Share Email Print
cover

Proceedings Paper

Testing the analog processor of a structural neural system
Author(s): G. R. Kirikera; I. Kang; J. W. Lee; V. Shinde; B. Westheider; Vesselin N. Shanov; M. J. Schulz; M. Sundaresan; A. Ghoshal
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Structural Health Monitoring ideally would check the health of the structure in real time all the time. Simplifying the sensor system and the data acquisition equipment plays a very important role in achieving this goal. This paper discusses a practical technique that uses long continuous sensors and biomimetic signal processing to simplify health monitoring. The testing of a structural neural system with an updated analog processor module is discussed in this paper. A neuron is formed by connecting sensor elements to an analog processor. The structural neural system is formed by connecting multiple neurons to mimic the signal processing architecture of the neural system of the human body. This approach reduces the required number of data acquisition channels and still predicts the location of damage within a grid of miniature neurons. Different types of sensors can also be used. A piezoelectric ribbon sensor can sense damage due to impacts or crack growth because these damages generate Lamb waves that are detected by the neural system. The neuron can also receive diagnostic waves generated to check the structure on demand and when it is not in operation. In addition, new continuous multi-wall carbon nanotube sensors are being used as strain and crack detection neurons that operate during both static and dynamic loading. In general, the Structural Neural System may provide an advantage for the continuous monitoring of most large sensor systems in which anomalous events must be detected, and where it is impractical to have a separate channel of data acquisition for each sensor. Moreover, the data reduction technique and damage detection algorithm are easy to understand, simple to implement, reliable, and many sensor types can be used.

Paper Details

Date Published: 16 May 2005
PDF: 12 pages
Proc. SPIE 5763, Smart Structures and Materials 2005: Smart Electronics, MEMS, BioMEMS, and Nanotechnology, (16 May 2005); doi: 10.1117/12.600243
Show Author Affiliations
G. R. Kirikera, Univ. of Cincinnati (United States)
I. Kang, Univ. of Cincinnati (United States)
J. W. Lee, Korea Institute of Machinery and Materials (South Korea)
V. Shinde, Univ. of Cincinnati (United States)
B. Westheider, Univ. of Cincinnati (United States)
Vesselin N. Shanov, Univ. of Cincinnati (United States)
M. J. Schulz, Univ. of Cincinnati (United States)
M. Sundaresan, North Carolina A&T State Univ. (United States)
A. Ghoshal, United Technologies Research Ctr. (United States)


Published in SPIE Proceedings Vol. 5763:
Smart Structures and Materials 2005: Smart Electronics, MEMS, BioMEMS, and Nanotechnology
Vijay K. Varadan, Editor(s)

© SPIE. Terms of Use
Back to Top