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Compressed sensing techniques for detecting damage in structures

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Structural Health Monitoring
Authors:
David Mascareñas
David Mascareñas
David Mascareñas
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Alessandro Cattaneo
Alessandro Cattaneo
Alessandro Cattaneo
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James Theiler at Los Alamos National Laboratory
James Theiler
Charles Farrar at Los Alamos National Laboratory
Charles Farrar
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Abstract and Figures

One of the principal challenges facing the structural health monitoring community is taking large, heterogeneous sets of data collected from sensors, and extracting information that allows the estimation of the damage condition of a structure. Another important challenge is to collect relevant data from a structure in a manner that is cost-effective, and respects the size, weight, cost, energy consumption and bandwidth limitations placed on the system. In this work, we established the suitability of compressed sensing to address both challenges. A digital version of a compressed sensor is implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is tested in a surrogate SHM application using acceleration measurements. Currently, the prototype compressed sensor is capable of collecting compressed coefficients from measurements and sending them to an off-board processor for signal reconstruction using ℓ1 norm minimization. A compressed version of the matched filter known as the smashed filter has also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.
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Article
Structural Health Monitoring
12(4) 325–338
ÓThe Author(s) 2013
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DOI: 10.1177/1475921713486164
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Compressed sensing techniques for
detecting damage in structures
David Mascaren
˜as
1
, Alessandro Cattaneo
2
,
James Theiler
3
and Charles Farrar
1
Abstract
One of the principal challenges facing the structural health monitoring community is taking large, heterogeneous sets of
data collected from sensors, and extracting information that allows the estimation of the damage condition of a struc-
ture. Another important challenge is to collect relevant data from a structure in a manner that is cost-effective, and
respects the size, weight, cost, energy consumption and bandwidth limitations placed on the system. In this work, we
established the suitability of compressed sensing to address both challenges. A digital version of a compressed sensor is
implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is
tested in a surrogate SHM application using acceleration measurements. Currently, the prototype compressed sensor is
capable of collecting compressed coefficients from measurements and sending them to an off-board processor for signal
reconstruction using ‘
1
norm minimization. A compressed version of the matched filter known as the smashed filter has
also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.
Keywords
Compressed sensing, Structural Health Monitoring, sparse modeling, low-power sensing
Introduction
Data for structural health monitoring (SHM) applica-
tions are generally collected using a distributed sensor
network. Distributed sensor networks made up of
nodes with hard-wired data and communication lines
generally have high installation costs, particularly in the
retrofit mode. Lynch et al.
1
reported that hard-wire
installation could consume 75% of the total testing time
and installation costs could consume 25% of the total
system costs. The goal is to transition to low-power,
wireless sensor networks featuring minimal installation
costs.
2
Two of the major problems with these types of
sensor networks are the minimization of energy and
communication bandwidth. Compressed sensing tech-
niques hold promise to help address both of these
demands. By collecting compressed coefficients, the sig-
nal of interest can be represented using a fraction of the
measurements required by traditional Nyquist sam-
pling. The result is reduced energy consumption for
data collection, storage and transmission.
3,4
In addi-
tion, the bandwidth required to transmit the sampled
signal is also significantly reduced. The focus of this
work is to evaluate the applicability of compressed
sensing techniques to expand the capabilities of wireless
sensor networks for SHM applications. First, a novel,
compressive sensing–based framework for implement-
ing low-power SHM wireless sensor networks will be
proposed. Next, three compressed sensing techniques
are characterized in order to demonstrate their applic-
ability to the proposed wireless sensor network frame-
work for SHM applications. The compressed sensing
techniques that are investigated in this research are as
follows. First, ‘
1
norm minimization-based techniques
will be used to reconstruct experimentally measured
and compressed acceleration signals from a surrogate
three-story structure excited at a single frequency.
These reconstructions will make use of the Fourier
basis. Next, ‘
1
norm minimization-based techniques will
1
Engineering Institute, Los Alamos National Laboratory, Los Alamos,
NM, USA
2
Department of Mechanics, Politecnico di Milano, Milan, Italy
3
ISR-3, Los Alamos National Laboratory, Los Alamos, NM, USA
Corresponding author:
David Mascaren
˜as, Engineering Institute, Los Alamos National
Laboratory, P.O. Box 1663, MS T001, Los Alamos, NM 87545, USA.
Email: dmascarenas@lanl.gov
... Their research results showed that the sparsity of structural acceleration data is an important factor affecting the accuracy of data reconstruction. Mascarenas et al. [41] studied the application of compressed sensing technology to structural damage identification. According to their research, compression sensors are first used to collect compression coefficients from measured signals, which are then transmitted to off board processors to reconstruct the data by minimizing the L1 norm. ...
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