Symbolic dynamics and analysis of time series data for diagnostics of a dc-dc forward converter

Gregory M. Bower, Jeffrey Mayer, Karl Martin Reichard

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

3 Scopus citations

Abstract

This paper presents a novel approach to diagnosis of dc-dc converters with application to prognosis. The methodology is based on Symbolic Dynamics and Diagnostics. The data derived method builds a statistical baseline of the converter that is used to compare future statistical models of the converter as it degrades. Methods to determine the partitioning and number of partitions for the Symbolic Dynamics algorithm are discussed. In addition, a failure analysis is performed on a dc-dc forward converter to identify components with a high probability of failure. These components are then chosen to be monitored during accelerated testing of the dc-dc forward converter. The methodology is experimentally validated with data recorded from two dc-dc converters under accelerated life testing.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society 2011, PHM 2011
EditorsAbhinav Saxena, Sankalita Saha, Jose R. Celaya
PublisherPrognostics and Health Management Society
Pages469-478
Number of pages10
ISBN (Electronic)9781936263035
StatePublished - Jan 1 2014
Event2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011 - Montreal, Canada
Duration: Sep 25 2011Sep 29 2011

Other

Other2011 Annual Conference of the Prognostics and Health Management Society, PHM 2011
CountryCanada
CityMontreal
Period9/25/119/29/11

All Science Journal Classification (ASJC) codes

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

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