Prospective Approaches to Predicting the Remaining Useful Life of Aircraft Engines

Cover Page

Cite item

Full Text

Abstract

This survey covers the literature on the fault diagnosis and prediction of the remaining useful life of aircraft engines based on deep learning. A formal statement of the remaining useful life estimation problem is given. The basic architectures of deep neural networks are considered to detect rare failures and predict the next failures using aircraft engine condition monitoring data. The extraction of informative features using autoencoders is discussed. The structure of long short-term memory (LSTM) and attention mechanism (AM) cells applied in deep neural networks to predict the remaining useful life is described. The problem of integrating remaining useful life prediction into maintenance planning based on reinforcement learning is considered.

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. Fentaye, D., Zaccaria, V., Kyprianidis, K. Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks // Machines. – 2021. – Vol. 9, no. 12. – Art. no. 337. – doi: 10.3390/machines9120337
  2. 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 Transaction on Instrumentation and Measurement. – 2023. – Vol. 72. – P. 1–15. – doi: 10.1109/TIM.2023.3236342
  3. Upadhyay, R., Amhia, H. LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines // ITM Web of Conferences. – 2023. – Vol. 57. – Art. no. 03004. – doi: 10.1051/itmconf/20235703004
  4. Stanton, I., Munir, K., Ikram, A., El-Bakry, M. Predictive Maintenance Analytics and Implementation for Aircraft: Challenges and Opportunities // Systems Engineering. – 2023. – Vol. 26, iss. 2. – Р. 216–237. – doi: 10.1002/sys.21651
  5. Кулида Е.Л., Лебедев В.Г. Прогнозирование технического обслуживания авиационных двигателей на основе глубокого обучения // Материалы XIV Всероссийского совещания по проблемам управления (ВСПУ-2024). – Москва, 2024. – С. 2502–2506.
  6. Сай В.К., Щербаков М.В. Прогнозирование отказов сложных многообъектных систем на основе комбинации нейросетей: пути повышения точности прогнозирования // Прикаспийский журнал: управление и высокие технологии. – 2020 – № 1 (49). – С. 49–60. – doi: 10.21672/2074–1707.2020.49.4.049–060
  7. Scott, M.J., Verhagen, W.J.C., Bieber, 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. – Art. no. 7070. – doi: 10.3390/s22187070
  8. Khalid, S., Song, J., Azad, M.M. A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management // Mathematics. – 2023. – Vol. 11, no. 18. – Art. no. 3837. – doi: 10.3390/math11183837
  9. Naderi, E., Meskin N., Khorasani, K. Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model–based Approach // J. Eng. Gas Turbines Power. – 2012. – Vol. 13, no. 1. – doi: 10.1115/GT2010-23442
  10. Amirkhani, S., Tootchi, A., Chaibakhsh, A. Fault Detection and Isolation of Gas Turbine Using Series–parallel NARX Model // ISA Transactions. – 2022. – Vol. 120. – P. 205–221. – doi: 10.1016/j.isatra.2021.03.019
  11. Gharoun, H., Keramati, A., Nasiri, M., Azadeh, A. An Integrated Approach for Aircraft Turbofan Engine Fault Detection Based on Data Mining Techniques // Expert system. – 2021. – Vol. 36, no. 4. – doi: 10.1111/exsy.12370
  12. Gharoun, H., Hamid, M., Ghaderi, S.F., Nasiri, M. Anomaly Detection via Data Techniques for Aircraft Engine Operation Monitoring // Proceedings of 4th International Industrial Engineering Conference (IIEC 2018). – Tehran, Iran, 2018. – P. 1–15.
  13. Garcia, C.E., Camana, M.R., Koo, I. Machine Learning–based Scheme for Multi-class Fault Detection in Turbine Engine Disks // ICT Express. – 2021. – Vol. 7, iss. 1. – P. 15–22. – doi: 10.1016/j.icte.2021.01.009
  14. Li, Z, Goebel, K., Wu, D. Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning // Journal of Engineering for Gas Turbines and Power. – 2018. – Vol. 141, no. 4. – doi: 10.1115/1.4041674
  15. Celikmih, K., Inan, O., Uguz, H., et al. Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method // Scientific Programming. – 2020. – Vol. 10. – doi: 10.1155/2020/8616039.
  16. Costa, N., Sánchez, L. Variational Encoding Approach for Interpretable Assessment of Remaining Useful Life Estimation // Reliability Engineering and System Safety. – 2022. – Vol. 222, no. 1. – doi: 10.1016/j.ress.2022.108353
  17. Abdullah, T.A.A., Zahid, M.S.M., Turki, A.F., et al. Sig-LIME: a Signal-Based Enhancement of LIME Explanation Technique // IEEE Access. – 2024. – Vol. 12. – P. 52641-52658. – doi: 10.1109/ACCESS.2024.3384277
  18. Gao, J., Wang, Y., Sun, Z. An Interpretable RUL Prediction Method of Aircraft Engines Under Complex Operating Conditions Using Spatio–temporal Features // Measurement Science and Technology. – 2024. – Vol. 35, no. 7. – doi: 10.1088/1361–6501/ad3b2c
  19. 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/ress.2021.107961
  20. Adhikari, P., Rao, H.G., Buderath, M. Machine Learning Based Data Driven Diagnostics & Prognostics Framework for Aircraft Predictive Maintenance // Proceedings of the 10th International Symposium on NDT in Aerospace. – Dresden, Germany, 2018. – P. 24–26.
  21. Graves, A., Schmidhuber, J. Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures // Neural Networks. – 2005. – Vol. 18, no. 5–6. – P. 602–610. – doi: 10.1016/j.neunet.2005.06.042
  22. Graves, A., Jaitly, N., Mohamed, A.R. Hybrid Speech Recognition with Deep Bidirectional LSTM // Proceedings of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding. – Olomouc, Czech Republic, 2014. – P. 273–278. – doi: 10.1109/ASRU.2013.6707742
  23. Kefalas, M., Baratchi, M., Apostolidis, A., et al. Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines // Proceedings of the IEEE International Conference on Prognostics and Health Management (ICPHM). – Detroit, USA, 2021. – doi: 10.1109/ICPHM51084.2021.9486549
  24. Liu, L., Song, X., Zhou, Z. Aircraft Engine Remaining Useful Life Estimation Via a Double Attention-based Data-driven // Reliability Engineering and System Safety. – 2022. – Vol. 221, no. 3. – doi: 10.1016/j.ress2022.108330
  25. Сай В.К. Глубокие нейронные сети для предсказательного технического обслуживания // Моделирование, оптимизация и информационные технологии. – 2019. – Т. 7, № 4. – doi: 10.26102/2310-6018/2019.27.4.011
  26. Xia, J, Feng, Y., Lu, C., et al. LSTM-Based Multi-layer Self-attention Method for Remaining Useful Life Estimation of Mechanical Systems // Engineering Failure Analysis. – 2021. – Vol. 125, no. 12. – doi: 10.1016/j.engfainal.2021.105385
  27. Dangut, M.D., Skaf, Z., Jennions, I.K. Rare Failure Prediction Using an Integrated Auto–encoder and Bidirectional Gated Recurrent Unit Network // IFAC-PapersOnLine. – 2020. – Vol. 53, iss. 4. – P. 276–282.
  28. 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
  29. Сыпало, К.И., Пономарев, А.К., Ахатов, И.Ш. Перспективные технологии для авиационной промышленности: Аналитический обзор. – М.: НАУКА, 2017. – 463 с.
  30. Badea, V.E., Zamfiroiu, A., Boncea, R. Big Data in the Aerospace Industry // Informatica Economica. – 2018. – Vol. 22, no. 1. – P. 17–24. – doi: 10.12948/issn14531305/1.2018.02
  31. Zhao, Y., Wang, Y. Remaining Useful Life Prediction for Multi-sensor Systems Using a Novel End-to-End Deep-learning Method // Measurement. – 2021. – Vol. 182, no. 163. – doi: 10.1016/j.measurement.2021.109685
  32. Frederick, D.K., DeCastro, J.A., Litt, J.S. User's Guide for the Commercial Modular Aero-propulsion System Simulation (C-MAPSS). Report no. NASA/TM-2007-215026. – Cleveland: National Aeronautics and Space Administration, 2007. – 47 p.
  33. Saxena, A., Goebel, K., Simon, D., Eklund, N. Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation // Proceedings of International Conference on Prognostics and Health Management. – Denver, USA, 2008. – doi: 10.1109/PHM.2008.4711414
  34. Song, Y., Bliek, L., Xia, T., Zhang, Y. A Temporal Pyramid Pooling-Based Convolutional Neural Network for Remaining Useful Life Prediction // Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021). – P. 603–609. – doi: 10.3850/978–981-18-2016-8_478-cd
  35. Chao, M.A., Kulkarni, C., Goebel, K., Fink, O. Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics // Data. – 2021. – Vol. 6 (1), no. 5. – doi: 10.3390/data6010005
  36. Azyus, A.F. Determining the Method of Predictive Maintenance for Aircraft Engine Using Machine Learning // Journal of Computer Science and Technology Studies. – 2022. – Vol. 4, no. 1. – doi: 10.32996/jcsts.2022.4.1.1
  37. Hasib, A.A., Rahman, A., Khabir, M., Shawon, M.T.R. An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine // arXiv. – 2023. – arXiv:2309.13310v1. – DOI: https://doi.org/10.48550/arXiv.2309.13310
  38. Liu, T., Bao, J., Wang, J., Wang, J. Deep Learning for Industrial Image: Challenges, Methods for Enriching the Sample Space and Restricting the Hypothesis Space, and Possible Issue // International Journal of Computer Integrated Manufacturing. – 2022. – Vol. 35, iss. 10–11. – P. 1077–1106. – doi: 10.1080/0951192X.2021.1901319
  39. Hinton, G.E., Salakhutdinov, R.R. Reducing the Dimensionality of Data with Neural Networks // Science. – 2006. – Vol. 313. – P. 504–507. – doi: 10.1126/science.1127647
  40. Fu, S., Zhong, S., Lin, L., Zhao, M. A Re-optimized Deep Auto-encoder for Gas Turbine Unsupervised Anomaly Detection // Engineering Applications of Artificial Intelligence. – 2021. – Vol. 101, no. 12. – doi: 10.1016/j.engappi.2021.104199
  41. Babu, G.S., Zhao, P., Li, X. Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life // Proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA). – Dallas, USA, 2016. – Vol. 9642. – P. 214–228. – doi: 10.1007/978-3-319-32025-0_14
  42. Li, X., Ding, Q., Sun, J.Q. Remaining Useful Life Estimation in Prognostics Using Deep Convolution Neural Networks // Reliability Engineering and System Safety. – 2018. – Vol. 172, no. 1-2. – doi: 10.1016/j.ress.2017.11.021
  43. Li, H., Zhao, W., Zhang, Y., Zio, E. Remaining Useful Life Prediction Using Multiscale Deep Convolutional Neural Network // Applied Soft Computing. – 2020. – Vol. 89. – doi: 10.1016/j.asoc.2020.106113
  44. Абдуракипов С.С., Бутаков Е.Б. Сравнительный анализ алгоритмов машинного обучения для определения предотказных и аварийных состояний авиадвигателей // Автометрия. – 2020. – Т. 56, № 6. – С. 34–48. – doi: 10.15372/AUT20200605
  45. Schuster, M., Paliwal, K.K. Bidirectional Recurrent Neural Networks // IEEE Transactions on Signal Processing. – 1997. – Vol. 45, no. 11. – P. 2673–2681. – doi: 10.1109/78.650093
  46. Hu, K., Cheng, Y., Wu, J., et al. Deep Bidirectional Recurrent Neural Networks Ensemble for Remaining Useful Life Prediction of Aircraft Engine // IEEE Transactions on Cybernetics. – 2021. – Vol. 53, no. 4. – P. 2531–2543. – doi: 10.1109/TCYB.2021.3124838
  47. Hochreiter, S., Schmidhuber, J. Long Short-term Memory // Neural Computation. – 1997. – Vol. 9, no. 8. – P. 1735–1780. – doi: 10.1162/neco.1997.9.8.1735
  48. Wang, J., Wen, G., Yang, S., Liu, Y. Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network // Proceedings of the 2018 Prognostics and System Health Management Conference (PHM-Chongqing). -Chongqing, China, 2018. – P. 1037–1042. – doi: 10.1109/PHM-Chongqing.2018.00184
  49. Khan, K., Sohaib, M., Rashid, A., et al. 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. – P. 1–17. – doi: 10.1007/s40430-021-03121-2
  50. Wu, Q., Ding, K., Huang, B. Approach for Fault Prognosis Using Recurrent Neural Network // Journal of Intelligent Manufacturing. – 2020. – Vol. 31, no. 3. – P. 1621–1633. – doi: 10.1007/s10845-018-1428-5
  51. Peng, C., Chen, Y., Chen, Q., et al. A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion // Sensors. – 2021. – Vol. 21, no. 2. – doi: 10.3390/s21020418
  52. da Rosa, T.G., de Melani, A.H.A., Pereira, F.H., et al. Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis // Sensors. – 2022. – Vol. 22, no. 24. – doi: 10.3390/s22249738
  53. Peng, C., Wu, J., Wang, Q. Remaining Useful Life Prediction Using Dual-Channel LSTM with Time Feature and Its Difference // Entropy. – 2022. – Vol. 24, no. 12. – doi: 10.3390/e24121818
  54. Wang, X., Huang, T., Zhu, K., Zhao, X. LSTM-Based Broad Learning System for Remaining Useful Life Prediction // Mathematics. – 2022. – Vol. 10, no. 12. – doi: 10.3390/math10122066
  55. Azyus, A.F., Wijaya, S.K., Naved, M. Determining RUL Predictive Maintenance on Aircraft Engines Using GRU // Journal of Mechanical, Civil and Industrial Engineering. – 2022. – Vol. 3, no. 3. – P. 79–84. – doi: 10.32996/jmcie.2022.3.3.10
  56. Boujamza, A., Elhaq, S.L. Attention-Based LSTM for Remaining Useful Life Estimation of Aircraft Engines // Advances in Control and Optimization of Dynamical Systems. – 2022. – Vol. 55, iss. 12. – P. 450–455. – doi: 10.1016/j.ifacol.2022.07.353
  57. Jiang, Y., Li, C., Yang, Z., et al. Remaining Useful Life Estimation Combining Two-step Maximal Information Coefficient and Temporal Convolutional Network with Attention Mechanism // IEEE Access. – 2021. – Vol. 9. – P. 16 323–16 336. – doi: 10.1109/ACCESS.2021.3052305
  58. Xia, J., Feng, Y., Teng, D., et al. Distance Self–attention Network Method for Remaining Useful Life Estimation of Aeroengine with Parallel Computing // Reliability Engineering and System Safety. – 2022. – Vol. 225, no. 1. – doi: 10.1016/j.ress.2022.108636
  59. Vaswani, A., Shazeer, N., Parmar, N., et al. Attention Is All You Need // Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017). – Long Beach, CA, USA, 2017. – P. 5998–6008. – doi: 10.48550/arhiv1706.03762
  60. Ma, Q., Zhang, M., Xu, Y., et al. Remaining Useful Life Estimation for Turbofan Engine with Transformer-based Deep Architecture // Proceedings of the 26th International Conference on Automation and Computing (ICAC). – Portsmouth, United Kingdom, 2021. – doi: 10.23919/ICAC50006.2021.9594150
  61. Zhang, Z., Song, W., Li, Q. Dual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction // IEEE Transactions on Instrumentation and Measurement. – 2022. – Vol. 71. – doi: 10.1109/TIM.2022.3160561
  62. Chadha, G.S., Shah, S.R.B., Schwung, A., Ding, S.X. Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation // IEEE Access. – 2022. – Vol. 10. – doi: 10.1109/ACCESS.2022.3187702
  63. Fan, Z., Li, W., Chang, K.-C. A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction // Sensors. – 2024. – Vol. 24, no. 3. – doi: 10.3390/s24030824
  64. Xiang, F., Zhang, Y., Zhang, S., et al. Bayesian Gated-Transformer Model for Risk-Aware Prediction of Aero-Engine Remaining Useful Life // Expert System with Applications. – 2024. – Vol. 238, no. 1. – doi: 10.1016/j.eswa.2023.121859
  65. Fan, Z., Li, W., Chang, K.-C. A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation // Mathematics. – 2023. – Vol. 11, iss. 24. – DOI: 103390/math11244972
  66. Zheng, S., Ristovski, K., Farahat, A., Gupta, C. Long Short-Term Memory Network for Remaining Useful Life Estimation // Proceedings of the 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). – Dallas, TX, USA, 2017. – P. 88–95. – doi: 10.1109/ICPHM.2017.7998311
  67. Mo, H., Lucca, F., Malacarne, J., Iacca, G. Multi-Head CNN-LSTM with Prediction Error Analysis for Remaining Useful Life Prediction // Proceedings of the 27th Conference of Open Innovations Association (FRUCT). – Trento, Italy, 2020. – P. 164–171. – doi: 10.23919/FRUCT49677.2020.9111058
  68. Mo, Y., Wu, Q., Li, X., Huang, B. Remaining Useful Life Estimation via Transformer Encoder Enhanced by a Gated Convolutional Unit // Journal of Intelligent Manufacturing. – 2021. – Vol. 32. – P. 1997–2006. – doi: 10.1007/s10845-021-01750-x
  69. Siraskar, R., Kumar, S., Patil, S., et al. Reinforcement Learning for Predictive Maintenance: A Systematic Technical Review // Artificial Intelligence Review. – 2023. – Vol. 56. – P. 12 885–12 947. – doi: 10.1007/s10462-023-10468-6
  70. Hu, Y., Miao, X., Zhang, J., et al. Reinforcement Learning Driven Maintenance Strategy: A Novel Solution for Long-term Aircraft Maintenance Decision Optimization // Computers & Industrial Engineering. – 2021. – Vol. 153. – doi: 10.1006/j.cie.2020.107056
  71. Ribeiro, J., Andrade, P., Carvalho, M., et al. Playful Probes for Design Interaction with Machine Learning: A Tool for Aircraft Condition–based Maintenance Planning and Visualisation // Mathematics. – 2022. – Vol. 10, no. 9. – doi: 10.3390/math.10091604
  72. Silva, C., Andrade, P., Ribeiro, B., Santos, B.F. Adaptive Reinforcement Learning for Task Scheduling in Aircraft Maintenance // Scientific Reports. – 2023. – Vol. 13 (1). – doi: 10.1038/s41598-023-41169-3
  73. Dangut, M.D., Jennions, I.K., King, S., Skaf, Z. Application of Deep Reinforcement Learning for Extremely Rare Failure Prediction in Aircraft Maintenance // Mechanical Systems and Signal Processing. – 2022. – Vol. 171, no. 8. – doi: 10.1016/j.ymssp.2022.108873
  74. Pater, I., Reijns, A., Mitici, M. Alarm-based Predictive Maintenance Scheduling for Aircraft Engines with Imperfect Remaining Useful Life Prognostics // Reliability Engineering and System Safety. – 2022. – Vol. 221. – doi: 10.1016/j.ress.2022.108341
  75. Fink, O., Wang, Q., Svensén, M., et al. Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications // Engineering Applications and Artificial Intelligence. – 2020. – Vol. 92, no. 033. – doi: 10.1016/j.engappai.2020.103678
  76. Lee, J., Mitici, M. Deep Reinforcement Learning for Predictive Aircraft Maintenance Using Probabilistic Forecast of Remaining Useful Life // Reliability and System Safety. – 2023. – Vol. 230, no. 1. – doi: 10.1016/j.ress.2022.108908
  77. Srivastava, N., Hinton, G., Sutskever, A., et al. A Simple Way to Prevent Neural Networks from Overfitting // Journal of Machine Learning Research. – 2014. – Vol. 15. – doi: 10.5555/2627435.2670313
  78. Haarnoja, T., Zhou, A., Abbeel, P., Levine, S. Soft Actor-critic: Off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor // Proceedings of the 35th International Conference on Machine Learning (ICML). – Stockholm, Sweden, 2018. – Vol. 5. – P. 2976–2989.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

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».