Paper
1 August 2003 Recent advances in an artificial neural system for structural health monitoring
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Abstract
This paper discusses recent advances in modeling and simulation of an artificial neural system and simulation of wave propagation for designing structural health monitoring systems. An artificial neural system was modeled using piezoceramic nerves and electronic components. Wave propagation in a panel is modeled using classical plate theory and a closed-form solution of wave propagation and reflection is obtained. Equations representing a half sine input similar to a projectile impact or a tone burst excitation were added to the existing algorithm that predicts the response of the artificial neural system due to impulse inputs. Firing switches have been modeled in the simulation to predict the sequential firing of the neurons as the waves pass over them. Also, new active fiber sensors have been designed for use in the artificial neural system. Simulated responses of the artificial neural system are shown in this paper and indicate that large neural systems can be formed with hundreds of sensor nodes. Experiments were performed to study a small neural system on a glass fiber panel. Waves were induced in the panel due to a lead break to simulate a crack and due to an impact from an impact hammer. Testing showed the location of a crack could be determined within the unit cell of the neural system for an orthotropic panel.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Goutham R. Kirikera, Saurabh Datta, Mark J. Schulz, Anindya Ghoshal, Mannur J. Sundaresan, Jeff Feaster, and Derke Hughes "Recent advances in an artificial neural system for structural health monitoring", Proc. SPIE 5046, Nondestructive Evaluation and Health Monitoring of Aerospace Materials and Composites II, (1 August 2003); https://doi.org/10.1117/12.484118
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Cited by 15 scholarly publications.
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KEYWORDS
Sensors

Neurons

Wave propagation

Acoustic emission

Structural health monitoring

Ferroelectric materials

Systems modeling

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