Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life

Chao Hu, Byeng D. Youn, Pingfeng Wang

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

Abstract

The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust, i.e., it may be less accurate when the real data acquired after the deployment differs from the testing data; (ii) it wastes the resources for constructing the algorithms that are discarded in the deployment; (iii) it requires the testing data in addition to the training data, which increases the overall expenses for the algorithm selection. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely, the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms for data-driven prognostics. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Denver, CO, United States
Duration: Jun 20 2011Jun 23 2011

Publication series

Name2011 IEEE International Conference on Prognostics and Health Management, PHM 2011 - Conference Proceedings

Other

Other2011 IEEE International Conference on Prognostics and Health Management, PHM 2011
CountryUnited States
CityDenver, CO
Period6/20/116/23/11

Keywords

  • K-fold cross validation
  • RUL prediction
  • data-driven prognostics
  • ensemble
  • weighting schemes

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management

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