Sensor failure detection, identification, and accommodation using fully connected cascade neural network

IEEE Transactions on Industrial Electronics

Volumn 62, Issue 3, 2015, Pages 1683-1692

  Hussain, Saed ^a   Mokhtar, Maizura ^a,b   Howe, Joe M ^a,c  

^a UNIVERSITY OF CENTRAL LANCASHIRE   (United Kingdom)

^b UNIVERSITY OF SHEFFIELD   (United Kingdom)

^c UNIVERSITY OF CHESTER   (United Kingdom)

Author keywords

analytical redundancy; failure detection; fault tolerance; neural networks; Sensors

Indexed keywords

AIRCRAFT CONTROL; FAULT DETECTION; FAULT TOLERANCE; FLIGHT CONTROL SYSTEMS; NEURAL NETWORKS; NEURONS; SENSORS;

ANALYTICAL REDUNDANCY; CASCADE NEURAL NETWORKS; FAILURE DETECTION; MULTI LAYER PERCEPTRON; NEURAL NETWORK (NN); RELIABLE OPERATION; SAFETY CRITICAL APPLICATIONS; SENSOR FAILURE DETECTION;

NETWORK ARCHITECTURE;

EID: 84923171457     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2014.2361600     Document Type: Article

References (51)

1. E. Balaban, A. Saxena, P. Bansal, K. F. Goebel, and S. Curran, "Modeling, detection, disambiguation of sensor faults for aerospace applications," IEEE Sensors J., vol. 9, no. 12, pp. 1907-1917, Dec. 2009.
2. J. C. D. Silva, A. Saxena, E. Balaban, and K. Goebel, "A knowledge-based system approach for sensor fault modeling, detection and mitigation," Expert Syst. Appl., vol. 39, no. 12, pp. 10 977-10 989, Sep. 2012.
3. I. Samy, I. Postlethwaite, and D. Gu, "Neural network based sensor validation scheme demonstrated on an unmanned air vehicle (UAV) model," in Proc. 47th IEEE Conf. Decision Control, 2008, pp. 1237-1242.
4. G. Campa,M. Fravolini, M. Napolitano, and B. Seanor, "Neural networksbased sensor validation for the flight control system of a B777 research model," in Proc. Amer. Control Conf., 2002, vol. 1, pp. 412-417.
5. Q. Wang, F. Yang, Q. Ge, and Q. Yang, "A sensor network modeling and fault detection method for large wind farms by using neural networks," in Proc. 11th IEEE Int. Conf. Control Autom., 2014, pp. 308-313.
6. Z. F. Wang, J.-L. Zarader, and S. Argentieri, "Aircraft fault diagnosis and decision system based on improved artificial neural networks," in Proc. IEEE/ASME Int. Conf. AIM, Jul. 2012, pp. 1123-1128.
7. M. Mrugalski, "An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection," Int. J. Appl. Math. Comput. Sci., vol. 23, no. 1, pp. 157-169, Jan. 2013.
8. S. Rajendran, U. Govindarajan, S. Senthilvadivelu, and S. Uandai, "Intelligent sensor fault-tolerant control for variable speed wind electrical systems," IET Power Electron., vol. 6, no. 7, pp. 1308-1319, Aug. 2013.
9. D. Gastaldello, A. Souza, C. Ramos, P. da Costa Junior, and M. Zago, "Fault location in underground systems using artificial neural networks and PSCAD/EMTDC," in Proc. IEEE 16th Int. Conf. INES, Jun. 2012, pp. 423-427.
10. M. R. Napolitano, Y. An, and B. A. Seanor, "A fault tolerant flight control system for sensor and actuator failures using neural networks," Aircraft Des., vol. 3, no. 2, pp. 103-128, Jun. 2000.
11. I. Samy, I. Postlethwaite, and D.-W. Gu, "Survey and application of sensor fault detection and isolation schemes," Control Eng. Pract., vol. 19, no. 7, pp. 658-674, Jul. 2011.
12. M. Meireles, P. Almeida, and M. Simoes, "A comprehensive review for industrial applicability of artificial neural networks," IEEE Trans. Ind. Electron., vol. 50, no. 3, pp. 585-601, Jun. 2003.
13. B. M. Wilamowski, "How to not get frustrated with neural networks," in Proc. IEEE Int. Conf. Ind. Technol., Mar. 2011, pp. 5-11.
14. B. Wilamowski, N. Cotton, O. Kaynak, and G. Dundar, "Computing gradient vector and Jacobian matrix in arbitrarily connected neural networks," IEEE Trans. Ind. Electron., vol. 55, no. 10, pp. 3784-3790, Oct. 2008.
15. D. Hunter, H. Yu, and M. Pukish, "Selection of proper neural network sizes and architectures: A comparative study," IEEE Trans. Ind. Informat., vol. 8, no. 2, pp. 228-240, May 2012.
16. B. M. Wilamowski, "Challenges in applications of computational intelligence in industrial electronics," in Proc. IEEE ISIE, Jul. 2010, pp. 15-22.
17. B. Wilamowski, "Neural network architectures and learning algorithms," IEEE Ind. Electron. Mag., vol. 3, no. 4, pp. 56-63, Dec. 2009.
18. S. Hussain, M. Mokhtar, and J. M. Howe, "Aircraft sensor estimation for fault tolerant flight control system using fully connected cascade neural network," in Proc. Int. Joint Conf. Neural Netw., 2013, pp. 1-8.
19. P. Goupil, "AIRBUS state of the art and practices on FDI and FTC in flight control system," Control Eng. Pract., vol. 19, no. 6, pp. 524-539, Jun. 2011.
20. Y. Zhang and J. Jiang, "Bibliographical review on reconfigurable faulttolerant control systems," Ann. Rev. Control, vol. 32, no. 2, pp. 229-252, Dec. 2008.
21. R. Isermann and P. Ballé, "Trends in the application of model-based fault detection and diagnosis of technical processes," Control Eng. Pract., vol. 5, no. 5, pp. 709-719, May 1997.
22. G. Campa et al., "On-line learning neural networks for sensor validation for the flight control system of a B777 research scale model," Int. J. Robust Nonlinear Control, vol. 12, no. 11, pp. 987-1007, Sep. 2002.
23. Y. An, "A design of fault tolerant flight control systems for sensor and actuator failures using on-line learning neural network," Ph.D. dissertation, West Virginia Univ., Morgantown, WV, USA, 1998.
24. I. Samy, I. Postlethwaite, and D.-W. Gu, "Detection and accommodation of sensor faults in UAVs-A comparison of NN and EKF based approaches," in Proc. 49th IEEE CDC, Dec. 2010, pp. 4365-4372.
25. S. Julier and J. Uhlmann, "A new extension of the Kalman filter to nonlinear systems," in Proc. SPIE, Signal Process., Sensor Fusion, Target Recog. VI, 1997, vol. 3068, pp. 182-193.
26. G. Heredia and A. Ollero, "Sensor fault detection in small autonomous helicopters using observer/Kalman filter identification," in Proc. IEEE Int. Conf. Mechatronics, Apr. 2009, pp. 1-6.
27. C. Hajiyev and H. E. Soken, "Robust estimation of UAV dynamics in the presence of measurement faults," J. Aerosp. Eng., vol. 25, no. 1, pp. 80- 89, Jan. 2012.
28. 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, Jul. 2008.
29. I. Hwang, S. Kim, Y. Kim, and C. E. Seah, "A survey of fault detection, isolation, reconfiguration methods," IEEE Trans. Control Syst. Technol., vol. 18, no. 3, pp. 636-653, May 2010.
30. G. H. B. Foo, Z. Xinan, and D. M. Vilathgamuwa, "A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter," IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3485-3495, Aug. 2013.
31. G.-J. Huang, G.-X. Liu, G.-X. Chen, and T.-G. Chen, "Self-recovery method based on auto-associative neural network for intelligent sensors," in Proc. 8th World World Congr. Intell. Control Autom., Jul. 2010, no. 2007, pp. 6918-6922.
32. S. Gururajan, M. L. Fravolini, H. Chao, M. Rhudy, and M. R. Napolitano, "Performance evaluation of neural network based approaches for airspeed Sensor Failure Accommodation on a small UAV," in Proc. 21st Mediterranean Conf. Control Autom., Jun. 2013, pp. 603-608.
33. T.-H. Guo and J. Musgrave, "Neural network based sensor validation for reusable rocket engines," in Proc. Amer. Control Conf., 1995, vol. 2, pp. 1367-1372.
34. S. Toma, L. Capocchi, and G.-A. Capolino, "Wound-rotor induction generator inter-turn short-circuits diagnosis using a new digital neural network," IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4043-4052, Sep. 2013.
35. H. Yu andW. Auburn, "Fast and efficient and training of neural networks," in Proc. 3rd Int. Conf. Hum. Syst. Interaction, May 2010, pp. 175-181.
36. B. M. Wilamowski, N. Cotton, J. Hewlett, and O. Kaynak, "Neural network trainer with second order learning algorithms," in Proc. Int. Conf. Intell. Eng. Syst., Jun. 2007, pp. 127-132.
37. B. M. Wilamowski, "C++ implementation of neural networks trainer," in Proc. Int. Conf. Intell. Eng. Syst., Apr. 2009, pp. 257-262.
38. B. M. Wilamowski, "Advanced learning algorithms," in Proc. Int. Conf. Intell. Eng. Syst., Apr. 2009, pp. 9-17.
39. B.Wilamowski, "Understanding neural networks," in Intelligent Systems, ser. Electrical Engineering Handbook. Boca Raton, FL, USA: CRC Press, Feb. 2011, pp. 1-11.
40. G. Zhang, B. Eddy Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," Int. J. Forecasting, vol. 14, no. 1, pp. 35-62, Mar. 1998.
41. Laminar Research, X-Plane 10 Global | The World's Most Advanced Flight Simulator | X-Plane.com. [Online]. Available: http://www. x-plane.com
42. Laminar Research, FAA-Certified X-Plane. [Online]. Available: http:// www.x-plane.com/pro/certified/
43. X-Plane 10 Manual, Laminar Research, Columbia, SC, USA, 2012.
44. R. Collinson, "Navigation systems," in Introduction to Avionics Systems. Dordrecht, The Netherlands: Springer-Verlag, 2011, pp. 303-376.
45. R. P. G. Collinson, "Inertial sensors and attitude derivation," in Introduction to Avionics Systems. Dordrecht, The Netherlands: Springer-Verlag, 2011, pp. 255-302.
46. D.-M. Ma, J.-K. Shiau, I.-C. Wang, and Y.-H. Lin, "Attitude determination using a MEMS-based flight information measurement unit," Sensors, vol. 12, no. 1, pp. 1-23, Jan. 2012.
47. R. Collinson, "Aerodynamics and aircraft control," in Introduction to Avionics Systems. Dordrecht, The Netherlands: Springer-Verlag, 2011, pp. 101-117.
48. A. Kozionov, M. Kalinkin, A. Natekin, and A. Loginov, "Wavelet-based sensor validation: Differentiating abrupt sensor faults from system dynamics," in Proc. IEEE 7th Int. Symp. Intell. Signal Process., Sep. 2011, pp. 1-5.
49. I. Samy, "Development and evaluation of neural network models for cost reduction in unmanned air vehicles," Ph.D. dissertation, Univ. Leicester, Leicester, U.K., May 2009.
50. G. Heredia, A. Ollero, M. Bejar, and R. Mahtani, "Sensor and actuator fault detection in small autonomous helicopters," Mechatronics, vol. 18, no. 2, pp. 90-99, Mar. 2008.
51. M. Perhinschi et al., "An adaptive threshold approach for the design of an actuator failure detection and identification scheme," IEEE Trans. Control Syst. Technol., vol. 14, no. 3, pp. 519-525, May 2006.