Imperial College London
Portrait Picture

M H Ferri Aliabadi

Faculty of Engineering, Department of Aeronautics

Chair in Aerostructures
 
 
 
//

Contact

 

+44 (0)20 7594 5077m.h.aliabadi

 
 
//

Assistant

 

Miss Lisa Kelly +44 (0)20 7594 5056

 
//

Location

 

CAGB323City and Guilds BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Seno:2019:10.1016/j.ymssp.2019.04.023,
author = {Seno, AH and Sharif, Khodaei Z and Aliabadi, MHF},
doi = {10.1016/j.ymssp.2019.04.023},
journal = {Mechanical Systems and Signal Processing},
pages = {20--36},
title = {Passive sensing method for impact localisation in composite plates under simulated environmental and operational conditions},
url = {http://dx.doi.org/10.1016/j.ymssp.2019.04.023},
volume = {129},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A novel feature extraction method is developed for impact localisation based on Artificial Neural Networks (ANNs) in sensorized composite structures subjected to environmental and operational conditions. Impact induced lamb waves are investigated for the first time for different impact scenarios (angle, mass and energy) on flat and curved plates under environmental (temperature range) and operational (vibration) conditions. The Time of Arrival (TOA) is significantly influenced by these conditions hence complicating the impact localisation. To overcome this complication, a novel and robust TOA extraction method is proposed. It is based on Normalised Smoothed Envelope Threshold (NSET) coupled with a high pass filter to remove vibration noise prior to TOA extraction. Localisation ANNs were trained with data from a single baseline impact condition and were tested under impacts with varying conditions. It was shown that by using the proposed method for TOA extraction, the trained ANN is able to better predict the location of impacts compared to an ANN trained with data from common TOA extraction methods (detection area 0.99–56.08% of sensing region versus 0.28–1.55% for NSET). The developed method gives consistent accuracy and significantly reduces the required training data, making ANN based impact localisation more feasible for real life application.
AU  - Seno,AH
AU  - Sharif,Khodaei Z
AU  - Aliabadi,MHF
DO  - 10.1016/j.ymssp.2019.04.023
EP  - 36
PY  - 2019///
SN  - 0888-3270
SP  - 20
TI  - Passive sensing method for impact localisation in composite plates under simulated environmental and operational conditions
T2  - Mechanical Systems and Signal Processing
UR  - http://dx.doi.org/10.1016/j.ymssp.2019.04.023
UR  - http://hdl.handle.net/10044/1/69907
VL  - 129
ER  -
Download