KEYWORDS: Capacitors, Sensors, Energy harvesting, Wind energy, Solar cells, Solar energy, Microsoft Foundation Class Library, Electronics, Sensing systems, Wind turbine technology
With a global interest in the development of clean, renewable energy, wind energy has seen steady growth over the past several years. Advances in wind turbine technology bring larger, more complex turbines and wind farms. An important issue in the development of these complex systems is the ability to monitor the state of each turbine in an effort to improve the efficiency and power generation. Wireless sensor nodes can be used to interrogate the current state and health of wind turbine structures; however, a drawback of most current wireless sensor technology is their reliance on batteries for power. Energy harvesting solutions present the ability to create autonomous power sources for small, low-power electronics through the scavenging of ambient energy; however, most conventional energy harvesting systems employ a single mode of energy conversion, and thus are highly susceptible to variations in the ambient energy. In this work, a multi-source energy harvesting system is developed to power embedded electronics for wind turbine applications in which energy can be scavenged simultaneously from several ambient energy sources. Field testing is performed on a full-size, residential scale wind turbine where both vibration and solar energy harvesting systems are utilized to power wireless sensing systems. Two wireless sensors are investigated, including the wireless impedance device (WID) sensor node, developed at Los Alamos National Laboratory (LANL), and an ultra-low power RF system-on-chip board that is the basis for an embedded wireless accelerometer node currently under development at LANL. Results indicate the ability of the multi-source harvester to successfully power both sensors.
KEYWORDS: Sensors, Microcontrollers, Structural health monitoring, Rockets, Adhesives, Aluminum, Power supplies, Data storage, Active sensors, Environmental sensing
The paper presents a discussion of the design, development, and assembly of Structural Health Monitoring (SHM)
experiments launched in space on a sub-orbital flight. Onboard experiments were focused on investigating the utility of
piezoelectric wafer active sensors (PWAS) as active elements of spacecraft SHM systems and the electro-mechanical
impedance method as a promising SHM methodology for space systems. A Magneto-elastic active sensor (MEAS) was
used to record in-flight dynamics of the payload. The list of PWAS experiments included a bolted-joint experiment, an
adhesive endurance experiment, and an experiment to monitor PWAS condition during spaceflight. Electromechanical
impedances of piezoelectric sensors were recorded in-flight at varying input frequencies using onboard microcontroller
units. PWAS and MEAS data were recovered from the payload after landing. Details of the sub-orbital flight
experiments are considered and conclusions pertaining to flight results are presented. The paper discusses issues
encountered during design, development, and assembly of the payload and aspects central to successful demonstration of
the SHM during sub-orbital space flight.
This paper overviews the test setup and experimental methods for structural health monitoring (SHM) of two 9-meter
CX-100 wind turbine blades that underwent fatigue loading at the National Renewable Energy Laboratory's (NREL)
National Wind Technology Center (NWTC). The first blade was a pristine blade, which was manufactured to standard
specifications for the CX-100 design. The second blade was manufactured for the University of Massachusetts, Lowell
with intentional simulated defects within the fabric layup. Each blade was instrumented with piezoelectric transducers,
accelerometers, acoustic emission sensors, and foil strain gauges. The blades underwent harmonic excitation at their
first natural frequency using the Universal Resonant Excitation (UREX) system at NREL. Blades were initially excited
at 25% of their design load, and then with steadily increasing loads until each blade reached failure. Data from the
sensors were collected between and during fatigue loading sessions. The data were measured over multi-scale frequency
ranges using a variety of acquisition equipment, including off-the-shelf systems and specially designed hardware
developed at Los Alamos National Laboratory (LANL). The hardware systems were evaluated for their aptness in data
collection for effective application of SHM methods to the blades. The results of this assessment will inform the
selection of acquisition hardware and sensor types to be deployed on a CX-100 flight test to be conducted in
collaboration with Sandia National Laboratory at the U.S. Department of Agriculture's (USDA) Conservation and
Production Research Laboratory (CPRL) in Bushland, Texas.
This paper presents the deployment of an embedded active sensing platform for real-time condition monitoring of
telescopes in the RAPid Telescopes for Optical Response (RAPTOR) observatory network. The RAPTOR network
consists of several ground-based autonomous astronomical observatories primarily designed to search for astrophysical
transients such as gamma-ray bursts. In order to capture astrophysical transients of interest, the telescopes must remain
in peak operating condition to move swiftly from one potential transient to the next throughout the night. However,
certain components of these telescopes have until recently been maintained in an ad hoc manner, often being permitted
to run to failure, resulting in the inability to drive the telescope. In a recent study, a damage classifier was developed
using the statistical pattern recognition paradigm of structural health monitoring (SHM) to identify the onset of damage
in critical telescope drive components. In this work, a prototype embedded active sensing platform is deployed to the
telescope structure in order to record data for use in detecting the onset of telescope drive component damage and alert
system administrators prior to system failure.
This paper presents the initial analysis results of several structural health monitoring (SHM) methods applied to two 9-
meter CX-100 wind turbine blades subjected to fatigue loading at the National Renewable Energy Laboratory's (NREL)
National Wind Technology Center (NWTC). The first blade was a pristine blade, manufactured to standard CX-100
design specifications. The second blade was manufactured for the University of Massachusetts, Lowell (UMass), with
intentional simulated defects within the fabric layup. Each blade was instrumented with a variety of sensors on its
surface. The blades were subject to harmonic excitation at their first natural frequency with steadily increasing loading
until ultimately reaching failure. Data from the sensors were collected between and during fatigue loading sessions. The
data were measured at multi-scale frequency ranges using a variety of data acquisition equipment, including off-the-shelf
systems and prototype data acquisition hardware. The data were analyzed to identify fatigue damage initiation and to
assess damage progression. Modal response, diffuse wave-field transfer functions in time and frequency domains, and
wave propagation methods were applied to assess the condition of the turbine blade. The analysis methods implemented
were evaluated in conjunction with hardware-specific performance for their efficacy in enabling the assessment of
damage progression in the blade. The results of this assessment will inform the selection of specific data to be collected
and analysis methods to be implemented for a CX-100 flight test to be conducted in collaboration with Sandia National
Laboratory at the U.S. Department of Agriculture's (USDA) Conservation and Production Research Laboratory (CPRL)
in Bushland, Texas.
This paper presents the performance of a variety of structural health monitoring (SHM) techniques, based on the use of
piezoelectric active sensors, to determine the structural integrity of a 9m CX-100 wind turbine blade (developed by
Sandia National Laboratory). First, the dynamic characterization of a CX-100 blade is performed using piezoelectric
transducers, where the results are compared to those by conventional accelerometers. Several SHM techniques,
including Lamb wave propagations, frequency response functions, and time series based methods are then utilized to
analyze the condition of the wind turbine blade. The main focus of this research is to assess and construct a performance
matrix to compare the performance of each method in identifying incipient damage, with a special consideration given
the issues related to field deployment. Experiments are conducted on a stationary, full length CX-100 wind turbine
blade. This examination is a precursor for planned full-scale fatigue testing of the blade and subsequent tests to be
performed on an operational CX-100 Rotor Blade to be flown in the field.
KEYWORDS: Sensors, Structural health monitoring, Diagnostics, Sensor networks, MATLAB, Ferroelectric materials, Bridges, Control systems, Data storage, Data acquisition
Wireless sensor nodes with impedance measurement capabilities, often based on the Analog Devices AD5933
impedance chip and Atmel's 8-bit ATMega 1281 microcontroller, have been demonstrated to be effective in collecting
data for localized damage detection (such as for loose bolt detection) and for sensor self-diagnostics. Previouslydeveloped
nodes rely on radio telemetry and off-board processing (usually via a PC) to ascertain damage presence or
sensor condition. Recent firmware improvements for the wireless impedance device (WID) now allow seamless
integration of the WID with SHMTools and mFUSE, an open-source function sequencer and SHM process platform for
Matlab. Furthermore, SHM processes developed using mFUSE can be implemented in hardware on the WID, allowing
greater autonomy among the sensor nodes to identify and report damage in real time. This paper presents the capabilities
of the newly integrated hardware and software, as well as experimental validation.
This paper describes a new software package, SHMTools, for prototyping algorithms for various structural health
monitoring (SHM) applications. The software includes a set of standardized MATLAB routines covering three
main stages of SHM: data acquisition, feature extraction, and feature classification for damage identification. A
subset of the software in SHMTools is embeddable, which consists of Matlab functions that can be cross-compiled
into generic "C" programs to be run on a target hardware. The software is designed to accommodate multiple
sensing modalities, including piezoelectric active-sensing, which have become widely used in SHM practice. The
software package, standardized datasets, and detailed documentation are publicly available for use by the SHM
community. The details of this software will be discussed, along with several example processes to demonstrate
its utility.
KEYWORDS: Sensors, Energy harvesting, Wind energy, Ferroelectric materials, Structural health monitoring, Antennas, Solar energy, Capacitors, Sensor networks, Data storage
In this paper we present a method for coupling wireless energy transmission with traditional energy harvesting
techniques in order to power sensor nodes for structural health monitoring applications. The goal of this study is to
develop a system that can be permanently embedded within civil structures without the need for on-board power sources.
Wireless energy transmission is included to supplement energy harvesting techniques that rely on ambient or
environmental, energy sources. This approach combines several transducer types that harvest ambient energy with
wireless transmission sources, providing a robust solution that does not rely on a single energy source. Experimental
results from laboratory and field experiments are presented to address duty cycle limitations of conventional energy
harvesting techniques, and the advantages gained by incorporating a wireless energy transmission subsystem. Methods
of increasing the efficiency, energy storage medium, target applications and the integrated use of energy harvesting
sources with wireless energy transmission will be discussed.
KEYWORDS: Sensors, Ferroelectric materials, Structural health monitoring, Signal processing, Damage detection, Data storage, Sun, Diagnostics, Autoregressive models, Temperature metrology
Various experimental studies have demonstrated that an impedance-based approach to structural health monitoring can
be an effective means of damage detection. Using the self-sensing and active-sensing capabilities of piezoelectric
materials, the electromechanical impedance response can be monitored to provide a qualitative indication of the overall
health of a structure. Although impedance analyzers are commonly used to collect such data, they are bulky and
impractical for long-term field implementation, so a smaller and more portable device is desired. However, impedance
measurements often contain a sizeable number of data points, and a smaller device may not possess enough memory to
store the required information, particularly for real-time analysis. Therefore, the amount of data used to assess the
integrity of a structure must be significantly reduced. A new type of cross correlation analysis, for which impedance data
is instantaneously correlated between different sensor sets and different frequency ranges, as opposed to be correlated to
pre-stored baseline data, is proposed to drastically reduce the amount of data to a single correlation coefficient and
provide a quantitative means of detecting damage relative to the sensor positions. The proposed analysis is carried out on
a 3-story representative structure and its efficiency is demonstrated.
In this paper, we present experimental investigations using energy harvesting and wireless energy transmission to
operate embedded structural health monitoring sensor nodes. The goal of this study is to develop sensing systems
that can be permanently embedded within a host structure without the need for an on-board power source. With this
approach the required energy will be harvested from the ambient environment, or periodically delivered by a RF
energy source to supplement conventional harvesting approaches. This approach combines several transducer types
to harvest energy from multiple sources, providing a more robust solution that does not rely on a single energy
source. Both piezoelectric and thermoelectric transducers are considered as energy harvesters to extract the ambient
energy commonly available on civil structures such as bridges. Methods of increasing the efficiency, energy storage
medium, target applications and the integrated use of energy harvesting sources with wireless energy transmission
will be discussed.
KEYWORDS: Sensors, Data acquisition, Structural health monitoring, Data storage, Sensor networks, Bridges, Connectors, Microcontrollers, Multiplexers, Data transmission
This paper presents recent developments in an extremely compact, wireless impedance sensor node for combined use
with both impedance method and low-frequency vibrational data acquisition. The sensor node, referred to as the WID3
(Wireless Impedance Device) integrates several components, including an impedance chip, a microcontroller for local
computing, telemetry for wireless data transmission, multiplexers for managing up to seven piezoelectric transducers per
node, energy storage mediums, and several triggering options into one package to truly realize a self-contained wireless
active-sensor node for SHM applications. Furthermore, we recently extended the capability of this device by
implementing low-frequency A/D and D/A converters so that the same device can measure low-frequency vibration
data. The WID3 requires less than 60 mW of power to operate and is designed for the mobile-agent based wireless
sensing network. The performance of this miniaturized device is compared to our previous results and its capabilities are
demonstrated.
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