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A data driven dynamic health monitoring method for Electronic System

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Considering the health monitoring requirement of Electronic System, two indexes, such as detection speed and detection reliability, are indispensable. Here a data driven dynamic health monitoring (DDDHM) method is presented. The main idea of DDDHM is to employ a robust learning machine robust least square support vector regression (LSSVR) to monitor quality of electronic system. As to obtain more flexibility, which benefit for getting more support vector, here two-kernel function is employed. Moreover, roust LSSVR can deal with the data set under the disturbance for its robust property, Numerical experiment reveal the validity of the proposed DDDHM.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 25th International Symposium on Industrial Electronics, ISIE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1006-1010
Number of pages5
ISBN (Electronic)9781509008735
DOIs
StatePublished - 17 Nov 2016
Externally publishedYes
Event25th IEEE International Symposium on Industrial Electronics, ISIE 2016 - Santa Clara, United States
Duration: 8 Jun 201610 Jun 2016

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2016-November

Conference

Conference25th IEEE International Symposium on Industrial Electronics, ISIE 2016
Country/TerritoryUnited States
CitySanta Clara
Period8/06/1610/06/16

Keywords

  • DDDHM
  • Data driven
  • Dynamic health monitoring
  • Electronic system

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