Computer Science ›› 2021, Vol. 48 ›› Issue (6): 184-189.doi: 10.11896/jsjkx.200700117

• Artificial Intelligence • Previous Articles     Next Articles

Fault Prediction Method Based on Improved RNN and VAR for Ship Equipment

ZENG You-yu, XIE Qiang   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2020-07-19 Revised:2020-08-13 Online:2021-06-15 Published:2021-06-03
  • About author:ZENG You-yu,born in 1996,postgraduate.Her main research interests include fault diagnosis and prediction,knowledge engineering.(
    XIE Qiang,born in 1972,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include knowle-dge engineering and data mining.

Abstract: Aiming at the problem that the existing multivariable time series prediction methods cannot be applied to the multi-sensor fault prediction of ships,an improved recurrent neural network and vector autoregressive fault prediction method for ships equipment is proposed.This method can not only learn the interdependence of multiple variables and the long-term dependence of time series,but also help to reduce the insensitivity of traditional neural network to the input scale of time series prediction.Firstly,the data of normal state and fault state are extracted from the ship history database and converted into the input of the supervised learning problem.Then,the complex correlation between ship variables is captured by the attention mechanism.The nonlinear and linear relationship of ship time signals are captured by inputting the output of attention mechanism into recurrent neural network and vector autoregression.Finally,the outputs of recurrent neural network components and the outputs of vector autoregressive components are processed as the final prediction results.The experimental results show that the proposed method is more stable in the training process of ship equipment fault prediction,and the root-mean-square error of the test results below 1.2.It can more accurately predict the trend of ship equipment properties and fault occurrence.

Key words: Attention mechanism, Fault prediction, Recurrent neural network, Ship equipment, Vector autoregression

CLC Number: 

  • TP206
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