| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
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
| Organizations | Location | People |
|---|
article
A multivariate data analysis approach towards vibration analysis and vibration-based damage assessment
Abstract
This paper introduces a novel methodology for structural vibration analysis and vibration-based monitoring which utilises a special type of Principal Components Analysis (PCA), known as Singular Spectrum Analysis (SSA). In this study the methodology is introduced and demonstrated for the purposes of damage assessment in structures using their free decay response. The method's damage assessment properties are first demonstrated on a numerical example for a two degree-of-freedom (2DOF) spring-mass and damper system with non-linear stiffness. The method is then applied to an experimental case study of a composite laminate beam. The method is based on the decomposition of the frequency domain structural variation response using new variables, the Principal Components (PCs). Only a certain number of the new variables are used to approximate the original vibration signal with very good accuracy. The presented results demonstrate the potential of the method for vibration based signal reconstruction and damage diagnosis. The healthy and the different damaged scenarios are clearly distinguishable in the new space of only two reconstructed components where a strong clustering efect is observed.