The initiation and propagation of damage in composite laminates generate Acoustic Emission. The use of
real time AE monitoring has been quite extensive for in-service composite structures. In the present work,
experimental and numerical studies were performed to characterize the acoustic wave propagation in thin
glass/epoxy composite plates. Experimentally obtained and simulated emission signals were used to
identify and locate the source of the acoustic wave. Signal processing algorithms and a passive damage
diagnosis system based on AE techniques were proposed for continuously monitoring and assessing the
structural health of composite laminates. The local sensing and distributed processing features of the sensor
system result in a decreased demand for bandwidth and lower computational power needed at each node.
The study of the mechanical interaction among the host, interface, and a device embedded within a laminated composite
is important. Embedding micro-sensors in composite laminates produces material discontinuity around the inclusions.
This in turn produces stress concentrations at or near the inclusions. Both 2D plane strain and 3D FEM models are
developed to analyze the stress/strain state surrounding the embedded micro-sensors within a unidirectional composite
laminate. The objective of the present numerical effort is to take into account the observed resin-rich areas caused by
embedment, and to determine their effects on the local stress field around the embedment and the corresponding
potential failure modes.
The response of a structure is usually modified when the structure is damaged, especially in the vicinity of the damaged zones. Such local perturbations are generally very small but they can be detected using wavelet transform techniques. To this end, a distributed two-dimensional Continuous Wavelet Transform (2D CWT) algorithm is proposed that can use data from discrete sets of nodes and provide spatially continuous variation in the structural response parameters that are used to monitor structural degradation. Combined with an embedded sensor network to provide nodal response signals, this algorithm has potential for Structural Health Monitoring (SHM). The advantageous features of this algorithm are its reliance on local data, its ability to yield spatially continuous information, and its limited communication and computation requirements.