• Smart Structures and Systems
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• v.6 no.5_6
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• pp.423-438
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• 2010

Wireless smart sensors enable new approaches to improve structural health monitoring (SHM) practices through the use of distributed data processing. Such an approach is scalable to the large number of sensor nodes required for high-fidelity modal analysis and damage detection. While much of the technology associated with smart sensors has been available for nearly a decade, there have been limited numbers of fulls-cale implementations due to the lack of critical hardware and software elements. This research develops a flexible wireless smart sensor framework for full-scale, autonomous SHM that integrates the necessary software and hardware while addressing key implementation requirements. The Imote2 smart sensor platform is employed, providing the computation and communication resources that support demanding sensor network applications such as SHM of civil infrastructure. A multi-metric Imote2 sensor board with onboard signal processing specifically designed for SHM applications has been designed and validated. The framework software is based on a service-oriented architecture that is modular, reusable and extensible, thus allowing engineers to more readily realize the potential of smart sensor technology. Flexible network management software combines a sleep/wake cycle for enhanced power efficiency with threshold detection for triggering network wide operations such as synchronized sensing or decentralized modal analysis. The framework developed in this research has been validated on a full-scale a cable-stayed bridge in South Korea.

• Smart Structures and Systems
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• v.7 no.5
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• pp.365-378
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• 2011

Piezo-based active structural health monitoring (SHM) requires amplifiers specifically designed for capacitive loads. Moreover, with the increase in number of applications of wireless SHM systems, energy efficiency and cost reduction for this type of amplifiers is becoming a requirement. General lab grade amplifiers are big and costly, and not built for outdoor environments. Although some piezoceramic power amplifiers are available in the market, none of them are specifically targeting the wireless constraints and low power requirements. In this paper, a piezoceramic transducer amplifier for wireless active SHM systems has been designed. Power requirements are met by two digital On/Off switches that set the amplifier in a standby state when not in use. It provides a stable ${\pm}180$ Volts output with a bandwidth of 7k Hz using a single 12 V battery. Additionally, both voltage and current outputs are provided for feedback control, impedance check, or actuator damage verification. Vibration control tests of an aluminum beam were conducted in the University of Houston lab, while wireless active SHM tests of a wind turbine blade were performed in the Harbin Institute of Technology wind tunnel. The results showed that the developed amplifier provided equivalent results to commercial solutions in suppressing structural vibrations, and that it allows researchers to perform active wireless SHM on moving objects with no power wires from the grid.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.689-709
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• 2010

In this paper, a low cost, low power but multifunctional wireless sensor node is presented for the impedance-based SHM using piezoelectric sensors. Firstly, a miniaturized impedance measuring chip device is utilized for low cost and low power structural excitation/sensing. Then, structural damage detection/sensor self-diagnosis algorithms are embedded on the on-board microcontroller. This sensor node uses the power harvested from the solar energy to measure and analyze the impedance data. Simultaneously it monitors temperature on the structure near the piezoelectric sensor and battery power consumption. The wireless sensor node is based on the TinyOS platform for operation, and users can take MATLAB$^{(R)}$ interface for the control of the sensor node through serial communication. In order to validate the performance of this multifunctional wireless impedance sensor node, a series of experimental studies have been carried out for detecting loose bolts and crack damages on lab-scale steel structural members as well as on real steel bridge and building structures. It has been found that the proposed sensor nodes can be effectively used for local wireless health monitoring of structural components and for constructing a low-cost and multifunctional SHM system as "place and forget" wireless sensors.

• Smart Structures and Systems
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• v.16 no.3
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• pp.383-399
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• 2015

Today, as railways increase their capacity and speeds, it is more important than ever to be completely aware of the state of vehicles fleet's condition to ensure the highest quality and safety standards, as well as being able to maintain the costs as low as possible. Operation of a modern, dynamic and efficient railway demands a real time, accurate and reliable evaluation of the infrastructure assets, including signal networks and diagnostic systems able to acquire functional parameters. In the conventional system, measurement data are reliably collected using coaxial wires for communication between sensors and the repository. As sensors grow in size, the cost of the monitoring system can grow. Recently, auto-powered wireless sensor has been considered as an alternative tool for economical and accurate realization of structural health monitoring system, being provided by the following essential features: on-board micro-processor, sensing capability, wireless communication, auto-powered battery, and low cost. In this work, an original harvester device is designed to supply wireless sensor system battery using train bogie energy. Piezoelectric materials have in here considered due to their established ability to directly convert applied strain energy into usable electric energy and their relatively simple modelling into an integrated system. The mechanical and electrical properties of the system are studied according to the project specifications. The numerical formulation is implemented with in-house code using commercial software tool and then experimentally validated through a proof of concept setup using an excitation signal by a real application scenario.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.243-248
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• 2013

With rapid growth and fast diffusion of smartphone technologies, many users are deeply concerned about the smart applications and many mobile applications converged with various related technologies are rapidly disseminated. Especially, the convergence technologies like mobile apps that can establish the wireless ad hoc network between smartphone and other peripherals and exchange data are appear and progressed continuously. In this paper, we design and implement the smart app using bluetooth based wireless ad hoc sensor network that can connect smartphone with sensors and exchange data for various smart applications. The proposed smart application in this paper collects data obtained from more than 2 multi-sensors in real time and fulfills the decision making function by storing data at the database and analysing it. The smart application designed and implemented in this paper is the healthcare application that can analyze and evaluate the patient's health condition with sensing data from multi-sensors in real time through bluetooth module.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.79-85
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• 2020

IIoT stands for Industrial Internet of Things used in manufacturing, healthcare, and transportation in networked smart factories. Recently, IIoT's environment requires an automated control system through intelligent cognition to improve efficiency. In particular, IIoT can be applied to automatic calibration of production equipment for improved management in industrial environments. Such automation systems require a wireless network for transmitting industrial data. Self-calibration systems in laser transmission paths using wireless networks can save resources and improve production quality by real-time monitoring and remote control of laser transmission path. In this paper, we propose a wireless networked system for self-calibration of laser equipment that requires a laser transmission path, and we show the results of the prototype evaluation. The self-calibration system of laser equipment measures the coordinates of the laser points with sensors and sends them to the host using the proposed application protocol. We propose a wireless network service for the wired motor controller to align the laser coordinates. Using this wireless network, the host controls the motor by sending a control command of the motor controller in an HTTP message based on the received coordinate values. Finally, we build a prototype system of the proposed design to verify the detection performance and analyze the network performance.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.355-372
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• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.675-687
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• 2010

Structural Health Monitoring (SHM) is the science and technology of monitoring and assessing the condition of aerospace, civil and mechanical infrastructures using a sensing system integrated into the structure. Impedance-based SHM measures impedance of a structure using a PZT (Lead Zirconate Titanate) patch. This paper presents a low-power wireless autonomous and active SHM node called Autonomous SHM Sensor 2 (ASN-2), which is based on the impedance method. In this study, we incorporated three methods to save power. First, entire data processing is performed on-board, which minimizes radio transmission time. Considering that the radio of a wireless sensor node consumes the highest power among all modules, reduction of the transmission time saves substantial power. Second, a rectangular pulse train is used to excite a PZT patch instead of a sinusoidal wave. This eliminates a digital-to-analog converter and reduces the memory space. Third, ASN-2 senses the phase of the response signal instead of the magnitude. Sensing the phase of the signal eliminates an analog-to-digital converter and Fast Fourier Transform operation, which not only saves power, but also enables us to use a low-end low-power processor. Our SHM sensor node ASN-2 is implemented using a TI MSP430 microcontroller evaluation board. A cluster of ASN-2 nodes forms a wireless network. Each node wakes up at a predetermined interval, such as once in four hours, performs an SHM operation, reports the result to the central node wirelessly, and returns to sleep. The power consumption of our ASN-2 is 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it is estimated to run for about 2.5 years with two AAA-size batteries ignoring the internal battery leakage.

• Smart Structures and Systems
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• v.6 no.8
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• pp.939-951
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• 2010

A low-cost wireless sensor network (WSN) solution with highly expandable super and simple nodes was developed. The super node was designed as a sensing unit as well as a receiving terminal with low energy consumption. The simple node was designed to serve as a cheaper alternative for large-scale deployment. A 12-bit ADC inputs and DAC outputs were reserved for sensor boards to ease the sensing integration. Vibration and thermal field tests of the Chi-Lu Bridge were conducted to evaluate the WSN's performance. Integral acceleration, temperature and tilt sensing modules were constructed to simplify the task of long-term environmental monitoring on this bridge, while a star topology was used to avoid collisions and reduce power consumption. We showed that, given sufficient power and additional power amplifier, the WSN can successfully be active for more than 7 days and satisfy the half bridge 120-meter transmission requirement. The time and frequency responses of cables shocked by external force and temperature variations around cables in one day were recorded and analyzed. Finally, guidelines on power characterization of the WSN platform and selection of acceleration sensors for structural health monitoring applications were given.

• Smart Structures and Systems
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• v.5 no.4
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• pp.415-426
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• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.