Prognostics of composite structures utilizing structural health monitoring data fusion

Nick Eleftheroglou; Dimitrios Zarouchas; Rene Alderliesten; Rinze Benedictus; Theodoros Loutas

Prognostics of composite structures utilizing structural health monitoring data fusion

Nick Eleftheroglou, Dimitrios Zarouchas, Rene Alderliesten, Rinze Benedictus, Theodoros Loutas

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (SciVal)

Abstract

A new structural health monitoring (SHM) data fusion methodology is proposed in order to produce features with strong prognostic capability. The Non-Homogenous Hidden Semi Markov model (NHHSMM) is utilized to estimate the remaining useful life (RUL) of composite structures using conventional as well as fused SHM data. The proposed data fusion methodology and NHHSMM are applied to open hole carbon/epoxy specimens under fatigue loading. During the specimens’ lifetime, acoustic emission (AE) and digital image correlation (DIC) techniques were used in order to capture the inherently dynamic and multi-scale character of damage. This work investigates the prognostic performance of the fused data in comparison to the performance of the conventional AE and DIC data. Prognostic performance metrics were used and as it was found fused data has potential to provide better prognostics.

Original language	English
Title of host publication	9th European Workshop on Structural Health Monitoring, EWSHM 2018
Number of pages	12
Publication status	Published - 2018
Event	9th European Workshop on Structural Health Monitoring, EWSHM 2018 - Manchester, United Kingdom Duration: 10 Jul 2018 → 13 Jul 2018

Conference

Conference	9th European Workshop on Structural Health Monitoring, EWSHM 2018
Country/Territory	United Kingdom
City	Manchester
Period	10/07/18 → 13/07/18

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@inproceedings{b4f285bfe0ea4f12814a94756126de1e,

title = "Prognostics of composite structures utilizing structural health monitoring data fusion",

abstract = "A new structural health monitoring (SHM) data fusion methodology is proposed in order to produce features with strong prognostic capability. The Non-Homogenous Hidden Semi Markov model (NHHSMM) is utilized to estimate the remaining useful life (RUL) of composite structures using conventional as well as fused SHM data. The proposed data fusion methodology and NHHSMM are applied to open hole carbon/epoxy specimens under fatigue loading. During the specimens{\textquoteright} lifetime, acoustic emission (AE) and digital image correlation (DIC) techniques were used in order to capture the inherently dynamic and multi-scale character of damage. This work investigates the prognostic performance of the fused data in comparison to the performance of the conventional AE and DIC data. Prognostic performance metrics were used and as it was found fused data has potential to provide better prognostics.",

author = "Nick Eleftheroglou and Dimitrios Zarouchas and Rene Alderliesten and Rinze Benedictus and Theodoros Loutas",

year = "2018",

language = "English",

booktitle = "9th European Workshop on Structural Health Monitoring, EWSHM 2018",

note = "9th European Workshop on Structural Health Monitoring, EWSHM 2018 ; Conference date: 10-07-2018 Through 13-07-2018",

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TY - GEN

T1 - Prognostics of composite structures utilizing structural health monitoring data fusion

AU - Eleftheroglou, Nick

AU - Zarouchas, Dimitrios

AU - Alderliesten, Rene

AU - Benedictus, Rinze

AU - Loutas, Theodoros

PY - 2018

Y1 - 2018

N2 - A new structural health monitoring (SHM) data fusion methodology is proposed in order to produce features with strong prognostic capability. The Non-Homogenous Hidden Semi Markov model (NHHSMM) is utilized to estimate the remaining useful life (RUL) of composite structures using conventional as well as fused SHM data. The proposed data fusion methodology and NHHSMM are applied to open hole carbon/epoxy specimens under fatigue loading. During the specimens’ lifetime, acoustic emission (AE) and digital image correlation (DIC) techniques were used in order to capture the inherently dynamic and multi-scale character of damage. This work investigates the prognostic performance of the fused data in comparison to the performance of the conventional AE and DIC data. Prognostic performance metrics were used and as it was found fused data has potential to provide better prognostics.

AB - A new structural health monitoring (SHM) data fusion methodology is proposed in order to produce features with strong prognostic capability. The Non-Homogenous Hidden Semi Markov model (NHHSMM) is utilized to estimate the remaining useful life (RUL) of composite structures using conventional as well as fused SHM data. The proposed data fusion methodology and NHHSMM are applied to open hole carbon/epoxy specimens under fatigue loading. During the specimens’ lifetime, acoustic emission (AE) and digital image correlation (DIC) techniques were used in order to capture the inherently dynamic and multi-scale character of damage. This work investigates the prognostic performance of the fused data in comparison to the performance of the conventional AE and DIC data. Prognostic performance metrics were used and as it was found fused data has potential to provide better prognostics.

UR - http://www.scopus.com/inward/record.url?scp=85070936617&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85070936617

BT - 9th European Workshop on Structural Health Monitoring, EWSHM 2018

T2 - 9th European Workshop on Structural Health Monitoring, EWSHM 2018

Y2 - 10 July 2018 through 13 July 2018

ER -

Prognostics of composite structures utilizing structural health monitoring data fusion

Abstract

Conference

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