza, A., Lissane Elhaq, S. Attention-Based LSTM for Remaining Useful Life Estimation of Aircraft Engines // IFACPapersOnLine. – 2022. – Vol. 55, no. 12. – P. 450–455. – doi: 10.1016/j.ifacol.2022.07.353.
  55. Wang, C., Zhu, Z., Lu, N. A Data-Driven Degradation Prognostic Strategy for Aero-Engine under Various Operational Conditions // Neurocomputing. – 2021. – Vol. 462, no. 3. – P. 195–207. – doi: 10.1016/j.neucom.2021.07.080.
  56. Chao, M.A., Kulkarni, C., Goebel, K., Fink, O. Fusing Physics-based and Deep Learning Models for Prognostics // Reliability Engineering and System Safety. – 2022. – Vol. 217, no. 3. – doi: 10.1016/j.ress.2021.107961.
  57. Aimasso, A., Berri, P.C., Dalla Vedova, M.D. A Genetic-based Prognostic Method for Aerospace Electromechanical Actuators. International Journal of Mechanics and Control. – 2021. – Vol. 22, no. 2. – P. 195–206.
  58. Baldo, I., Vedova, M.D.L., Querques, I., Magglore, P. Prognostics of Aerospace Electromechanical Actuators: Comparison between Model-based Meta-heuristic Methods // Journal of Physics: Conference Series. – 2023. – Vol. 2526, no. 1. – Art. no. 012073. – doi: 10.1088/1743-6596/1/012073.
  59. Dalla Vedova, M.D.L., Berri, P.C., Re, S. A Comparison of Bio-inspired Meta-hevristic Algorithms for Aircraft Actuator Prognostics // 29th European Safety and Reliability Conference. – Hannover, – 2019. – doi: 10.3850/978-981-11-2724-3_0476-cd.
  60. Berri, P.C., Dalla Vedova, M.D., Maggiore, P. A Lumped Parameter High Fidelity EMA Model for Model-Based Prognostics // 29th European Safety and Reliability Conference. – Hannover, 2019. – P. 1086–1093. doi: 10.3850/978-981-11-2724-3_0480-cd.
  61. Berri, P.C., Dalla Vedova, M.D.L., Maggiore, P., Viglione, F. A Simplified Monitoring Model for PMSM Servoactuator Prognostics // Proceedings of the MATEC Web of Conferences. – Osaka, 2018. – doi: 10.1051/matecconf/201930404013.
  62. Randall, R.B. The Use of Simulation Models to Generate Data Corresponding to Faults in Machines // 8th International Conference on Reliability, Maintainability and Safety (ICRMS-2009). – Chengdu, 2009. – doi: 10.1109/ICRMS.2009.5269971.
  63. Ahmad, M.F., Isa, N.A.M., Lim, W.H., Ang, K.M. Differential Evolution: A Recent Review Based on State-of-the-Art Works // Alexandria Engineering Journal. – 2022. – Vol. 61, no. 5. – P. 3831–3872. – doi: 10.1016/j.aej.2021.09.013.
  64. Kennedy, J., Eberhart, R. Particle Swarm Optimization // Proceedings of the IEEE International Conference on Neural Networks. – Perth, 1995. – P. 1942–1948. – doi: 10.1109/ICNN.1995.488968.
  65. Darwish, A. Bio-inspired Computing: Algorithms Review, Deep Analysis, and the Scope of Applications // Future Computing and Informatics Journal. – 2018. – Vol. 3, no. 2. – P. 231–246. – doi: 10.1016/j.fcij.2018.06.001.
  66. Mirjalili, S., Mirjalili, S.M., Lewis, A. Grey Wolf Optimizer // Advances in Engineering. – 2014. – Vol. 69. – P. 46–61. – doi: 10.1016/j.advengsoft.2013.12.007.
  67. Kumar, V., Kumar, D. An Astrophysics-inspired Grey Wolf Algorithm for Numerical Optimization and its Application to Engineering Design Problems // Advances in Engineering Software. – 2017. – Vol. 112. – P. 231–254. – doi: 10.1016/advengsoft.2017.05.008.
  68. Егоров И.В., Соколов М.П. Сравнение эффективности нейросетевых алгоритмов с методами факторного анализа при диагностировании технического состояния ГТД // Научный вестник МГТУ ГА. Серия «Эксплуатация воздушного транспорта и ремонт авиационной техники». – 2007. – № 123. – С. 89–95. [Egorov, I.V., Sokolov, M.P. Sravnenie efektivnosti neirosetevykh algoritmov s metodami faktopnogo analiza pri diagnostirovanii tekhnicheskogo sostoyaniya // Nauchnyj vestnik MGTU GA. Seriya «Ekspluatatsiya vozdushnogo transporta I remont aviatsionnoj tekhniki». – 2007. – No. 123. – P. 89–95. (In Russian)].
  69. Соколов М.П., Земсков А.А., Куц М.С. Тренды технического диагностирования силовых установок и трансмиссий воздушных судов // Транспортное, горное и строительное машиностроение: наука и производство. – 2022. – № 15. – С. 37–46. [Sokolov, M.P., Zemskov, A.A., Kuts, M.S. Trendy tekhnicheskogo diagnostirovaniya silovykh ustanovok i transmissij vozdushnykh sudov // Transportnoe, gornoe i stroitel’noe mashinostroenie: nauka i proizvodstvo. – 2022. – No. 15. – P. 37–46. (In Russian)]
  70. Mikhailov, A., Karavay, M., Farhadov, M. Inverse Sets in Big Data Processing // Proceedings of the 11th IEEE International Conference on Application of Information and Communication Technologies (AICT2017). – Мoscow, 2017. – Vol. 1. – Р. 40–43.
  71. Сальников С.В., Солодкий Е.М., Вишняков Д.Д. и др. Диагностика асинхронного двигателя на основе машинного обучения // Международная конференция по мягким вычислениям и измерениям. – Санкт-Петербург, 2023. – Т. 1. – С. 295–300. [Sal’nikov, S.V., Solodkij, E.M., Vishnyakov, D.D., et al. Diagnostika asinkhronnogo dvigatelya na osnove mashinnogo obucheniya //Mezhdunarodnaya konferentsiya po myagkim vychisleniyam I izmereniyam. – Saint Petersburg, 2023. – Vol. 1. – P. 295–300. (In Russian)]
  72. Козлов Д.С., Тюменцев Ю.В. Нейросетевые методы обнаружения отказов датчиков и приводов летательного аппарата // Труды МАИ. – 2012. – Вып. 52. – C. 2. [Kozlov, D.S., Tiumentsev, Y.V. Nejrosetevye metody obnaruzheniya otkazov datchikov i privodov letatel’nogo apparata // Trudy MAI. – 2012. – Vol. 52. – P. 2. (In Russian)]
  73. Erofeev, E., Khaletskiy, L., Skryabin, A., Steblinkin, A. Methodologies and test-rig configurations for the experimental improvement of flight control actuation systems // Recent Advances in Aerospace Actuation Systems and Component. Conference proceedings. – Toulouse, 2018. – P. 109–116.
  74. Вересников Г.С., Скрябин А.В., Гуцевич Д.Е. Разработка математической модели для исследования алгоритмов оценки и прогноза технического состояния сервопривода БЛА // Известия ЮФУ. Технические науки. – 2019. – № 7. – С. 170–181. [Veresnikov, G.S., Skryabin, A.V., Gutsevich, D.E. Razrabotka matematicheskoj modeli dlya issledovaniya algoritmov otsenki I prognoza tekhnicheskogo sostoyaniya servoprivoda BLA // Izvestiya YUFU. Tekhnicheskie nauki. – 2019. – No. 7. – P. 170–181. (In Russian)]
  75. Veresnikov, G.S., Skryabin, A.V., Bazhenov, S.G. The Development of Algorithms for EMA Fault Early Detection System // Proceedings of the 32th Congress of the International Council of the Aeronautical Sciences. – Shanghai, 2020. – URL: https://www.icas.org/ICAS_ARCHIVE/ICAS2020/data/preview/ICAS2020_0239.htm.
  76. Veresnikov, G.S., Lebedev, V.G., Ogorodnikov, O.V., Golev, A.V. Fault Detection Technique for Electromechanical Actuator of the Aircraft Using Neural Networks // Lecture Notes in Mechanical Engineering. – Cham: Springer, 2021. – P. 519–528.
  77. Вересников Г.С., Скрябин А.В. Алгоритмы поиска информативных признаков для прогнозирования технического состояния электромеханического привода // Тр. 14-й международной конференции «Управление развитием крупномасштабных систем» (MLSD-2021). – Москва, 2021. – С. 1389–1397. [Veresnikov, G.S., Skryabin, A.V. Feature Selection Algorithms for Forecasting Technical Condition of Electromechanical Actuator // Proceedings of the 14th International Conference «Management of Large-Scale System Development» (MLSD). – Мoscow, 2021. – URL: https://ieeexplore.ieee.org/document/9600182/.
  78. Вересников Г.С., Скрябин А.В. Диагностика неисправностей смешанного типа в редукторе электромеханического привода летательного аппарата с использованием нейронных сетей // Тр. 15-й Международной конференции «Управление развитием крупномасштабных систем» (MLSD-2022). – Москва, 2022. – С. 1116–1122. [Veresnikov, G.S., Skryabin, A.V. Diagnostics of Mixed Type Failures in the Aircraft Electromechanical Actuator Gear by Using Neural Networks // Proceedings of the 15th International Conference Management of Large-Scale System Development (MLSD). – Мoscow, 2022. – URL: https://ieeexplore.ieee.org/document/9600182.

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Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

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2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».