Recent advances in an artificial neural system for structural health monitoring

2003 ◽  
Author(s):  
Goutham R. Kirikera ◽  
Saurabh Datta ◽  
Mark J. Schulz ◽  
Anindya Ghoshal ◽  
Mannur J. Sundaresan ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Neural System ◽  
Structural Health ◽  
Recent Advances ◽  
Artificial Neural

An Artificial Neural Receptor System for Structural Health Monitoring

2005 ◽  
Vol 4 (3) ◽  
pp. 229-245 ◽  
Author(s):  
W. N. Martin ◽  
A. Ghoshal ◽  
M. J. Sundaresan ◽  
G. L. Lebby ◽  
P. R. Pratap ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Receptor System ◽  
Structural Health ◽  
Artificial Neural

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Sign in / Sign up

Export Citation Format

Share Document