Development of Feature Extraction Methods for Structural Health
Monitoring Systems for Civil Infrastructure
A Thesis Proposal By
Jeff Meadows
Mentor: Prof. Kerop Janoyan
Purpose
To develop and compare feature extraction methods for use in practical applications of Structural
Health Monitoring systems.
Introduction to SHM
Structural Health Monitoring
Structural Health Monitoring (SHM) is a rapidly growing field of research. The purpose of these
systems is to detect damage in structures. Comparison can be made to virus protection for
computers where real time protection indicates problems without draining costly memory,
though energy is the limiting feature for SHM systems. The variety of definitions stems from the
fact that SHM has multiple uses and can be defined differently for each. This ambiguity and
variety adds an additional challenge to those designing these systems. Some of the more
common definitions of damage include visual damage such as cracks, holes, and fractures but
may be as difficult to spot as decreases in stiffness and torsional stability. SHM systems are
designed to identify any or all of these with minimal human interaction.
SHM systems most often evaluate the health of a structure using sensor networks. Sensors
measure structure quantities such as strain, displacement, and acceleration as well as
environmental conditions like temperature, wind, and moisture [1]. Various types of sensors are
imbedded in the material while others are placed post-construction. For sensors that measure
displacement, acceleration, and strain, a load must be applied to the structure. To minimize
human interaction and allow real-time data sampling, some systems take advantage of ambient
loading or loading applied from normal usage of the structure. For example the normal passage
of cars over a bridge is considered ambient loading.
The networks require that an array of sensors be placed on the structure to allow communication
between sensors as well as locate the damage. As the density of sensors increases on a structure,
the quality and resolution of damage information also increases. However, sensor installation
costs, sensor power consumption, and data processing capacity act as limiting factors for sensor
density.
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Figure 1. Sensor Arrangement on the Jamboree Road overcrossing demonstrating sensor
placement for global analysis. [2]
Sensor density also is dependent upon the type of analysis performed. Analysis methods can
generally be organized into two categories, global and local. Global analysis involves
identifying and locating damage by the response of the entire structure compared to a baseline
model. Feng et al. addressed global analysis when studying a pair of bridges [2]. Figure 1
shows sensor arrangements for the Jamboree Road overcrossing. Though the bridge was studied
in three dimensions, less than 20 sensors collected data. The result was a baseline or undamaged
model of the bridge reaction to certain loading cases. Staszewski et al. developed a method to
produce optimal sensor arrangements for local analysis [3]. Local analysis requires denser
sensor arrays. Though local analysis cannot characterize an entire structure, it has the capability
to more accurately locate damage such as cracks on a small scale.
Figure 2. Sensor arrangement on a 530 mm x 300 mm composite plate demonstrating local
analysis. [3]
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Goals of SHM
Ultimately, SHM systems improve the safety of structures, especially civil infrastructure.
Though rare, the failure of bridges and buildings is disastrous costing numerous lives and
colossal economic losses. SHM has a distinct advantage over traditional infrastructure
inspection methods; they offer real-time, continuous analysis and detection of damage without
damaging the structure. In buildings, they can also detect damage to internal members normally
not available to inspectors.
SHM systems are designed to convey information allowing authorities to make an educated
decision regarding actions to be taken when damage occurs. Effective systems must be able to:
1.
2.
3.
4.
Detect Damage
Accurately Locate Damage
Identify the Magnitude of Damage
Produce a Life-Cycle Assessment for the Structure
Though the first goal seems obvious, it also depends upon the definition of damage which, as
was previously stated, is often ambiguous. Different forms of damage may also produce
different analysis results. Accurate location of damage normally requires appropriate sensor
placement. Many feature extraction methods use forms of triangulation to locate data, though no
system appears to have it perfected yet. Precisely locating damage greatly optimizes further
inspections of the structure. Identification of damage magnitude is difficult due to inconsistent
loading conditions. Since the most effective SHM systems use ambient loading, the magnitude
of the load is often unknown. The last goal is a result of implementation of SHM systems early
in a structure’s life-cycle. Continuous data collection throughout a structure’s life-cycle can
produce valuable information in regards to how the particular structure ages and degrades.
Knowledge of whether a structure degrades progressively or in sudden events is helpful in
determining the possible remaining life of a structure.
Life-cycle assessments come with the challenge of developing SHM systems and sensors that
last as long as the structure that it analyses. Considering the lifespan of bridges, 50 or more
years, compared to the lifespan of most electronics, this is a monumental task. Development of a
long-term system is one of the major goals of Clarkson University Professors K.D. Janoyan, R.
Jha, and E.S. Sazanov and graduate student M.P. Fuchs. They have developed a wireless
intelligent sensor and actuator network (WISAN) that addresses this issue and will be applied to
a bridge to determine its long term performance. This system also implements wireless sensors
which decreases complicated and expensive sensor installation and wiring, signal degradation
through long cables, and data overloads [5].
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Results of SHM Implementation
Spencer et al. envision as a final result of SHM implementation smart structures, that is
structures that can recognize dangerous situations without human interaction [4]. Such a system
could be used to identify unsafe conditions in buildings as a result of earthquakes and quickly
warn occupants to evacuate. Though this is currently not possible, the results of such a system
are worth the efforts.
Presently, factors of safety are used in design to offset unknown variability in decay of structural
stability. Variability results from adverse environmental conditions, unforeseen usage changes,
inconsistent materials, and imperfect construction practices. Sohn et al. identify multiple cases
in the United States, including aircraft frames and bridges, where current structures are
approaching or exceeding their expected life spans and require constant inspections [1]. These
inspections require costly, arduous manpower that relies upon subjective evaluations which are
often inconsistent. When subjective evaluations are not enough, destructive testing may be
required, damaging prospectively sound structures.
SHM has numerous applications including pipelines, aeronautics, and civil infrastructure. The
full development of SHM has major safety and economic impacts on the integrity infrastructure
in the United States and other developed countries [1]. Failure of structures before expectations
may not be noticed and cause hazardous situations that could be remedied by immediate
notification by a remote SHM system. On the other hand, often structures are replaced or
repaired even though they have performed better than expected. In this situation the life-cycle
assessments produced from SHM systems would be invaluable because of the costs that could be
saved by retaining the structure as opposed to replacing or repairing it.
Feature Extraction and Data Analysis Methods
One of the major issues facing SHM researchers is the analysis of the massive amounts of data
that are produced. Real-time protection requires that data be taken almost continuously or at
least during loading events. There are also uncertainties as to how to best translate the material
read by the sensors into an informative characterization of the structural condition. Researchers
extract from the data features that change when damage occurs like natural frequency, mode
shape, and damping ratios [6]. Many different mathematical and statistical approaches have
been taken, but very few successfully identify damage location. Recently a method called the
Hilbert-Huang Transform (HHT) has been identified that allows accurate identification of
damage location. The application of the HHT method to civil infrastructure is the focus of this
research.
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Commonly Used Feature Extraction Methods
Sohn et al. identify about twenty different types of feature extraction methods that have been
used in SHM applications and list studies that have demonstrated them [1]. Natural frequency,
mode shape, and damping ratios are normally the most common features extracted from the data
though a few others exist. The most commonly used methods of extraction can be separated into
two categories.
The first category is comprised mainly of methods utilizing Fourier analysis. These methods are
used due to the nonlinearity of the vibration and motion of structures. This group includes, but is
not limited to, frequency response functions (FRF), the identification of mode shapes and mode
shape curvatures, resonant frequency shifts, damping shifts, and dynamic flexibility [1]. Each
method was created to fulfill the requirements of certain applications. In general the methods are
good, but they usually result in global characterization of civil infrastructure. The problem with
this global characterization is that it normally cannot fulfill the second goal of SHM systems
which is to identify the location of damage. Quite often Fourier analysis also has a limited
resolution, resulting in less than satisfactory identification of damage magnitude.
The second major category of methods involves frequency-time analysis. These methods also
address the nonlinearity of structural motion but also identify the motion and damage cases as
non-stationary. Huang et al. compares and contrasts these methods. They are the spectrogram,
otherwise known as Short Time Fourier Transform (STFT), the wavelet analysis, the WignerWille Distribution, the evolutionary spectrum, the Empirical Orthogonal Function (EOF)
Expansion, Empirical Mode Decomposition (EMD), and HHT [7]. With the exception of the last
two, the author argues that even these modifications of the Fourier analysis failed in some way or
another. The last is unique in that it produces a local representation of the data as well as an
energy-frequency-time distribution of results.
The HHT Method
The HHT method is actually a combination of multiple techniques and steps. The first step uses
EMD. The EMD process decomposes the signal received from sensors into Intrinsic Mode
Functions (IMF) [8]. The IMF’s are much easier to deal with mainly because often a finite
amount of IMF’s can be produced. IMF’s are produced sequentially with each subsequent IMF
being derived from the remainder (the portion of the initial signal not represented by previous
IMF’s) of the initial signal. When a large amount is produced, the higher (later) IMF’s often are
not greatly affected by damage. In most cases higher IMF’s have very little shape at all. Rilling
et al. produced an EMD of a simple signal resulting in three IMF’s. As Figure 3 indicates, each
subsequent IMF becomes simpler than the last producing very little residual signal.
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Figure 3. EMD of simple triangular and sinusoidal superimposed functions. [9]
The Hilbert Transform is then performed on the IMF’s to produce an analytic signal. From the
analytic signal, instantaneous frequencies can be determined. The Hilbert Transform also allows
the instantaneous frequency and the amplitude of the signal to be represented as functions of
time [8]. The importance of this three dimensional representation (frequency, amplitude, and
time) allows accurate determination of both damage time and signal magnitude. Using signal
magnitude from multiple sensor readings can indicate the location of damage.
Proposed Research
The proposed research is to act as a companion to ongoing research involving the WISAN
system at Clarkson University under Professor K. Janoyan. The WISAN project objectives
include both a laboratory and field test of the system on scaled and actual deployment,
respectively. The goal of the proposed research is to adapt HHT to the WISAN system and
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determine parameters using a computer model prior to application in these systems. The results
of the computer model should be comparable to those of both the laboratory model and the actual
deployment of the system.
Analysis of FEM
The WISAN project taking place at Clarkson University will produce valuable data sets on both
the laboratory and life-size scale. The analysis of these data sets can and should be attempted
using multiple methods. However, each type of feature extraction method requires certain
qualities and quantities to be measured in order to produce accurate results. The proposed
research will produce guidelines and specifications necessary to allow HHT and possible other
methods to accurately detect and locate damage in laboratory or practical applications.
The proposed method will use a Finite Element Model (FEM) to produce a similar virtual
structure to those found in the lab and ultimately the field. A similar procedure was completed
by Jha et al. Though comparison to laboratory data was not made, a virtual plate was fitted with
virtual sensors and comparisons between damaged and undamaged structures were made using
HHT and Short Time Fourier Transform (STFT).
Though the goal of the research was to compare two energy-frequency-time extraction methods,
HHT and STFT, the research will be used as a model for the proposed research. Since the
WISAN tests and other similar projects will be performed on more complicated structures, a
structure similar to proposed laboratory work will be used as the FEM model. The model will be
fitted with virtual sensors. Vibrations will be applied to the structure in a damaged and
undamaged state. Data (acceleration, displacement, etc.) will be collected at the sensor locations.
The data will be processed using the HHT and most likely another method such as STFT.
Analysis of previous research has led to possible concerns to the application of HHT to realistic
systems. Though in the case of an FEM, virtually infinite amounts and resolutions of data are
available, physical systems do not have this luxury. It is imperative that data sampling of
physical systems meet the practical requirements set by the analysis method. In the case of
HHT, there are three particular parameters that must be studied and specified.
1. Data Length
2. Sampling Rate
3. Sensor Arrangement
The total time or data length is the deciding factor for the minimum frequency values [8]. Larger
data lengths indicate smaller minimum frequencies and higher frequency resolution. The
opposite is true of sampling rate; shorter time increments lead to higher maximum frequencies
[8]. The determination of these two factors depends upon the frequency ranges which are most
greatly affected by damage to a structure. The third parameter, sensor arrangement, is integral to
locating damage. Jha et al. cites locating ability as one of the major benefits of the HHT method,
but others wisely warn that no single sensor can identify location [6].
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The development of the FEM and specification of these parameters will take place in the spring
and early summer of 2005. Specifications are intended to be complete in a timely fashion in
order to offer advice for laboratory tests to be performed this summer.
Comparison to Lab Model
Beginning in the late summer of 2005 and continuing into the following fall comparisons will be
made to analyze the validity of the application of the HHT method to the structural model.
Modifications may be made to the analysis method and the defined parameters to better reflect
the results of the lab testing. If modifications are made, the FEM data will be reprocessed to
determine the validity of the changes. At this point it may also be beneficial to develop an FEM
analysis and parameter specification for a larger, more complicated structure for future
researchers to use as a comparison.
Timeline
Date
Spring 2005
Activity Completion
FEM Model and
Data Production
Summer 2005 Data Analysis and
Definition of Parameters
Fall 2005
Comparison to Lab
or Field Model
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References
[1]
Sohn, Hoon, Charles R. Farrar, Francois M. Hemez, Devin D. Shunk, Daniel W.
Stinemates, and Brett R. Nadler. A Review of Structural Health Monitoring Literature:
1996-2001. Los Alamos National Laboratory Report, LA-13976-MS, 2003.
[2]
Feng, Maria Q., Doo Kie Kim, Jin-Hak Yi, and Yangbo Chen. “Baseline Models for
Bridge Performance Monitoring”. Journal of Engineering Mechanics 130 (2004): 562569.
[3]
Staszewski, W. J., K. Worden, R. Wardle, and G. R. Tomlinson. “Fail safe sensor
distributions for impact detection in composite materials”. Smart Materials and
Structures. 9 (2001): 298-303.
[4]
Spencer, Jr., B.F., Manuel E. Ruiz-Sandoval, and Narito Kurata. “Smart Sensing
Technology: Opportunities and Challenges”. Journal of Structural Control and Health
Monitoring. (2004).
[5]
Nagayama, T., M. Ruiz-Sandoval, B. F. Spencer Jr., K. A. Mechitov, G. Agha. Wireless
Strain Sensor Development for Civil Infrastructure. First International Workshop on
Networked Sensing Systems, June 2004, Tokyo, Japan: 2004.
[6]
Yang, J. N., Y. Lei, S. Lin and N. Huang. “Hilbert-Huang Based Approach for Structural
Damage Detection”. Journal of Engineering Mechanics 130 (2004): 85-95.
[7]
Huang, N.E., Z. Shen, S. R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N. Yen, C.C. Tung,
and H.H. Liu. “The empirical mode decomposition and the Hilbert spectrum for
nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of
London A, 454 (1998): 903-995.
[8]
Jha, R., F. Yan, and G. Ahmadi. Energy-Frequency-Time Analysis of Structural
Vibrations Using Hilbert-Huang Transform. Proc. of 12th AIAA/ASME/AHS Adaptive
Structures Conf., 19-22 April 2004, Palm Springs, CA: 2004.
[9]
Rilling, Gabriel, Patrick Flandrin, and Paulo Gonçalvès.
On Empirical Mode
Decomposition and Its Algorithms. IEEE-EURASIP Workshop on Nonlinear Signal and
Image Processing NSIP-03, June 2003, Grado, Italy.
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