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An intelligent prognostic method for SSADT based on SVM

  • Fuqiang Sun*
  • , Tongmin Jiang
  • , Xiaoyang Li
  • , Ye Fan
  • *Corresponding author for this work
  • Beihang University

Research output: Contribution to journalArticlepeer-review

Abstract

The support vector machine (SVM), which has long-term prediction period, strong generalization ability and high prediction accuracy, provides an efficient new way for life prediction of accelerated degradation testing (ADT). In this paper, an intelligent prognostic model for step-stress ADT (SSADT) based on SVM is proposed. The SSADT data of superluminescent diode (SLD) is utilized to validate the proposed method.

Original languageEnglish
Pages (from-to)103-108
Number of pages6
JournalChemical Engineering Transactions
Volume33
DOIs
StatePublished - 2013

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