Structural Health Monitoring of a Composite Wing Model Using Proper Orthogonal Decomposition

2007 ◽  
Author(s):  
Conner Shane ◽  
Ratneshwar Jha
Keyword(s):  
Structural Health Monitoring ◽  
Proper Orthogonal Decomposition ◽  
Health Monitoring ◽  
Orthogonal Decomposition ◽  
Structural Health ◽  
Wing Model ◽  
Proper Orthogonal ◽  
Composite Wing

scholarly journals Output-only Structural Health Monitoring of a Riveted Steel Railway Bridge utilizing Proper Orthogonal Decomposition, Artificial Neural Network, and Strain Measurements

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Ahmed Rageh ◽  
Saeed Eftekhar Azam ◽  
Daniel Linzell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Operational Conditions ◽  
Damage Scenarios ◽  
Structural Health ◽  
Damage Indices ◽  
Wide Range ◽  
Artificial Neural ◽  
Output Only ◽  
Proper Orthogonal

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the superstructure of this structural system is the stringer-to-floor beam connection failure, which was the focus of this study. However, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The damage feature adopted in this framework was the variations of Proper Orthogonal Modes (POMs) of the measured structural response. To automatically detect the occurrence, location, and intensity of deficiencies from the POMs, Artificial Neural Networks (ANN) were adopted. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. To alleviate the variability of POMs due to non-stationarity of the train loads, a preset windowing of measured output was completed in conjunction with automated peak-picking. Furthermore, input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent; a significant advancement. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLAB’s Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer-to-floor beam connections, measured signal amplitudes were artificially decreased at select locations. Finally, to validate the applicability of the proposed method using low-cost measurement devices, the measured signals were corrupted by high levels of white, Gaussian noises featuring spatial correlations. It was concluded that the proposed framework could successfully identify 20 damage indices, which were artificially imposed on measured signals under operational conditions.

Application of structural health monitoring techniques to composite wing panels

2019 ◽  
Vol 53 (25) ◽  
pp. 3515-3533
Author(s):  
Fulvio Romano ◽  
Monica Ciminello ◽  
Assunta Sorrentino ◽  
Umberto Mercurio
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Composite Structures ◽  
Low Frequency ◽  
Controlled Environment ◽  
Stiffened Panels ◽  
Health Monitoring System ◽  
Structural Health ◽  
Measurement Analysis ◽  
Composite Wing

This detailed study proposes a structural health monitoring system which enables the identification, localisation, and correct measurement analysis, in relation to the damage and debonding induced by low energy impacts within aircraft composite wing panels. The said system has been envisaged as an offline system which aims to be considered as a valid alternative method in relation to the current first two maintenance approach levels: visual inspection, which is to be followed if necessary by ultrasonic scanning techniques. The architecture includes two different technologies which act at different frequency ranges: high-frequency sensors/actuators piezoceramics and low-frequency distributed fiber optic sensors. Experimental and numerical results on small stiffened panels are illustrated in this study, where technological verification and validation have been assessed within a laboratory-controlled environment. In addition, the potential benefit by utilising such techniques within the design of the aircraft composite structures has also been illustrated; in comparison with the current aircraft composite structures, a higher weight saving and better performing structures is foreseen.

Structural health monitoring of composite wing structure during durability test

2007 ◽  
Vol 79 (1) ◽  
pp. 133-139 ◽  
Author(s):  
S. Takeda ◽  
Y. Aoki ◽  
T. Ishikawa ◽  
N. Takeda ◽  
H. Kikukawa
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Durability Test ◽  
Structural Health ◽  
Wing Structure ◽  
Composite Wing
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