A novel approach towards fatigue damage prognostics of composite materials utilizing SHM data and stochastic degradation modeling

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Abstract

A prognostic framework is proposed in order to estimate the remaining useful life of composite materials under fatigue loading based on acoustic emission data and a sophisticated Non Homogenous Hidden Semi Markov Model. Bayesian neural networks are also utilized as an alternative machine learning technique for the non-linear regression task. A comparison between the two algorithms operation, input, output and performance highlights their ability to tackle the prognostic task.
Original languageEnglish
Title of host publication8th European Workshop on Structural Health Monitoring
Subtitle of host publicationBilbao, Spain
PublisherNDT.net
Pages859-868
Number of pages10
Volume2
ISBN (Print)9781510827936
Publication statusPublished - 2016
Event8th European Workshop on Structural Health Monitoring - Bilbao, Spain
Duration: 5 Jul 20168 Jul 2016
Conference number: 8
http://www.ndt.net/events/EWSHM2016/app/content/

Conference

Conference8th European Workshop on Structural Health Monitoring
Abbreviated titleEWSHM 2016
Country/TerritorySpain
CityBilbao
Period5/07/168/07/16
Internet address

Keywords

  • SHM
  • remaining useful life
  • machine learning techniques
  • composite materials
  • acoustic emission

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