This paper proposes a continuous real-time structural health monitoring (SHM) and damage detection system. Wavelet
packet decomposition (WPD) with a likelihood ratio was used for the damage sensitive indicator (DSI). A benchmark model updating structure instrumented with acceleration sensors was used for demonstration tests. A real-time reference-free damage detection algorithm is successfully implemented and verified using the benchmark test structure. The DSI based on WPD with a likelihood ratio algorithm showed consistency with two different damage scenarios. Furthermore, the application was web-published on a remote collaboratory site for remote access to a real-time structural health monitoring system.
KEYWORDS: Stochastic processes, Systems modeling, Data modeling, Genetic algorithms, Mathematical modeling, Vibrometry, Structural health monitoring, Manufacturing, Process modeling, Statistical modeling
In this paper, we present a new stochastic model updating methodology to identify spatially varying material properties
based on experimental data. This data is typically obtained from ambient or forced vibration measurements. For this
purpose, a linear elastic property is modeled as a random field. Stochastic properties of the random field are quantified
using an exponential covariance kernel. In order to combine the stochasticity with a Galerkin numerical model of a
structure, the covariance kernel is discretized using the Karhunen-Loeve (KL) expansion. In the KL expansion, the
covariance kernel is decomposed in the spectral domain by numerically solving a homogeneous Fredholm integral
equation. For model updating, stochastic parameters are updated so that the Galerkin model provides dynamic properties
matching with realizations that have been identified in experiments. In this paper, Gaussian realizations for varying
properties have been employed for the corresponding reference model. This proposed method demonstrates its ability to
identify the updating stochastic properties.
In this paper, a low-cost digital image correlation-based constitutive sensor with a novel identification algorithm that is
deployable and scalable in the field is proposed. The term 'constitutive sensor' is coined herein to describe a sensor that
is capable of determining the target material constitutive parameters. The proposed method is different from existing
identification methods in that it does not need to solve boundary value problems of the target materials using updated
parameters. Since the development of the digital image correlation (DIC) technique in the 1980s, the DIC technique has
been broadly evaluated and improved for measuring full-field displacements of test specimens, mainly in laboratory
settings. Although its potential in damage and mechanical identification is immense, the high cost of current commercial
DIC systems makes it difficult to apply the DIC technique to in-field health monitoring of structures. To realize a first
ever application of DIC in the field, a prototypical low-cost sensing unit consisting of a high performance embedded
microprocessor board, a low-cost web camera, and a communication module is suggested. In the proposed constitutive
sensor, DIC displacement fields considered as true values are used in computing stress fields satisfying the equilibrium
condition and strain fields using finite element concepts. The unknown constitutive law is initially assumed to be fully
anisotropic and linear elastic. A steady state genetic algorithm is utilized to search for the material parameters by
minimizing a cost function that measures energy residuals. The main features that allow the sensor to be deployable in
the field are introduced, and a validation of the proposed constitutive sensor concept using synthetic data is presented.
In this paper, a novel vibration-based methodology for fast inverse identification of delamination in E-glass/epoxy
composite panels has been proposed with experimental demonstration using a scanning laser vibrometer (SLV). The
methodology consists of 1) a parameter subset selection for delamination damage localization and 2) iterative inverse
eigenvalue analysis for damage quantification. It can potentially lead to a functional formulation relating spatial and
global damage indices such as curvature damage factor to local damage parameters. The functional relationship will be
suitable to fast or real-time in-situ delamination damage identification. To accomplish the objectives, a shear-locking
free higher-order finite element model has been combined with a micromechanics theory-based continuum damage
model as an identification model for locating delamination. Applications of the proposed methodology to an Eglass/
epoxy panel [CSM/UM1208/3 layers of C1800]s = [CSM/0/(90/0)3]s with delamination have been demonstrated
both numerically and experimentally using a piezoelectric actuator, a PVDF sensor and non-contact measuring SLV.
Experimental modal analysis has been successfully conducted using the sample specimen to demonstrate the proposed
methodology.
In this paper, a wavelet entropy based damage identification method is experimentally validated using wireless smart
sensor units (Imote2) with TinyOS-based firmware. Recently, the wireless smart sensor network has drawn significant
attention for applications in Structural Health Monitoring (SHM). Wavelet entropy is considered to be a damagesensitive
signature that can be obtained both at different spatial locations and time stations to indicate changes in
dynamic responses of structures. Compared to metrics based on the Fourier Transform, metrics based on wavelets
require much simpler mathematics, with no complex numbers. Thus wavelet-based SHM methods would be easier to
embed on motes. Wavelets can have other (mathematical) advantages when the structures are complex and the dynamic
signals are non-stationary. Particularly, use of the relative wavelet entropy (RWE) has been extensively explored for use
in damage detection using wireless smart sensors. First, sensor validation tests have been conducted using wireless and
wired sensors. To verify an off-line time synchronization technique and the feasibility of using acceleration data from
wireless sensors, modal identifications have been conducted using the ERA technique. Finally, the wavelet entropy
based damage detection method has been demonstrated using Imote2 wireless smart sensors.
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