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Proceedings Paper

Neural system for structural health monitoring
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Paper Abstract

This is an overview paper that discusses the concept of an embeddable structural health monitoring system for use in composite and heterogeneous material systems. The sensor system is formed by integrating groups of autonomous unit cells into a structure, much like neurons in biological systems. Each unit cell consists of an embedded processor and a group of distributed sensors that gives the structure the ability to sense damage. In addition, each unit cell periodically updates a central processor on the status of health in its neighborhood. This micro-architectured synthetic nervous system has an advanced sensing capability based on new continuous sensor technology. This technology uses a plurality of serially connected piezoceramic nodes to form a distributed sensor capable of measuring waves generated in structures by damage events, including impact and crack propagation. Simulations show that the neural system can detect faint acoustic waves in large plates. An experiment demonstrates the use of a simple neural system that was able to measure simulated acoustic emissions that were not clearly recognizable by a single conventional piezoceramic sensor.

Paper Details

Date Published: 6 August 2001
PDF: 12 pages
Proc. SPIE 4328, Smart Structures and Materials 2001: Sensory Phenomena and Measurement Instrumentation for Smart Structures and Materials, (6 August 2001); doi: 10.1117/12.435516
Show Author Affiliations
Mannur J. Sundaresan, North Carolina A&T State Univ. (United States)
Mark J. Schulz, North Carolina A&T State Univ. (United States)
Anindya Ghoshal, North Carolina A&T State Univ. (United States)
William N. Martin, North Carolina A&T State Univ. (United States)
Promod R. Pratap, Univ. of North Carolina/Greensboro (United States)


Published in SPIE Proceedings Vol. 4328:
Smart Structures and Materials 2001: Sensory Phenomena and Measurement Instrumentation for Smart Structures and Materials
Eric Udd; Daniele Inaudi, Editor(s)

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