NDLI: Use of artificial neural networks for optimal sensing in complex structures analysis

Content Provider	IEEE Xplore Digital Library
Author	Savastano, M. Lecce, L. Marulo, F. Sansone, C.
Copyright Year	1994
Description	Author affiliation: Lab. of Adv. Electr. Metrology, CNR, Napoli, Italy (Savastano, M.)
Abstract	Among the advantages offered by the Artificial Neural Networks (ANNs), in the analysis and active control of structures characterized by high modal densities and complexity, it should be mentioned the possibility of optimizing the number and the position of sensors and actuators. This feature can result in a sensible reduction of cost in the analysis and control of large structures and could be of great advantage when some of the points under investigation of the structure are not physically accessible. The first step of research is the validation of the ANN approach to a satisfactory analysis of the structure. Previous papers have highlighted the encouraging results obtained from an ANN implementation for the description of a stiffened aluminum panel behaviour. In that case the ANN was trained with data obtained by a validated FEM (Finite Element Method) structural model. In the simulation phase the ANN estimated the behaviour of all the grid points with data referred only to a portion of points with interesting performances in terms of accuracy and computing time. The present paper describes a measurement system in which the ANN is trained with real data, experimentally obtained by accelerometers. The investigation was carried out considering a sinusoidal excitation, typically produced by the rotating engines of turboprob aircrafts. Real data allowed us to rest the ability of the ANN to learn the structural dynamics taking into the right account the influence of noise, not considered in the previous phase of the research. The experimental results highlight again the good ANN behaviour accuracy in the estimation of all the rest points considered for the analysis. The present research is the necessary premise for ANNs based active control applications in aeronautics and aerospace.
Starting Page	127
Ending Page	130
File Size	421330
Page Count	4
File Format	PDF
ISBN	0780318803
DOI	10.1109/IMTC.1994.352108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-05-10
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Sensor phenomena and characterization Actuators Costs Aluminum Finite element methods Computational modeling Phase estimation Grid computing Accelerometers
Content Type	Text
Resource Type	Article

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