TY  - CHAP
A1  - Neu, Eugen
A1  - Janser, Frank
A1  - Khatibi, Akbar A.
A1  - Orifici, Adrian C.
T1  - In-flight vibration-based structural health monitoring of aircraft wings
T2  - 30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea
N2  - This work presents a methodology for automated
damage-sensitive feature extraction and anomaly
detection under multivariate operational variability
for in-flight assessment of wings. The
method uses a passive excitation approach, i. e.
without the need for artificial actuation. The
modal system properties (natural frequencies and
damping ratios) are used as damage-sensitive
features. Special emphasis is placed on the use
of Fiber Bragg Grating (FBG) sensing technology
and the consideration of Operational and
Environmental Variability (OEV). Measurements
from a wind tunnel investigation with a composite
cantilever equipped with FBG and piezoelectric
sensors are used to successfully detect an impact
damage. In addition, the feasibility of damage
localisation and severity estimation is evaluated
based on the coupling found between damageand
OEV-induced feature changes.
Y1  - 2016
ER  - 
TY  - JOUR
A1  - Neu, Eugen
A1  - Janser, Frank
A1  - Khatibi, Akbar A.
A1  - Orifici, Adrian C.
T1  - Fully Automated Operational Modal Analysis using multi-stage clustering
JF  - Mechanical Systems and Signal Processing
Y1  - 2017
U6  - http://dx.doi.org/10.1016/j.ymssp.2016.07.031
SN  - 0888-3270
VL  - Vol. 84, Part A
SP  - 308
EP  - 323
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Neu, Eugen
A1  - Janser, Frank
A1  - Khatibi, Akbar A.
A1  - Orifici, Adrian C.
T1  - Automated modal parameter-based anomaly detection under varying wind excitation
JF  - Structural Health Monitoring
N2  - Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.
Y1  - 2016
U6  - http://dx.doi.org/10.1177/1475921716665803
SN  - 1475-9217
VL  - 15
IS  - 6
SP  - 1
EP  - 20
PB  - Sage
CY  - London
ER  -