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.(1056532596@qq.com)
    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
[1]KE Y,SONG E Z,YAO C,et al.Review of Marine diesel engine fault prediction and health management technology [J].Journal of Harbin Engineering University,2020,41(1):125-131.
[2]JI P,LIU W T,ZHANG Y,et al.Research on PID Controller of Ship Power Station Based on RBF Neural Network[J].Journal of Chongqing Institute of Technology,2020,32(2):203-209.
[3]MIAO J G,WANG J Y,ZHANG H,et al.Research progress of Fault diagnosis technology of unmanned aerial vehicle [J].Journal of Instrumentation,2020,07:1-15
[4]MALLIKA I L,RATNAM V,RAMAN S,et al.Machine lear-ning algorithm to forecast ionospheric time delays using Global Navigation satellite system observations[J].Acta Astronautica,2020,173:221-231.
[5]WANG C A.Taxi Demand Forecast Based on SVR Space-time grid Model [J].Electronic World,2020(3):51-52.
[6]ZHANG C B,SUN Y M.Prediction of tool wear in combination of feature engineering and deep feed forward Network [J].Mechanical Design and Manufacturing,2020(6):190-193.
[7]BAI S,KOLTER J Z,KOLTUN V.An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling[EB/OL].https:arXiv.org/abs/1803.01271,2018.
[8]TAYLOR S J,LETHAM B.2017:Forecasting at scale[EB/OL].https://doi.org/10.7287/peerj.preprints.3190v2.
[9]LAI G K,CHANG W C,YANG Y M,et al.Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks[C]//The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval(SIGIR’18).2018:95-104.
[10]SHIH S Y,SUN F K,LEE H Y.Temporal pattern attention for multivariate time series forecasting[J].Machine Learning,2019,108(8/9):1421-1441.
[11]YI L R,WANG S Y,YIN L L,et al.Prediction of industrialSensor timing Data based on multivariable LSTM [J].Intelligent Computer and Applications,2018,8(5):13-16.
[12]CHEN Y P,YU L,CHEN H.Traffic Anomaly Detection Based on Wavelet Neural and ARMA Model in Big Data Environment[J].Journal of Chongqing Institute of Technology,2020,33(10):149-154.
[13]GHOSH S,DAS N,DAS I,et al.Understanding Deep Learning Techniques for Image Segmentation[J].ACM Computing Surveys,2019,52(4):1-35.
[14]LAI G,CHANG W C,YANG Y,et al.Modeling long-andshort-term temporal patterns with deep neural networks[C]//41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2018).Ann Arbor,MI,United states 2018:95-104.
[15]WANG J J,YAN J X,LI C,et al.Deep heterogeneous GRU model for predictive analytics in smart manufacturing:Application to tool wear prediction[J].Computers in Industry,2019,111:1-14.
[16]SUN Y S,JIANG Q,HU J,et al.Generation model of pedestrian trajectory prediction based on attention mechanism [J].Computer Applications,2019,39(3):668-674.
[17]SARA M,DAVID L.Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting[J]. Sensors(Basel,Switzerland),2019,19(20):4571-4589.
[1] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[2] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[3] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[4] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[5] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[6] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[9] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[10] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[11] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[12] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[13] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[14] MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147.
[15] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!