Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach

Jiajie Fan, Kam Chuen Yung, Michael Pecht

Research output: Journal article publicationJournal articleAcademic researchpeer-review

55 Citations (Scopus)

Abstract

Lumen degradation is a common failure mode in LED light sources. Lumen maintenance life, defined as the time when the maintained percentages of the initial light output fall below a failure threshold, is a key characteristic for assessing the reliability of LED light sources. Owing to the long lifetime and high reliability of LED lights sources, it is challenging to estimate the lumen maintenance life for LED light sources using traditional life testing that records failure data. This paper describes a particle filter-based (PF-based) prognostic approach based on both Sequential Monte Carlo (SMC) and Bayesian techniques to predict the lumen maintenance life of LED light sources. The lumen maintenance degradation data collected from an accelerated degradation test was used to demonstrate the prediction algorithm and methodology of the proposed PF approach. Its feasibility and prediction accuracy were then validated and compared with the TM-21 standard method that was created by the Illuminating Engineering Society of North America (IESNA). Finally, a robustness study was also conducted to analyze the initialization of parameters impacting the prediction accuracy and the uncertainties of the proposed PF method. The results show that, compared to the TM-21 method, the PF approach achieves better prediction performance, with an error of less than 5% in predicting the long-term lumen maintenance life of LED light sources.
Original languageEnglish
Pages (from-to)2411-2420
Number of pages10
JournalExpert Systems with Applications
Volume42
Issue number5
DOIs
Publication statusPublished - 1 Apr 2015

Keywords

  • LED light sources
  • Lumen maintenance
  • Particle filter
  • Prognostics
  • Remaining useful life
  • TM-21 standard

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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