Deconvolution processing for improved acoustic wavefield imaging

The identification of defects in plate-like structures has been successfully treated using both local guided ultrasonic waves and global modal quantities. Although there are many papers on these techniques, a lack of comparability between the two methods persists. This makes it difficult for users to identify the most appropriate method for the defect in question. This paper examines the effect of different parameterizations of a circular defect in a square aluminum plate on the system response in a case study. The measured local ultrasound signal from a propagating guided wave makes up the initial data set. The first method uses the entire waveform for the objective function of the optimization problem, while the second considers the velocity of the A0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$A_0$$\\end{document} mode. In the third and fourth methods, the first and second global natural frequencies of the plate modeled with free-free boundary conditions are investigated. The numerical models are validated experimentally through measurements with a laser Doppler vibrometer. This results in the qualitative and quantitative evaluation of the objective functions for all parameter combinations of the defect. The recommendation to sequentially employ modal analysis and then the ultrasound procedure is made for the defect type used in this study. The data gathered on the objective functions suggests that potential joint employment of the natural frequency and ultrasound methods may increase computational efficiency. For a specific case, the methods could also complement each other in terms of challenges such as local minima.

Wireless sensor network (WSN) application for bridge structure health monitoring (SHM), requires a scalable time synchronization service enabling data consistency and coordination. This paper describes the Energy-Balanced time Synchronization protocol (EBS), especially tailored for bridge structure health monitoring application. Through analyzing the characteristics of the bridge SHM, we present the especial requirements for designing the time synchronization protocol in WSN for SHM. Following the requirements, the proposed time synchronization protocol utilizes dynamically selecting the maximum reserve energy sensor node as broadcasting node to achieve energy balance among nodes in network. In addition, the EBS protocol fully exploits the wireless broadcast channel property in bridge SHM, so the broadcast communication flow is small. The high precision performance is reached by utilizing MAC-layer time-stamp as in the existing FTSP protocol. Finally, The EBS protocol performance is evaluated and compared to FTSP protocol.

Wireless sensor networks (WSNs) have been considered to be the next generation paradigm of structural health monitoring (SHM) systems due to the low cost, high scalability and ease of deployment. Due to the intrinsically energy-intensive nature of the sensor nodes in SHM application, it is highly preferable that they can be divided into subsets and take turns to monitor the condition of a structure. This approach is generally called as ‘coverage-preserving scheduling’ and has been widely adopted in existing WSN applications. The problem of partitioning the nodes into subsets is generally called as the ’maximum lifetime coverage problem (MLCP)’. However, existing solutions to the MLCP cannot be directly applied to SHM application. As compared to other WSN applications, we cannot define a specific coverage area independently for each sensor node in SHM, which is however the basic assumption in all existing solutions to the MLCP. In this paper, we proposed two approaches to solve the MLCP in SHM. The performance of the methods is demonstrated through both extensive simulations and real experiments.

Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems. The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring. The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities. The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.

Wireless sensor networks (WSNs) are one of the most able technologies in the structural health monitoring (SHM) field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

Structural health monitoring (SHM) uses an array of sensors to continuously monitor a structure to provide an early indication of problems such as damage to the structure from fatigue, corrosion or impact. The use of such a system enables maintenance costs to be reduced, and new structures can be designed to be lighter and more efficient. CSIRO has developed an SHM system for detecting high-velocity impacts in the skin of a structure, such as may occur to space vehicles. The system is a large sensor network containing about two-hundred nodes, each of which contains multiple sensors. The system has been built as a flexible testbed for undertaking research in the use of sensor networks in a wide range of SHM applications. This paper outlines the testbed that has been developed and the research that has been conducted using this testbed.

Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review

A variety of signal processing algorithms have been proposed to detect and locate defects in plate-like structures. However, the signal-to-noise ratio in these algorithms is too small especially in the reflection wave from the boundary, which further degrades the accuracy of localization of defects. A novel method for localization of defects is proposed in this article, based on the direct wave and fuzzy c-means clustering algorithm. To verify its effectiveness, experiments using the parallel linear and circular array are conducted, respectively. The experimental results show that the proposed method not only accurately locates single defect but also locates double defects in plate-like structures, and by comparing with the current discrete elliptic imaging algorithm, its location error of single defect is reduced from 20–25 mm to 0–3 mm and double defects is also reduced from 60–90 mm to 0–3 mm.

Wireless sensor Network (WSN) is a self- governing system used for monitoring. WSN with structural health monitoring (SHM) system is used to monitor the operations and health status of the structures. In the deployment of the backup sensors, the WSN aims to achieve a fault tolerance mechanism by placing a set of backup sensors. In the process of deploying the backup sensors, we follow a distributed approach in which the nodes are grouped into clusters. From each clusters, the separable point, isolated point are critical middle point are determined through which the backup sensors are placed. By achieving the fault tolerance mechanism, the energy is consumed and the lifetime of the sensor nodes are increased. The simulation results show the advantages of achieving the fault tolerance mechanism.

Development and Evaluation of a WSN for Real-time Structural Health Monitoring and Testing

Critical structures such as aircrafts, bridges, dams and buildings require periodic inspections to ensure safe operation. Reliable inspection of structures can be achieved by combining ultrasound non-destructive testing techniques with other sensors (for example, temperature sensor and accelerometers). In this study, we show that adapting wireless embedded systems to the task of structural health monitoring improves inspection productivity, increases mobility, and allows the aggregation of critical data to enhance inspection accuracy. To achieve this objective, we developed a customized system based on Reconfigurable Ultrasonic System-on-chip Hardware (RUSH) platform. RUSH collects and analyzes ultrasonic data to detect structural flaws such as cracks, voids, or fatigue. The collected data is then transferred through a Bluetooth transceiver to an Android smartphone referred to as Mobile Sensor Data Collector (MSDC), where the data is instantly displayed and forwarded to a central server for expert review over the Internet.

A major challenge impeding the deployment of wireless sensor networks for structural health monitoring (SHM) is developing means to supply power to the sensor nodes in a cost-effective manner. In this work an initial test of a roving-host wireless sensor network was performed on a bridge near Truth or Consequences, NM in August of 2007. The roving-host wireless sensor network features a radio controlled helicopter responsible for wirelessly delivering energy to sensor nodes on an as-needed basis. In addition, the helicopter also serves as a central data repository and processing center for the information collected by the sensor network. The sensor nodes used on the bridge were developed for measuring the peak displacement of the bridge, as well as measuring the preload of some of the bolted joints in the bridge. These sensors and sensor nodes were specifically designed to be able to operate from energy supplied wirelessly from the helicopter. The ultimate goal of this research is to ease the requirement for battery power supplies in wireless sensor networks.

Today, there is an emerging interest in Wireless Body Area Sensor Networks (WBASNs) for the real-time monitoring of patients and early chronic disease detection. In this context, this paper presents a synopsis survey of healthcare monitoring via the IEEE 802.15.6 (UWB) protocol. We intend to propose a survey of the current issues of wearable physiological monitoring signals and devices, application areas, and reliability in WBASNs. To help elderly and disabled people, it would be beneficial to use a wireless transportable gadget at home to gather useful data in traditional human activities. This will manage regular hospital and emergency department appointments and will monitor crucial physiological signals real-time. This paper will also present a study on new wireless technologies intended for body area sensor networks, including signal processing problems, spectral allocation, security, and future research challenges of WBASNs.

A multitype wireless sensor network (WSN) for structural health monitoring is developed for the National Stadium in China (generally known as “Bird's Nest”). The stadium is a super large-scale building built for the 2008 Beijing Olympic Games and can house more than 90,000 occupants. The structure is very rigid and weighs more than 40,000 tons in total. Considering the structural features and on-site environment, the system takes multitype sensors as measurement components including stress, displacement, acceleration, wind, and temperature. The monitoring module design consists of four functions: sensing, processing, wireless communication, and energy management. The communication between each sensor node is realized by using an adjustable and artificial-control chain-type network. A total of 290 sensors were installed on the structure, and the data collection work has been carried out for more than one year. This paper mainly focuses on the system development and project application, while the data analysis work is briefly discussed as well. It can be concluded that the customized WSN is robust and durable, which well satisfies the requirement of plenty multitype sensors working in a large-area distribution. The data analysis results reveal that the super large-scale structure is very sensitive to the temperature effect.

A bridge monitoring TestBed is developed as a research environment for sensor networks and related decision-support technologies. A continuous monitoring system, capable of handling a large number of sensor data channels and three video signals, is deployed on a four-span, 90-m long, reinforced concrete highway bridge. Of interest is the integration of the image and sensor data acquisition into a single computer, thereby providing accurate time synchronization between the response and corresponding traffic loads. Currently, video and acceleration records corresponding to traffic induced vibration are being recorded. All systems operate online via a high-speed wireless Internet network, allowing real-time data transmission. Elements of the above health monitoring framework are presented herein. Integration of these elements into an automated functional system is emphasized. The recorded data are currently being employed for structural system identification via a model-free technique. Effort is also underway to correlate the moving traffic loads with the recorded accelerations. Finally, the TestBed is available as a resource for verification of new sensor technologies, data acquisition/transmission algorithms, data mining strategies, and for decision-support applications.