International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles

  • Sun, Yu-shan (Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University) ;
  • Ran, Xiang-rui (Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University) ;
  • Li, Yue-ming (Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University) ;
  • Zhang, Guo-cheng (Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University) ;
  • Zhang, Ying-hao (Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University)
  • Received : 2015.08.14
  • Accepted : 2016.01.28
  • Published : 2016.05.31
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Abstract

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

Keywords

References

  1. Frank, P.M., 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results. automatica 26 (3), 459-474. https://doi.org/10.1016/0005-1098(90)90018-D
  2. Freitas, N., Dearden, R., Hutter, F., Morales-Menendez, R., Mutch, J., Poole, D., 2004. Diagnosis by a waiter and a mars explorer. Proc. IEEE 92 (3), 455-468. https://doi.org/10.1109/JPROC.2003.823157
  3. Gafurov, S.A., Klochkov, E.V., 2015. Autonomous unmanned underwater vehicles development tendencies. Procedia Eng. 106, 141-148. https://doi.org/10.1016/j.proeng.2015.06.017
  4. Han, S., Zhang, X.L., Chen, L., Wen-Jin, X.U., 2010. Object tracking method based on improved Gaussian particle filter. Syst. Eng. Electron. 32 (6), 1191-1194.
  5. Herold, C., Despiegel, V., Gentric, S., Dubuisson, S., Bloch, I., 2012. Head shape estimation using a particle filter including unknown static parameters. In: VISAPP 2012 e Proceedings of the International Conference on Computer Vision Theory and Applications, 2, pp. 284-293.
  6. Hutt, F., Dearden, R., 2003. The Gaussian particle filter for diagnosis of nonlinear system. In: FAC Symposium on Fault Detection, Supervision, and Safety of Technical Processes.
  7. Hutter, F., Dearden, R., 2003. Efficient on-line fault diagnosis for non-linear system. In: International Symposium on Artificial Intelligence, Robotics and Automation in Space.
  8. Schmal, Kendra, Cheng, Haiyan, 2015. Numerical study of a hybrid particle filter. Lect. Notes Eng. Comput. Sci. 2215 (1), 419-422.
  9. Mai, B.L., Choi, H.S., Seo, J.M., Baek, S.H., Kim, J.Y., 2014. Development and control of a new auv platform. Int. J. Control Autom. Syst. 12 (4), 886-894. https://doi.org/10.1007/s12555-012-0385-6
  10. Matko, Barisic, Antonio, Vasilijevic, Dula, Nad, 2012. Sigma-point unscented Kalman filtering used for AUV navigation. In: MED 2012-Conference Proceedings, pp. 1365-1372.
  11. Sadeghzadeh Nokhodberiz, Nargess, Poshtan, Javad, 2014. Belief consensusbased distributed particle filters for fault diagnosis of non-linear distributed systems. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 228 (3), 123-137.
  12. Silveira, P.M., Duque, C., Baldwin, T., Ribeiro, P.F., 2009. Sliding window recursive DFT with dyadic downsampling : a new strategy for time-varying power harmonic decomposition. In: Power & Energy Society General Meeting. PES '09. IEEE, pp. 1-6.
  13. Yafe, Han, Mingkai, Yue, Shuhua, Liu, Zhiguo, Sun, 2014. Particle filter resampling algorithms performance analysis under non-Gaussian noise. J. Comput. Inf. Syst. 10 (4), 1751-1758.
  14. Yuan, Fang, Zhu, Da-qi, Ye, Yin-zhong, 2011. Sliding-mode fault-tolerant control method of underwater vehicle based on reduced-order kalman filter. Control Decis. 26 (7), 1031-1035.
  15. Zhu, Hongqian, Hu, Huosheng, Gui, Wei hua, 2009. Adaptive unscented Kalman filter for deep-sea tracked vehicle localization. In: 2009 IEEE International Conference on Information and Automation, ICIA 2009, pp. 1056-1061.

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