Air-coupled ultrasound (ACU) is already an established method for the non-destructive failure inspection of carbon fiber reinforced polymers (CFRP). In the through-transmission setup, plate-like structures are placed between the ultrasound (US) source and the receiver. The ultrasonic wave propagating through the material is observed; deteriorations inside the material such as defects alter the captured signal. Such defects can be delaminations, cracks, thickness changes or porosity. In the measurement setup chosen, conventional piezoelectric transducers and receivers are replaced by laser-based components. On the excitation side a nanosecond laser pulse, illuminating the plate surface, was used to induce ultrasonic waves (thermal regime) directly into the specimen. On the receiver side a laser-based optical microphone was tested. This membrane-free microphone detects the refractive index changes of the air, when the ultrasound propagates through the miniature Fabry-Pérot etalon. Using this new measurement setup, C-scans of CFRP plates were performed containing impact damage, delaminations and blind holes. In comparison to conventional aircoupled testing methods, our method is sensitive over a broader frequency range, has better signal-to-noise ratio (SNR) and a smaller acoustic aperture. This allows obtaining a more detailed image of a specimen including defects.
In this paper, a novel optical air-coupled ultrasound (O-ACU) technique is proposed. A wide broadband (100 KHz - 1 MHz) laser-acoustic based optical microphone worked as probe in an air-coupled ultrasound (ACU) system. The O-ACU modality was used to detect several CFRP and GFRP impacted laminates and the results were compared with the classical ACU modality. Infrared thermography, as an established reference technique, was used for the validation. Advanced image processing techniques were applied. Conclusively, O-ACU shows an obvious improvement in sensibility and resolution.
Nowadays, more and more, the monitoring of concrete’s setting and hardening as well as concrete’s condition assessment and mechanical characterization is realized with the Ultrasonic Pulse Velocity technique. However, despite its increasing use, the high potential and the vast applicability over a wide range of materials and structures, the aforementioned nondestructive testing technique is only partially exploited since a) a default pulse usually not selected by the user is transmitted, b) a single frequency band dependent on the testing equipment (pulse generator and sensors) is excited and c) usually the first part of the signal is only considered. Moreover, the technique, as defined by its name, is based on pulse velocity measurements which strongly rely on a predefined threshold value for the calculation of the travel time between the transmitting and receiving sensor. To overcome all these issues, in the current experimental campaign, user-defined signals are generated, a broad range of ultrasonic frequencies is excited, while the full length of the signal is also taken into account. In addition, the pulse velocity measurements are replaced by the more advanced phase velocity calculations determined by reference phase points of the time domain signals or by phase differences of the signals transformed in the frequency domain. The experiments are mainly conducted in hardened concrete specimens but the aggregates are substituted by spherical glass beads of well-defined sizes and contents in order to better control the microstructure. Reference liquid media are also examined for comparison purposes. The results in both cases show strong dispersive trends indicated by significant changes in the phase velocity.
Recent water shortages, particularly evident in the state of California, are calling for better predictive capabilities, and
improved management techniques for existing water distribution infrastructure. One particular example involves large-scale
water distribution systems (on the scale of reservoirs and dams) in the Sierra Nevada, where the majority of the
state's water is obtained from melting snow. Current control strategies at this scale rely on sparse data sets, and are often
based on statistical predictions of snowmelt. Sudden, or unexpected, snowmelt can thus often lead to dam-overtopping,
or downstream flooding.
This paper assesses the feasibility of employing real-time hydrologic data, acquired by large-scale wireless sensor
networks (WSNs), to improve current water management strategies. A sixty node WSN, spanning a square kilometer,
was deployed in the Kings River Experimental Watershed, a research site in the Southern Sierra Nevada, at an elevation
of 1,600-2,000 m. The network provides real time information on a number of hydrologic variables, with a particular
emphasis on parameters pertaining to snowmelt processes. We lay out a system architecture that describes how this real-time
data could be coupled with hydrologic models, estimation-, optimization-, and control-techniques to develop an
automated water management infrastructure. We also investigate how data obtained by such networks could be used to
improve predictions of water quantities at nearby reservoirs.
KEYWORDS: Sensors, Sensor networks, Microelectromechanical systems, Data modeling, Acoustic emission, Structural health monitoring, Data processing, Signal detection, Bridges, Signal processing
Acoustic emission techniques (AET) have a lot of potential in structural health monitoring for example to detect cracks or wire breaks. However, the number of actual applications of conventional wired AET on structures is limited due to the expensive and time consuming installation process. Wires are also vulnerable to damage and vandalisms. Wireless systems instead are easy to be attached to structures, scalable and cost efficient.
A hybrid sensor network system is presented being able to use any kind of commercial available AE sensor controlled by a sensor node. In addition micro-electro-mechanical systems (MEMS) can be used as sensors measuring for example temperature, humidity or strain. The network combines multi-hop data transmission techniques with efficient data pre-processing in the nodes. The data processing of different sensor data prior to energy consuming radio transmission is an important feature to enable wireless networking. Moreover, clusters of sensor nodes are formed within the network to compare the pre-processed data. In this way it is possible to limit the data transfer through the network and to the sink as well as the amount of data to be reviewed by the owner.
In particular, this paper deals with the optimization of the network to record different type of data including AE data. The basic principles of a wireless monitoring system equipped with MEMS sensors is presented along with a first prototype able to record temperature, moisture, strain and other data continuously. The extraction of relevant information out of the recorded AE data in terms of array data processing is presented in a second paper by McLaskey et al. in these proceedings. Using these two techniques, monitoring of large structures in civil engineering becomes very efficient.
As civil infrastructure ages, the early detection of damage in a structure becomes increasingly important for both life
safety and economic reasons. This paper describes the analysis procedures used for beamforming acoustic emission
techniques as well as the promising results of preliminary experimental tests on a concrete bridge deck. The method of
acoustic emission offers a tool for detecting damage, such as cracking, as it occurs on or in a structure. In order to gain
meaningful information from acoustic emission analyses, the damage must be localized. Current acoustic emission
systems with localization capabilities are very costly and difficult to install. Sensors must be placed throughout the
structure to ensure that the damage is encompassed by the array. Beamforming offers a promising solution to these
problems and permits the use of wireless sensor networks for acoustic emission analyses. Using the beamforming
technique, the azmuthal direction of the location of the damage may be estimated by the stress waves impinging upon a
small diameter array (e.g. 30mm) of acoustic emission sensors. Additional signal discrimination may be gained via array
processing techniques such as the VESPA process. The beamforming approach requires no arrival time information and
is based on very simple delay and sum beamforming algorithms which can be easily implemented on a wireless sensor or
mote.
Array processing of seismic data provides a powerful tool for source location and identification. For this method to work
to its fullest potential, accurate transduction of the unadulterated source mechanism is required. In our tests, controlled
areas of normal-strength concrete specimens were exposed to a low relative humidity at an early age to induce cracking
due to drying shrinkage. The specimens were continuously monitored with an array of broad-band, high-fidelity acoustic
emission sensors contrived in our laboratory in order to study the location and temporal evolution of drying shrinkage
cracking.
The advantage of the broadband sensors (calibration NIST-traceable) compared to more traditional acoustic emission
sensors is that the full frequency content of the signals are preserved. The frequency content of the signals provides
information about the dispersion and scattering inherent to the concrete, and the full unadulterated waveforms provide
insight into the micromechanisms which create acoustic emissions in concrete. We report on experimental and analytical
methods, event location and source mechanisms, and possible physical causes of these microseisms.
KEYWORDS: Sensors, Sensor networks, Microelectromechanical systems, Acoustic emission, Data analysis, Bridges, Data acquisition, Signal detection, Inspection, Algorithm development
The inspection of building structures, especially bridges, is currently made by visual inspection. The few non-visual methodologies make use of wired sensor networks, which are relatively expensive, vulnerable to damage, and time consuming to install. Systems based on wireless sensor networks should be both cost efficient and easy to install, scalable and adaptive to different type of structures. Acoustic emission techniques are an additional monitoring method to investigate the status of a bridge or some of its components. It has the potential to detect defects in terms of cracks propagating during the routine use of structures. However, acoustic emissions recording and analysis techniques need powerful algorithms to handle and reduce the immense amount of data generated. These algorithms are developed on the basis of neural network techniques and - regarding localization of defects - by array techniques. Sensors with low price are essential for such monitoring systems to be accepted. Although the development costs of such a system are relatively high, the target price for the entire monitoring system will be several thousands Euro, depending on the size of the structure and the number of sensors necessary to cover the most important parts of the structure. Micro-Electro-Mechanical-Systems and hybrid sensors form the heart of Motes (network nodes). The network combined multi-hop data transmission techniques with efficient data pre-processing in the nodes. Using this technique, monitoring of large structures in civil engineering becomes very efficient including the sensing of temperature, moisture, strain and other data continuously. In this paper, the basic principles of a wireless monitoring system equipped with MEMS sensors is presented along with a first prototype. The authors work on details of network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction is presented.
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