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

被引：69

作者：

Eleftheroglou, Nick ^{[1
]}

Zarouchas, Dimitrios ^{[1
]}

Loutas, Theodoros ^{[2
]}

Alderliesten, Rene ^{[1
]}

Benedictus, Rinze ^{[1
]}

机构：

[1] Delft Univ Technol, Aerosp Engn Fac, Struct Integr & Composites Grp, NL-2629 HS Delft, Netherlands

[2] Univ Patras, Dept Mech Engn & Aeronaut, Appl Mech Lab, Rion 26500, Greece

来源：

RELIABILITY ENGINEERING & SYSTEM SAFETY | 2018年 / 178卷

关键词：

Data fusion; Remaining useful life; Prognostic performance metrics; Structural health monitoring; Composite structures; FATIGUE LIFE; FRAMEWORK; PREDICTION; POWER;

D O I：

10.1016/j.ress.2018.04.031

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：40 / 54

页数：15

共 39 条

[1]

[Anonymous], 2008, 62 M SOC MFPT

[2]

[Anonymous], 2009, ANN C PROGN HLTH MAN

[3]

[Anonymous], J INTELL MAT SYST ST

[4] A unified interpretation of the power laws in fatigue and the analytical correlations between cyclic properties of engineering materials [J].

Carpinteri, Alberto ;

Paggi, Marco .

INTERNATIONAL JOURNAL OF FATIGUE, 2009, 31 (10) :1524-1531

[5] Bayesian model selection and parameter estimation for fatigue damage progression models in composites [J].

Chiachio, J. ;

Chiachio, M. ;

Saxena, A. ;

Sankararaman, S. ;

Rus, G. ;

Goebel, K. .

INTERNATIONAL JOURNAL OF FATIGUE, 2015, 70 :361-373

[6] Condition-based prediction of time-dependent reliability in composites [J].

Chiachio, Juan ;

Chiachio, Manuel ;

Sankararaman, Shankar ;

Saxena, Abhinav ;

Goebel, Kai .

RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 142 :134-147

[7] A Bayesian framework for fatigue life prediction of composite laminates under co-existing matrix cracks and delamination [J].

Corbetta, Matteo ;

Sbarufatti, Claudio ;

Giglio, Marco ;

Saxena, Abhinav ;

Goebel, Kai .

COMPOSITE STRUCTURES, 2018, 187 :58-70

[8] Damage quantification in polymer composites using a hybrid NDT approach [J].

Cuadra, Jefferson ;

Vanniamparambil, Prashanth A. ;

Hazeli, Kavan ;

Bartoli, Ivan ;

Kontsos, Antonios .

COMPOSITES SCIENCE AND TECHNOLOGY, 2013, 83 :11-21

[9] FAILURE PROGNOSTICS BY A DATA-DRIVEN SIMILARITY-BASED APPROACH [J].

Di Maio, Francesco ;

Zio, Enrico .

INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING, 2013, 20 (01)

[10]

Elattar HM, 2016, COMPLEX INTELL SYST, V2, P125, DOI 10.1007/s40747-016-0019-3

← 1 2 3 4 →