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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Trendafilova, Irina
University of Strathclyde
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2019Triboelectric sensor as a dual system for impact monitoring and prediction of the damage in composite structurescitations
- 2019Detection and measurement of impacts in composite structures using a self-powered triboelectric sensorcitations
- 2018The effect of polycaprolactone nanofibers on the dynamic and impact behavior of glass fibre reinforcedcitations
- 2018Self-powered pressure sensor based on the triboelectric effect and its analysis using dynamic mechanical analysiscitations
- 2017Vibratory behaviour of glass fibre reinforced polymer (GFRP) interleaved with nylon nanofiberscitations
- 2017Delamination detection and growth assessment in composite laminated beams through data-driven vibration structural health monitoring
- 2017Delamination detection and growth assessment in composite laminated beams through data-driven vibration structural health monitoring
- 2016A study on the vibration-based self-monitoring capabilities of nano-enriched composite laminated beamscitations
- 2015Vibration-based delamination diagnosis and modelling for composite laminate platescitations
- 2015Damage assessment based on general signal correlationcitations
- 2015Damage assessment for wind turbine blades based on a multivariate statistical approachcitations
- 2014Delamination assessment in structures made of composites based on general signal correlationcitations
- 2014A multivariate data analysis approach towards vibration analysis and vibration-based damage assessmentcitations
- 2014An investigation in vibration modeling and vibration-based monitoring for composite laminates
- 2014Singular spectrum analysis for identifying structural nonlinearity using free-decay responses
- 2013Multivariate statistical analysis for damage and delamination in composite structures
- 2012A simple frequency-based delamination detection and localization method without baseline modelcitations
- 2012Delamination assessment in structures made of composites based on signal cross-correlation
- 2009Smart materials for intelligent structural health monitoring
Places of action
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document
Multivariate statistical analysis for damage and delamination in composite structures
Abstract
The article is devoted to the analysis of the vibration response of composite<br/>laminates .Our aim is to develop a method for analysis of the vibration response of structures made of composites which will also be used to develop a vibration-based health monitoring procedure for such structures. Composite materials and composite laminates in particular, exhibit complex dynamic behaviour which on most occasions cannot be modelled linearly. Delamination introduces additional nonlinearities in the vibration behaviour of the structure<br/>as a result of the interrupted contact between the layers or the opening and closing of the delamination. Thus conventional linear structural dynamics methods like modal analysis cannot be applied. In this study, the vibration response signals are recorded from damaged and non-damaged (healthy) laminated composite beams. The frequency domain signals are subjected to a special type of Principal Component Analysis, known as Multichannel Singular<br/>Spectrum Analysis (MSSA). This type of analysis is known to uncover oscillation patterns and was suggested in the investigation in place of modal analysis. The idea is to establish a new feature based state-space for the vibration response signal. The response of the healthy structure is used as a baseline to which all the responses are compared. MSSA decomposes the signal into new components which are lineal combinations of the original frequency series<br/>components. The first several components are responsible for most of the variance of the original signal. The new space is with a much smaller dimension as compared to the original data and creates new variables which can be used as damage features. The results demonstrate strong potential for using MSSA for the purpose of structural health monitoring.<br/>