International Journal of Naval Architecture and Ocean Engineering

  • 제8권3호
  • /
  • Pages.243-251
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  • 2016
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  • 2092-6782(pISSN)
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  • 2092-6790(eISSN)
대한조선학회 (The Society of Naval Architects of Korea)
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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)
  • 투고 : 2015.08.14
  • 심사 : 2016.01.28
  • 발행 : 2016.05.31
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초록

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.

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참고문헌

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피인용 문헌

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  2. Design of novel sliding-mode controller for high-velocity AUV with consideration of residual dead load vol.25, pp.1, 2016, https://doi.org/10.1007/s11771-018-3722-y
  3. Fault diagnosis in autonomous underwater vehicle propeller in the transition stage based on GP-RPF vol.15, pp.6, 2016, https://doi.org/10.1177/1729881418814683
  4. Fault Diagnosis Method of Autonomous Underwater Vehicle Based on Deep Learning vol.470, pp.None, 2019, https://doi.org/10.1088/1757-899x/470/1/012035
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  7. Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance vol.10, pp.6, 2016, https://doi.org/10.3390/app10062048
  8. A fault diagnosis approach for autonomous underwater vehicle thrusters using time-frequency entropy enhancement and boundary constraint-assisted relative gray relational grade vol.234, pp.4, 2016, https://doi.org/10.1177/0959651819862177
  9. Fault Diagnosis Method Based on High Precision CRPF under Complex Noise Environment vol.16, pp.3, 2020, https://doi.org/10.3745/jips.01.0053
  10. An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles vol.42, pp.11, 2016, https://doi.org/10.1177/0142331219901202
  11. An Underwater Thruster Fault Diagnosis Simulator and Thrust Calculation Method Based on Fault Clustering vol.2021, pp.None, 2016, https://doi.org/10.1155/2021/6635494
  12. Improved Particle Filter Using Clustering Similarity of the State Trajectory with Application to Nonlinear Estimation: Theory, Modeling, and Applications vol.2021, pp.None, 2016, https://doi.org/10.1155/2021/9916339
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