Fault detection for multi-source integrated navigation system using fully convolutional neural network

Fault detection for multi-source integrated navigation system using fully convolutional neural network

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

An accurate fault detection method is critical in preventing the integrity of multi-source navigation system from the abnormal measurements which may occur any time. Here, a multi-channel single-dimensional fully convolutional neural network fault detection method is proposed, where the system measuring residuals sequence is used as the input, and the output is the system operating state, such as normal or fault types, in pointwise. The proposed technique extracts the features with various scales, which contain both the local and the general information of the signal sequence, for making a comprehensive and precise classification. To show the validity of the proposed method, computer simulations and trolley testing based on INS/GNSS/UWB integrated navigation system are carried out. The simulation and experimental results show that the proposed fault detection method is superior to the existing algorithms on the faults detection rate and false alarm rate, and thus, system reliability and navigation precision have been greatly improved.


    1. 1)
      • 1. Stach, W., Kurgan, L., Pedryca, W.: ‘Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps’, IEEE Trans. Fuzzy Syst., 2008, 16, (1), pp. 6172.
    2. 2)
      • 2. Li, C., Li, X., Yang, X.G.: ‘A fault detection method for GNSS/INS integrated navigation system based on GARCH model’. Proc. of the ION 2015 Pacific PNT Meeting, Honolulu, Hawaii, April 2015, pp. 713718.
    3. 3)
      • 3. Filaretov, V., Zhirabok, A., Zuev, A., et al: ‘The development of system of accommodation to faults of navigation sensors of underwater vehicles with resistance to disturbance’. Control Automation Systems (ICCAS), 2014, pp. 15481553.
    4. 4)
      • 4. Rapoport, I., Oshman, Y.: ‘A Cramer-Rao-type estimation lower bound for system with measurement fault’, IEEE Trans Autom. Control., 2005, 50, (9), pp. 3445.
    5. 5)
      • 5. Wen, X., Ji, L.: ‘Fault detection and diagnosis in the INS/GPS navigation system’. World Automation Congress (WAC), HI, 2014, pp. 2732.
    6. 6)
      • 6. Bedjaoui, N., Weyer, E.: ‘Algorithms for leak detection, estimation, isolation and localization in open water channels’, Control Eng. Pract., 2011, 19, (6), pp. 564573.
    7. 7)
      • 7. Miao, L.J., Shi, J.: ‘Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems’, Chin. J. Aeronaut., 2014, 27, (4), pp. 947954.
    8. 8)
      • 8. Hartert, L., Mouchaweh, M.S., Billaudel, P.: ‘Monitoring of non stationary systems using dynamic pattern recognition’, in Rigatos, G. (Ed.): ‘Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior’, (IGI Global, Hershey, PA, 2010), pp. 417452.
    9. 9)
      • 9. Zhao, X., Wang, S., Zhang, J., et al: ‘Real-time fault detection method based on belief rule base for aircraft navigation system’, Chin. J. Aeronaut., 2013, 26, (3), pp. 717729.
    10. 10)
      • 10. Seera, M., Lim, C.P., Loo, C.K., et al: ‘A modified fuzzy min–max neural network for data clustering and its application to power quality monitoring’, Appl. Soft Comput., 2015, 28, pp. 1929.
    11. 11)
      • 11. Anas, Q., Chee, P.L.: ‘A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification’, Appl. Soft Comput., 2008, 8, (2), pp. 985995.
    12. 12)
      • 12. Ayoubi, M., Isermann, R.: ‘Neuro-fuzzy system for diagnosis’, Fuzzy Sets Syst., 1997, 89, (3), pp. 289307.
    13. 13)
      • 13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’, Advances in neural information processing systems, Lake Tahoe, December, 2012, pp. 10971105.
    14. 14)
      • 14. ‘Fault detection for airborne multi-source integrated navigation system using fully convolutional neural network’. Available at, accessed 15 September 2017.
    15. 15)
      • 15. Ince, T., Kiranyaz, S., Eren, L., et al: ‘Real-time motor fault detection by 1-D convolutional neural networks’, IEEE Trans. Ind. Electron., 2016, 63, (11), pp. 70677075.
    16. 16)
      • 16. Shelhamer, E., Long, J., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39, (4), pp. 640651.
    17. 17)
      • 17. Yuan, G.N., Yuan, K.F., Zhang, H.W.: ‘A variable proportion adaptive federal Kalman filter for INS/ESGM/GPS/DVL integrated navigation system’. Computational Sciences and Optimization (CSO), 2011 Fourth Int. Joint Conf. on. IEEE, Kunming and Lijiang City, China, 2011, pp. 978981.
    18. 18)
      • 18. Dierenbach, K., Ostrowski, S., Jozkow, G., et al: ‘UWB for navigation in GNSS compromised environments’. Proc. of the 28th Int. Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS + 2015), Tampa, FL, USA, September 2015, pp. 1418.
    19. 19)
      • 19. Koppanyi, Z., Toth, C.K.: ‘Indoor ultra-wide band network adjustment using maximum likelihood estimation’, ISPRS Ann. Photogramm., Remote Sens. Spatial Inf. Sci., 2014, 2, (1), pp. 3137.
    20. 20)
      • 20. Riedmiller, M., Heinrich, B.: ‘A direct adaptive method for faster backpropagation learning: the RPROP algorithm’. IEEE Int. Conf. on Neural Networks, IEEE, San Francisco, CA, USA, 1993, pp. 586591.

Related content

This is a required field
Please enter a valid email address