Structural health monitoring data fusion for in-situ life prognosis of composite structures

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Abstract

A novel framework to fuse structural health monitoring (SHM) data from different in-situ monitoring techniques is proposed aiming to develop a hyper-feature towards more effective prognostics. A state-of-the-art Non-Homogenous Hidden Semi Markov Model (NHHSMM) is utilized to model the damage accumulation of composite structures, subjected to fatigue loading, and estimate the remaining useful life (RUL) using conventional as well as fused SHM data. Acoustic Emission (AE) and Digital Image Correlation (DIC) are the selected in-situ SHM techniques. The proposed methodology is applied to open hole carbon/epoxy specimens under fatigue loading. RUL estimations utilizing features extracted from each SHM technique and after data fusion are compared, via established and newly proposed prognostic performance metrics.
Original languageEnglish
Pages (from-to)40-54
Number of pages15
JournalReliability Engineering and System Safety
Volume178
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • Composite structures
  • Data fusion
  • Prognostic performance metrics
  • Remaining useful life
  • Structural health monitoring

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