An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries

Abstract

Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2010
Accession Number
ADA562707

Entities

People

  • Abhinav Saxena
  • Bhaskar Saha
  • Jie Liu
  • Kai Goebel
  • Wilson Wang

Organizations

  • National Aeronautics and Space Administration

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computational Science
  • Computing System Architectures
  • Data Sets
  • Electrical Engineering
  • Failure Mode And Effect Analysis
  • Lithium Ion Batteries
  • Machine Learning
  • Mechanical Engineering
  • Network Architecture
  • Neural Networks
  • Recurrent Neural Networks
  • Reliability
  • Signal Processing

Readers

  • Electrical Engineering
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  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks