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Improving efficiency of angular velocity sensors on aircraft

Yıl 2022, Cilt: 03 Sayı: 02, 112 - 123, 29.12.2022

Öz

For flying equipment, the construction of intelligent systems that solve problems arising during operation is a new trend nowadays. Self-diagnostic and failure warning systems are developed on most modern aircraft, making the technical exploitation process safer and more cost-effective. In the study, the author has completed a small part of the system to automatically diagnose and repair the failure of the angular speed sensing block on the flying device. The author presents the following contents to complete this system in this paper. The primary purpose of the research is to build a complete automatic fault diagnosis and repair system for a specific class of inductance (angular speed sensor). The algorithms proposed in the paper are simulated on Matlab Simulink software, which will prove the feasibility of the proposed algorithm.

Kaynakça

  • [1] Z. Wang, J. . Zarader, and S. Argentieri, “Aircraft fault diagnosis and decision system based on improved artificial neural networks,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2012, pp. 1123–1128.
  • [2] W. Bin Chen, X. L. Liu, C. J. He, and Y. J. Liu, “Knowledge base design for fault diagnosis expert system based on production rule,” Proc. - 2009 Asia-Pacific Conf. Inf. Process. APCIP 2009, vol. 1, pp. 117–119, 2009.
  • [3] L. CHU, Q. LI, F. GU, X. DU, Y. HE, and Y. DENG, “Design, modeling, and control of morphing aircraft: A review,” Chinese J. Aeronaut., vol. 35, no. 5, pp. 220–246, May 2022.
  • [4] M. Cardei and D. Z. Du, “Improving Wireless Sensor Network Lifetime through Power Aware Organization,” Wirel. Networks 2005 113, vol. 11, no. 3, pp. 333–340, May 2005.
  • [5] T. Yang, “Aviation Sensors and Their Calibration,” Telem. Theory Methods Flight Test, pp. 81–149, 2021.
  • [6] A. Khamis, “Smart Mobility,” Smart Mobil., 2021.
  • [7] İ. Orhan, M. Kapanoğlu, and T. Karakoç, “Concurrent aircraft routing and maintenance scheduling ,” J. Aeronaut. Sp. Technol., vol. 5, no. 1, pp. 73–79, Jan. 2011.
  • [8] C. Hajiyev and F. Caliskan, “Sensor and control surface/actuator failure detection and isolation applied to F-16 flight dynamic,” Aircr. Eng. Aerosp. Technol., vol. 77, no. 2, pp. 152–160, 2005.
  • [9] C. Hajiyev and F. Caliskan, “Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering,” Aerosp. Sci. Technol., vol. 4, no. 6, pp. 415–422, Sep. 2000.
  • [10] S. Brooks and R. Roy, “An overview of self-engineering systems,” https://doi.org/10.1080/09544828.2021.1914323, vol. 32, no. 8, pp. 397–447, 2021.
  • [11] P. Lu, L. Van Eykeren, E. Van Kampen, C. C. De Visser, and Q. P. Chu, “Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis,” J. Guid. Control. Dyn., vol. 39, no. 3, pp. 590–604, Aug. 2015.
  • [12] W. Xue, Y. Q. Guo, and X. D. Zhang, “A bank of kalman filters and a Robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator,” Second Int. Conf. Innov. Comput. Inf. Control. ICICIC 2007, 2007.
  • [13] C. Hajiyev and H. E. Soken, “Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults,” Aerosp. Sci. Technol., vol. 28, no. 1, pp. 376–383, Jul. 2013.
  • [14] Q. He, W. Zhang, P. Lu, and J. Liu, “Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis,” Aerosp. Sci. Technol., vol. 98, p. 105649, Mar. 2020.
  • [15] G. F. Sullivan, “A O(t3+ |E| ) Fault Identification Algorithm for Diagnosable Systems,” IEEE Trans. Comput., vol. 37, no. 4, pp. 388–397, Apr. 1988.
  • [16] D. Q. Tuan, S. N. Firsov, and O. A. Pishchukhina, “Design a fault diagnose block of angular velocity sensors for control systems of a multipurpose aircraft.,” Sci. Technol. Air Force Ukr., vol. 11, no. 2, pp. 84–88, 2012.
  • [17] P. Lu, L. Van Eykeren, E. J. van Kampen, C. de Visser, and Q. Chu, “Double-model adaptive fault detection and diagnosis applied to real flight data,” Control Eng. Pract., vol. 36, pp. 39–57, Mar. 2015.
  • [18] S. Kim, J. Choi, and Y. Kim, “Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter,” IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 940–952, 2008.
  • [19] E. Baskaya, M. Bronz, and D. Delahaye, “Fault detection & diagnosis for small UAVs via machine learning,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2017-September, Nov. 2017.
  • [20] H. S. TRAN, T. T. TRAN, D. NGUYEN, D. D. QUOC, and H. T. NGUYEN, “Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors,” Int. J. Aviat. Sci. Technol., vol. 02, no. 01, pp. 15–21, Jun. 2021.
  • [21] R. Isermann, Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media, 2005.
Yıl 2022, Cilt: 03 Sayı: 02, 112 - 123, 29.12.2022

Öz

Kaynakça

  • [1] Z. Wang, J. . Zarader, and S. Argentieri, “Aircraft fault diagnosis and decision system based on improved artificial neural networks,” in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2012, pp. 1123–1128.
  • [2] W. Bin Chen, X. L. Liu, C. J. He, and Y. J. Liu, “Knowledge base design for fault diagnosis expert system based on production rule,” Proc. - 2009 Asia-Pacific Conf. Inf. Process. APCIP 2009, vol. 1, pp. 117–119, 2009.
  • [3] L. CHU, Q. LI, F. GU, X. DU, Y. HE, and Y. DENG, “Design, modeling, and control of morphing aircraft: A review,” Chinese J. Aeronaut., vol. 35, no. 5, pp. 220–246, May 2022.
  • [4] M. Cardei and D. Z. Du, “Improving Wireless Sensor Network Lifetime through Power Aware Organization,” Wirel. Networks 2005 113, vol. 11, no. 3, pp. 333–340, May 2005.
  • [5] T. Yang, “Aviation Sensors and Their Calibration,” Telem. Theory Methods Flight Test, pp. 81–149, 2021.
  • [6] A. Khamis, “Smart Mobility,” Smart Mobil., 2021.
  • [7] İ. Orhan, M. Kapanoğlu, and T. Karakoç, “Concurrent aircraft routing and maintenance scheduling ,” J. Aeronaut. Sp. Technol., vol. 5, no. 1, pp. 73–79, Jan. 2011.
  • [8] C. Hajiyev and F. Caliskan, “Sensor and control surface/actuator failure detection and isolation applied to F-16 flight dynamic,” Aircr. Eng. Aerosp. Technol., vol. 77, no. 2, pp. 152–160, 2005.
  • [9] C. Hajiyev and F. Caliskan, “Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering,” Aerosp. Sci. Technol., vol. 4, no. 6, pp. 415–422, Sep. 2000.
  • [10] S. Brooks and R. Roy, “An overview of self-engineering systems,” https://doi.org/10.1080/09544828.2021.1914323, vol. 32, no. 8, pp. 397–447, 2021.
  • [11] P. Lu, L. Van Eykeren, E. Van Kampen, C. C. De Visser, and Q. P. Chu, “Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis,” J. Guid. Control. Dyn., vol. 39, no. 3, pp. 590–604, Aug. 2015.
  • [12] W. Xue, Y. Q. Guo, and X. D. Zhang, “A bank of kalman filters and a Robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator,” Second Int. Conf. Innov. Comput. Inf. Control. ICICIC 2007, 2007.
  • [13] C. Hajiyev and H. E. Soken, “Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults,” Aerosp. Sci. Technol., vol. 28, no. 1, pp. 376–383, Jul. 2013.
  • [14] Q. He, W. Zhang, P. Lu, and J. Liu, “Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis,” Aerosp. Sci. Technol., vol. 98, p. 105649, Mar. 2020.
  • [15] G. F. Sullivan, “A O(t3+ |E| ) Fault Identification Algorithm for Diagnosable Systems,” IEEE Trans. Comput., vol. 37, no. 4, pp. 388–397, Apr. 1988.
  • [16] D. Q. Tuan, S. N. Firsov, and O. A. Pishchukhina, “Design a fault diagnose block of angular velocity sensors for control systems of a multipurpose aircraft.,” Sci. Technol. Air Force Ukr., vol. 11, no. 2, pp. 84–88, 2012.
  • [17] P. Lu, L. Van Eykeren, E. J. van Kampen, C. de Visser, and Q. Chu, “Double-model adaptive fault detection and diagnosis applied to real flight data,” Control Eng. Pract., vol. 36, pp. 39–57, Mar. 2015.
  • [18] S. Kim, J. Choi, and Y. Kim, “Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter,” IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 940–952, 2008.
  • [19] E. Baskaya, M. Bronz, and D. Delahaye, “Fault detection & diagnosis for small UAVs via machine learning,” AIAA/IEEE Digit. Avion. Syst. Conf. - Proc., vol. 2017-September, Nov. 2017.
  • [20] H. S. TRAN, T. T. TRAN, D. NGUYEN, D. D. QUOC, and H. T. NGUYEN, “Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors,” Int. J. Aviat. Sci. Technol., vol. 02, no. 01, pp. 15–21, Jun. 2021.
  • [21] R. Isermann, Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media, 2005.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uzay Mühendisliği
Bölüm Research Articles
Yazarlar

Hong Son Tran 0000-0002-7956-2377

Truong-thanh Nguyen Bu kişi benim 0000-0001-7992-2291

Anh-trung Vuong Bu kişi benim 0000-0002-4602-3975

Dinh-dung Nguyen 0000-0002-8966-051X

Omar Alharasees 0000-0002-6899-6057

Utku Kale 0000-0001-9178-5138

Yayımlanma Tarihi 29 Aralık 2022
Gönderilme Tarihi 19 Mayıs 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 03 Sayı: 02

Kaynak Göster

APA Tran, H. S., Nguyen, T.-t., Vuong, A.-t., Nguyen, D.-d., vd. (2022). Improving efficiency of angular velocity sensors on aircraft. International Journal of Aviation Science and Technology, 03(02), 112-123.

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