Structural Health Monitoring Using Neural Network Based Vibrational System Identification

TL;DR

This paper presents a neural network approach for structural health monitoring, enabling detection of internal defects in smart composite structures through vibrational system identification.

Contribution

It introduces a neural network-based method for modeling and analyzing dynamic data to recognize structural defects in composite materials.

Findings

Effective detection of delaminations and structural degradation

Adaptive system providing real-time structural integrity assessment

Applicable to various composite structural components

Abstract

Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integrity for composite structures.