Structural Health Monitoring Using Smart Sensors - ideals ...

e scalability, autonomous distributed computing, fault tolerance, etc. A system
architecture that potentially provides these desirable characteristics was proposed as a
homogeneous network of smart sensors consisting of Imote2s which run the DCS for
SHM. Realization of this system was explained in the subsequent chapters.
The availability of appropriate sensors is essential for SHM applications. Sensor
board customizability was demonstrated in Chapter 4. Strain is one of important physical
quantities utilized in SHM applications. While accelerometers are often available on smart
sensors, and their suitability for SHM applications has been studied, a smart sensor with
strain sensors is rare. The flexibility of the smart sensor platforms was shown through the
development of a strain sensor board equipped with a Wheatstone bridge circuit for the
Berkeley Mote platform. The strain sensor board is an analog circuit, needing an
antialiasing filter, which was also developed. Experimental verification of these sensor
boards demonstrated the customizability of smart sensor boards.
Middleware services frequently needed in SHM applications were studied and
realized on the Imote2. The amount of data required for SHM applications is usually so
large that centrally collecting all of the data is impractical, if not impossible. A modelbased
data aggregation service was developed to estimate required correlation functions in
a distributed manner. Data transfer requirements were greatly reduced. Another important
middleware service developed was reliable communication. Lost communication packets
carrying data can degrade signals in a similar way as observation noise does. The loss of a
packet carrying a command may leave an SHM system in an unknown state. Reliable
communication is, therefore, essential. Reliable communication protocols suitable for
long data records and a short comments were each developed for both unicast and
multicast applications. Synchronized sensing is also a crucial middleware service.
Unsynchronized signals distort modal identification results, especially the phase of mode
shapes. Even when clocks on smart sensors are synchronized, synchronized sensing is not
necessarily guaranteed. When sensing commences cannot be easily controlled. To achieve
synchronized sensing, clocks on the Imote2s were first synchronized, and then signals
were acquired with time stamps; the signals were subsequently resampled based on the
time stamps. In this way, signals were synchronized with each other with high precision.
The developed middleware services allow implementation of DCS for SHM, as well as a
wide variety of SHM applications, on Imote2s.
SHM algorithms implemented on Imote2s are based on DCS for SHM. In a local
sensor community, NExT and ERA estimates the modal properties of a structure from
acceleration responses. The mass perturbation DLV method localizes damaged element
using modal properties identified before and after damage. The damage localization
results are exchanged among neighboring local sensor communities to confirm the
consistency of localization results in overlapping parts of sensor communities. If not,
measurement and damage localization are repeated. This strategy was extended by
replacing the mass perturbation DLV method with the SDLV method, which eliminates
the need for mass normalization constant estimation.
Using the middleware services and algorithms, a framework for SHM using smart
sensors was realized on the Imote2 platform. Numerical functions used in the DCS
algorithms were first ported to Imote2s and their numerical accuracy and memory size
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