Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling

Nikos Eleftheroglou; Dimitrios Zarouchas; T.H. Loutas; Rene Alderliesten; Rinze Benedictus

doi:10.4028/www.scientific.net/KEM.713.34

Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling

Nikos Eleftheroglou, Dimitrios Zarouchas, T.H. Loutas, Rene Alderliesten, Rinze Benedictus

Research output: Contribution to journal › Article › Scientific › peer-review

15 Citations (Scopus)

74 Downloads (Pure)

Abstract

The present study utilizes a state-of-the-art stochastic modeling with structural health monitoring (SHM) data derived from strain measurements, in order to assess the remaining useful life (RUL) online in composite materials under fatigue loading. Non-Homogenous Hidden Semi Markov model (NHHSMM) is a suitable candidate with a rich mathematical structure capable of describing the composite’s multi-state damage evolution in time. The proposed model uses as input SHM data in the form of strain measurements obtained from the Digital Image Correlation (DIC) technique to a coupon-level constant amplitude fatigue test campaign. The obtained from the stochastic model RUL estimations are compared with the actual RUL and the effectiveness of the prognosis is discussed.

Original language	English
Pages (from-to)	34-37
Journal	Key Engineering Materials
Volume	713
DOIs	https://doi.org/10.4028/www.scientific.net/KEM.713.34
Publication status	Published - 2016

Keywords

Composite materials
Fatigue
strain data
stochastic models

Access to Document

10.4028/www.scientific.net/KEM.713.34

FDM paper2Accepted author manuscript, 1.12 MB

Cite this

@article{b0bdd6cd9a7c458493075d623c637594,

title = "Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling",

abstract = "The present study utilizes a state-of-the-art stochastic modeling with structural health monitoring (SHM) data derived from strain measurements, in order to assess the remaining useful life (RUL) online in composite materials under fatigue loading. Non-Homogenous Hidden Semi Markov model (NHHSMM) is a suitable candidate with a rich mathematical structure capable of describing the composite{\textquoteright}s multi-state damage evolution in time. The proposed model uses as input SHM data in the form of strain measurements obtained from the Digital Image Correlation (DIC) technique to a coupon-level constant amplitude fatigue test campaign. The obtained from the stochastic model RUL estimations are compared with the actual RUL and the effectiveness of the prognosis is discussed. ",

keywords = "Composite materials, Fatigue, strain data, stochastic models",

author = "Nikos Eleftheroglou and Dimitrios Zarouchas and T.H. Loutas and Rene Alderliesten and Rinze Benedictus",

year = "2016",

doi = "10.4028/www.scientific.net/KEM.713.34",

language = "English",

volume = "713",

pages = "34--37",

journal = "Key Engineering Materials",

issn = "1013-9826",

publisher = "Trans Tech Publications",

}

TY - JOUR

T1 - Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling

AU - Eleftheroglou, Nikos

AU - Zarouchas, Dimitrios

AU - Loutas, T.H.

AU - Alderliesten, Rene

AU - Benedictus, Rinze

PY - 2016

Y1 - 2016

N2 - The present study utilizes a state-of-the-art stochastic modeling with structural health monitoring (SHM) data derived from strain measurements, in order to assess the remaining useful life (RUL) online in composite materials under fatigue loading. Non-Homogenous Hidden Semi Markov model (NHHSMM) is a suitable candidate with a rich mathematical structure capable of describing the composite’s multi-state damage evolution in time. The proposed model uses as input SHM data in the form of strain measurements obtained from the Digital Image Correlation (DIC) technique to a coupon-level constant amplitude fatigue test campaign. The obtained from the stochastic model RUL estimations are compared with the actual RUL and the effectiveness of the prognosis is discussed.

AB - The present study utilizes a state-of-the-art stochastic modeling with structural health monitoring (SHM) data derived from strain measurements, in order to assess the remaining useful life (RUL) online in composite materials under fatigue loading. Non-Homogenous Hidden Semi Markov model (NHHSMM) is a suitable candidate with a rich mathematical structure capable of describing the composite’s multi-state damage evolution in time. The proposed model uses as input SHM data in the form of strain measurements obtained from the Digital Image Correlation (DIC) technique to a coupon-level constant amplitude fatigue test campaign. The obtained from the stochastic model RUL estimations are compared with the actual RUL and the effectiveness of the prognosis is discussed.

KW - Composite materials

KW - Fatigue

KW - strain data

KW - stochastic models

UR - http://resolver.tudelft.nl/uuid:b0bdd6cd-9a7c-4584-9307-5d623c637594

U2 - 10.4028/www.scientific.net/KEM.713.34

DO - 10.4028/www.scientific.net/KEM.713.34

M3 - Article

SN - 1013-9826

VL - 713

SP - 34

EP - 37

JO - Key Engineering Materials

JF - Key Engineering Materials

ER -

Online Remaining Fatigue Life Prognosis for Composite Materials Based on Strain Data and Stochastic Modeling

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this