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New fault detection and fault-tolerant scheme for Doppler velocity logger outage in ocean navigation systems

Published online by Cambridge University Press:  08 January 2021

Mojtaba Hashemi Open the ORCID record for Mojtaba Hashemi [Opens in a new window]  and
Ehsan Shami
Mojtaba Hashemi*
Affiliation:
School of Mechanical Engineering, University of Imam Hossein, Tehran, Iran
Ehsan Shami
Affiliation:
School of Mechanical Engineering, University of Imam Hossein, Tehran, Iran
*
*Corresponding author. E-mail: mo_hashemi@aut.ac.ir.
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Abstract

The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.

Keywords

DVL outagefault detectionfault-tolerantFE and FTC interactions
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 2 , March 2021 , pp. 409 - 424
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Copyright
Copyright © The Royal Institute of Navigation 2021

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References

Ansari-Rad, S., Hashemi, M. and Salarieh, H. (2019a). Pseudo DVL reconstruction by an evolutionary TS-fuzzy algorithm for ocean vehicles. Measurement, 147, 106831. doi:10.1016/j.measurement.2019.07.059CrossRefGoogle Scholar
Ansari-Rad, S., Kalhor, A. and Araabi, B. N. (2019b). Partial identification and control of MIMO systems via switching linear reduced-order models under weak stimulations. Evolving Systems, 10(2), 111–128. doi:10.1007/s12530-017-9214-8CrossRefGoogle Scholar
Brumback, B. D. and Srinath, M. D. (1987a). A fault-tolerant multisensor navigation system design. IEEE Transactions on Aerospace and Electronic Systems AES, 23(6), 738–756. doi:10.1109/TAES.1987.310911CrossRefGoogle Scholar
Brumback, B. and Srinath, M. (1987b). A Chi-square test for fault-detection in Kalman filters B. Automatic Control, IEEE Transactions on, 32(6), 552–554. Available at: https://ieeexplore.ieee.org/abstract/document/1104658/ [Accessed: 11 Oct. 2019].CrossRefGoogle Scholar
Chiang, K. W., Duong, T. T., Liao, J. K., Lai, Y. C., Chang, C. C., Cai, J. M. and Huang, S. C. (2012). On-line smoothing for an integrated navigation system with low-cost MEMS inertial sensors. Sensors, 12(12), 17372–17389. doi:10.3390/s121217372CrossRefGoogle ScholarPubMed
Davari, N. and Gholami, A. (2017). An asynchronous adaptive direct Kalman filter algorithm to improve underwater navigation system performance. IEEE Sensors Journal, 17(4), 1061–1068. doi:10.1109/JSEN.2016.2637402CrossRefGoogle Scholar
Hasan, A. M., et al. (2011). Automatic estimation of inertial navigation system errors for global positioning system outage recovery. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225(1), 86–96. doi:10.1243/09544100JAERO731CrossRefGoogle Scholar
Joerger, M. and Pervan, B. (2013). Kalman filter-based integrity monitoring against sensor faults. Journal of Guidance, Control, and Dynamics, 36(2), 349–361. doi:10.2514/1.59480CrossRefGoogle Scholar
Karmozdi, A., Hashemi, M. and Salarieh, H. (2018). Design and practical implementation of kinematic constraints in Inertial Navigation System-Doppler Velocity Log (INS-DVL)-based navigation. Navigation, Journal of the Institute of Navigation, 65(4), 629–642. doi:10.1002/navi.271Google Scholar
Li, J., et al. (2017). Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm. Information Fusion, 35, 1–10. doi:10.1016/J.INFFUS.2016.08.001CrossRefGoogle Scholar
Li, X. and Zhang, W. (2010). An adaptive fault-tolerant multisensor navigation strategy for automated vehicles. IEEE Transactions on Vehicular Technology, 59(6), 2815–2829. doi:10.1109/TVT.2010.2050014Google Scholar
Lin, Y., et al. (2018). Thruster fault-tolerant control for dynamic positioning of vessels. Applied Ocean Research, 80, 118–124. doi:10.1016/j.apor.2018.07.015CrossRefGoogle Scholar
Mirabadi, A., Schmid, F. and Mort, N. (1998). Fault Detection and Isolation in a Multisensor Train Navigation System. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems, pp. 1025–1035. Available at: https://digital-library.theiet.org/content/conferences/10.1049/cp_19980360 [Accessed: 11 Oct. 2019].Google Scholar
Ning, X., et al. (2015). INS/VNS/CNS integrated navigation method for planetary rovers. Aerospace Science and Technology, 48, 102–114. doi:10.1016/j.ast.2015.11.002CrossRefGoogle Scholar
Niu, X., Nassar, S. and El-Sheimy, N. (2007). An accurate land-vehicle MEMS IMU/GPS navigation system using 3D auxiliary velocity updates. Navigation, 54(3), 177–188. doi:10.1002/j.2161-4296.2007.tb00403.xCrossRefGoogle Scholar
Oh, S. H., et al. (2005). Attitude determination GPS/INS integrated navigation system with FDI algorithm for a UAV. Journal of Mechanical Science and Technology, 19(8), 1529–1543. doi:10.1007/BF03023931CrossRefGoogle Scholar
Pham, H. T. and Yang, B. S. (2010). Estimation and forecasting of machine health condition using ARMA/GARCH model. Mechanical Systems and Signal Processing, 24(2), 546–558. doi:10.1016/j.ymssp.2009.08.004CrossRefGoogle Scholar
Semeniuk, L. and Noureldin, A. (2006). Bridging GPS outages using neural network estimates of INS position and velocity errors. Measurement Science and Technology, 17(10), 2783–2798. doi:10.1088/0957-0233/17/10/033CrossRefGoogle Scholar
Shabani, M. and Gholami, A. (2016). Improved underwater integrated navigation system using unscented filtering approach. Journal of Navigation, 69(3), 561–581. doi:10.1017/S0373463315000752CrossRefGoogle Scholar
Ushaq, M. and Cheng, F. J. (2014). A fault tolerant integrated navigation scheme realized through online tuning of weighting factors for Federated Kalman Filter. Applied Mechanics and Materials, 1078–1085. doi:10.4028/www.scientific.net/AMM.446-447.1078Google Scholar
Whitcomb, L., Yoerger, D. and Singh, H. (1999). Advances in Doppler-based navigation of underwater robotic vehicles. Proceedings - IEEE International Conference on Robotics and Automation. IEEE, 1, 399–406. doi:10.1109/robot.1999.770011CrossRefGoogle Scholar
Wu, D. and Wang, Z. (2013). Strapdown INS/GPS integrated navigation using geometric algebra. Advances in Applied Clifford Algebras, 23(3), 767–785. doi:10.1007/s00006-013-0395-3CrossRefGoogle Scholar
Wu, Y., et al. (2019). Survey of underwater robot positioning navigation. Applied Ocean Research, doi:10.1016/j.apor.2019.06.002CrossRefGoogle Scholar
Xu, X., Li, P. and Liu, J. (2015). A fault-tolerant filtering algorithm for SINS/DVL/MCP integrated navigation system. Mathematical Problems in Engineering, 2015, 1–12. doi:10.1155/2015/581909Google Scholar
Yan, R., Gao, R. X. and Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96(PART A), 1–15. doi:10.1016/j.sigpro.2013.04.015CrossRefGoogle Scholar
Yao, Y., et al. (2017). A hybrid fusion algorithm for GPS/INS integration during GPS outages. Measurement: Journal of the International Measurement Confederation, 103, 42–51. doi:10.1016/j.measurement.2017.01.053CrossRefGoogle Scholar
Youssef, O. A. S. (2002). New algorithm to phase selection based on wavelet transforms. IEEE Power Engineering Review, 60–61. doi:10.1109/MPER.2002.4312305Google Scholar
Zabihi-Hesari, A., et al. (2019). Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(6), 1910–1923. doi:10.1177/0954406218778313Google Scholar
Zhu, Y., Cheng, X. and Wang, L. (2016). A novel fault detection method for an integrated navigation system using Gaussian process regression. Journal of Navigation, 69(4), 905–919. doi:10.1017/S0373463315001034CrossRefGoogle Scholar