Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211100285-9.doi: 10.11896/jsjkx.211100285

• Interdiscipline & Application • Previous Articles     Next Articles

Empirical Research on Remaining Useful Life Prediction Based on Machine Learning

WANG Jia-chang1, ZHENG Dai-wei2, TANG Lei1, ZHENG Dan-chen1, LIU Meng-juan2   

  1. 1 Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu 610213,China
    2 Network and Data Security Key Laboratory of Sichuan Province,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:WANG Jia-chang,born in 1978,postgraduate,senior engineer.His main research interests include nuclear reactor simulation and data mining.
  • Supported by:
    Nationl Natural Science Foundation of China(61202445) and Fundamental Research Funds for the Central Universities of Ministry of Education of China(ZYGX2016J096).

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Abstract: Remaining useful life(RUL) prediction is one essential task of the predictive maintenance system.This paper investigates the latest RUL prediction methods,focusing on direct RUL prediction based on machine learning.Firstly,we describe the four representative machine learning models adopted by the RUL prediction methods,including support vector regression(SVR),multilayer perceptron(MLP),convolutional neural network(CNN),and recurrent neural network(RNN).And then,we give the three primary benchmark datasets and two performance evaluation metrics widely used in RUL prediction.The contribution of this paper is to demonstrate the steps and key technical details of how to build the RUL prediction models over the benchmark dataset(C-MAPSS) provided by NASA.We also compare the performance of these representative prediction models in detail and visually analyze the experimental results.Experimental results show that the performance of SVR with a shallow structure is significantly weaker than those based on deep neural networks.CNN and RNN based models have a solid ability for mining complex feature interaction and temporal feature interaction.Finally,we provide an outlook on the future of predictive maintenance technology and discuss the main challenges.

Key words: Predictive maintenance, Remaining useful life prediction, Machine learning, Support vector regression, Multilayer perceptron, Convolutional neural network, Recurrent neural network

CLC Number: 

  • TP393
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