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Volume 26 Issue 2
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2018  >  26(2): 157-165
DUAN Jie, LI Hui, CHEN Zi-li, GONG Shi-hua, ZHAO Chao-wen. Online Fault Diagnosis of AUV Sensor Based on RBF and OS-ELM Neural Networks[J]. Journal of Unmanned Undersea Systems, 2018, 26(2): 157-165. doi: 10.11993/j.issn.2096-3920.2018.02.010
Citation: DUAN Jie, LI Hui, CHEN Zi-li, GONG Shi-hua, ZHAO Chao-wen. Online Fault Diagnosis of AUV Sensor Based on RBF and OS-ELM Neural Networks[J]. Journal of Unmanned Undersea Systems, 2018, 26(2): 157-165. doi: 10.11993/j.issn.2096-3920.2018.02.010
shu

Online Fault Diagnosis of AUV Sensor Based on RBF and OS-ELM Neural Networks

doi: 10.11993/j.issn.2096-3920.2018.02.010

  • Kunming Branch of the 705 Research Institute, China Shipbuilding Industry Corporation, Kunming 650106, China

  • Received Date: 2017-06-26
  • Rev Recd Date: 2017-10-01
  • Publish Date: 2018-05-08
    Abstract
  • Sensor is an important component part of an autonomous undersea vehicle(AUV). Real-time and accurate online fault diagnosis of AUV sensors is of great significance to improve the safety of an AUV. This study analyzes the machine learning algorithms, and builds a radial basis function(RBF) neural network-based AUV sensor predictor with high accuracy and real-time performance. Subsequently, the online sequential extreme learning machine(OS-ELM) algorithm is applied to the online sensor fault diagnosis to improve the real time performance and accuracy of the predictor. Two kinds of fault diagnosis models are simulated and compared by using the sea trial data of AUV sensor, and the results show that the prediction accuracy and real-time performance of the OS-ELM neural network predictor with RBF neural network algorithm are higher than that of RBF neural network predictor. This research may provide a reference for the design of on-line fault diagnosis scheme of AUV control system.

     

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