Data driven prognostics with lack of training data sets

Zhimin Xi, Xiangxue Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Data-driven prognostics typically requires sufficient offline training data sets for accurate remaining useful life (RUL) prediction of engineering products. This paper investigates performances of typical data-driven methodologies when the amount of training data sets is insufficient. The purpose is to better understand these methodologies especially when offline training datasets are insufficient. The neural network, similarity-based approach, and copula-based sampling approach were investigated when only three run-to-failure training units were available. The example of lithium-ion (Li-ion) battery capacity degradation was employed for the demonstration.

Original languageEnglish (US)
Title of host publication41st Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791857076
DOIs
StatePublished - Jan 1 2015
EventASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015 - Boston, United States
Duration: Aug 2 2015Aug 5 2015

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2A-2015

Other

OtherASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
CountryUnited States
CityBoston
Period8/2/158/5/15

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All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Keywords

  • Copula-based sampling approach
  • Data-driven
  • Neural network
  • Prognostics
  • Similarity-based approach

Cite this

Xi, Z., & Zhao, X. (2015). Data driven prognostics with lack of training data sets. In 41st Design Automation Conference (Proceedings of the ASME Design Engineering Technical Conference; Vol. 2A-2015). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201546932