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AN ARTIFICIAL NEURAL SYSTEM WITH DISTRIBUTED PARALLEL PROCESSING FOR STRUCTURAL HEALTH MONITORING
| Title: | AN ARTIFICIAL NEURAL SYSTEM WITH DISTRIBUTED PARALLEL PROCESSING FOR STRUCTURAL HEALTH MONITORING |
| Author: | KIRIKERA, GOUTHAM RAGHAVENDRA |
| Description: | There is a growing need for the development of in-situ continuous monitoring systems to allow the health monitoring of large structures and the rapid introduction of advanced high performance and heterogeneous materials and combinations of these materials into service. This thesis makes a contribution in the development of artificial neural systems for health monitoring of large and complex structures, and for impact location on targets. The artificial neural system is a passive monitoring system that can minimize the on board instrumentation needed for real-time health monitoring. The system uses highly-distributed interconnected sensor nodes and parallel processing that mimics the hierarchy of the biological neural system to collect dynamic strain signals caused by damage events. The dynamic strains can be in the form of high frequency waves called acoustic emissions caused by damage growth or lower frequency waves and vibration caused by impact to the structure. The artificial neural system processes these dynamic signals and provides an indication of the location and severity of the damage or impact. To verify the approach, an artificial neural system and wave propagation in the panel were modeled. Simulations of damage and impact in a glass fiber composite plate were performed in which the elastic response was computed in closed form at small time steps and the coupled piezoceramic constitutive equations and conductivity equations were also solved. Experimentation was then performed using a glass fiber composite panel and the simulation and experimental results were compared. These studies showed that the artificial neural system is a simultaneously sensitive to low frequency dynamic strains caused by structural vibrations and impact, as well as high frequency acoustic emission signals that accompany damage growth. An important advantage of this new approach is the application of inhibition and firing of the neurons that receive the damage signals. This allows a large reduction in the number of channels of data acquisition needed for health monitoring. Composite materials in particular can benefit from this new type of health monitoring system. Large structures like aircraft and submarines can be monitored simultaneously with a small number of data acquisition channels. |
| Permanent Link: |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1061292922
http://hdl.handle.net/2374.OX/12977 |
| Date: | 2003 |
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