Ensemble of data-driven prognostic algorithms with weight optimization and k-fold cross validation

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 weightedsum 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. Two case studies were employed to demonstrate the effectiveness of the proposed prognostic approach. The results suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.

Original languageEnglish (US)
Title of host publicationAnnual Conference of the Prognostics and Health Management Society, PHM 2010
PublisherPrognostics and Health Management Society
ISBN (Electronic)9781936263011
StatePublished - Jan 1 2010
Externally publishedYes
EventAnnual Conference of the Prognostics and Health Management Society, PHM 2010 - Portland, United States
Duration: Oct 13 2010Oct 16 2010

Publication series

NameAnnual Conference of the Prognostics and Health Management Society, PHM 2010

Other

OtherAnnual Conference of the Prognostics and Health Management Society, PHM 2010
CountryUnited States
CityPortland
Period10/13/1010/16/10

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications
  • Software

Fingerprint Dive into the research topics of 'Ensemble of data-driven prognostic algorithms with weight optimization and k-fold cross validation'. Together they form a unique fingerprint.

Cite this