Predictive model evaluation for PHM

  1. (PDF, 488 KB)
  2. Get@NRC: Predictive model evaluation for PHM (Opens in a new window)
AuthorSearch for: ; Search for: ; Search for: ; Search for:
Journal titleInternational Journal of Prognostics and Health Management
Pages111; # of pages: 11
SubjectMachine Learning Algorithms; Generic Methods; binary classifier; prognostics and health management (PHM); prognostics; Time to Failure; Predictive Models Evaluation
AbstractIn the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studied to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.
Publication date
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21275937
Export citationExport as RIS
Report a correctionReport a correction
Record identifierdce076fe-03db-4d8c-b097-5ca015aa414d
Record created2015-08-11
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)