Self-Powered Sensors for Monitoring of Highway Bridges

The task of structural health monitoring (SHM) of aging highway bridges and overpasses is important not only from the point of preventing economic losses from traffic delays and detours but also is a matter of preventing catastrophic failures and loss of human life. In recent years, wireless sensor technologies have been used extensively to develop SHM platforms for bridges. A limitation of wireless sensors is the finite life span of batteries and high cost of battery replacements, which make such systems prohibitively expensive in many cases. Energy harvesting is a solution capable to alleviate this problem. A novel wireless sensor system is presented that harvests vibrations of the bridge created by passing traffic, which is converted into usable electrical energy by means of a linear electromagnetic generator. Utilization of an electromagnetic generator allows harvesting of up to 12.5 mW of power in the resonant mode with the frequency of excitation at 3.1 Hz, in this particular design. The novelty of the system also includes tight integration of the power generator and a smart algorithm for energy conversion that switches between the low-power mode and the impedance matching mode. Finally, results of field experiments are presented in which the wireless system is operated exclusively by the harvested energy of vibration on a rural highway bridge with low traffic volume.     

A Mobile Host Approach for Wireless Powering and Interrogation of Structural Health Monitoring Sensor Networks

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can provide the data collection necessary for rapid structural assessment after an event such as a natural disaster puts the reliability of civil infrastructure in question. Technical challenges affecting deployment of such a network include ensuring power is maintained at the sensor nodes, reducing installation and maintenance costs, and automating the collection and analysis of data provided by a wireless sensor network. In this work, a new "mobile host" WSN paradigm is presented. This architecture utilizes nodes that are deployed without resident power. The associated sensors operate on a mechanical memory principle. A mobile host, such as a robot or unmanned aerial vehicle, is used on an as-needed basis to charge the node by wireless power delivery and subsequently retrieve the data by wireless interrogation. The mobile host may be guided in turn to any deployed node that requires interrogation. The contribution of this work is the first field demonstration of a mobile host wireless sensor network. The sensor node, referred to as THINNER, capable of collecting data wirelessly in the absence of electrical power was developed. A peak displacement sensor capable of interfacing with the THINNER sensor node was also designed and tested. A wireless energy delivery package capable of being carried by an airborne mobile host was developed. Finally, the system engineering required to implement the overall sensor network was carried out. The field demonstration took place on an out-of-service, full-scale bridge near Truth-or-Consequences, NM.     

Energy-Efficient Transmission Range Optimization Model for WSN-Based Internet of Things

With the explosive advancements in wireless communications and digital electronics, some tiny devices, sensors, became a part of our daily life in numerous fields. Wireless sensor networks (WSNs) is composed of tiny sensor devices. WSNs have emerged as a key technology enabling the realization of the Internet of Things (IoT). In particular, the sensor-based revolution of WSN-based IoT has led to considerable technological growth in nearly all circles of our life such as smart cities, smart homes, smart healthcare, security applications, environmental monitoring, etc. However, the limitations of energy, communication range, and computational resources are bottlenecks to the widespread applications of this technology. In order to tackle these issues, in this paper, we propose an Energy-efficient Transmission Range Optimized Model for IoT (ETROMI), which can optimize the transmission range of the sensor nodes to curb the hot-spot problem occurring in multi-hop communication. In particular, we maximize the transmission range by employing linear programming to alleviate the sensor nodes’ energy consumption and considerably enhance the network longevity compared to that achievable using state-of-the-art algorithms. Through extensive simulation results, we demonstrate the superiority of the proposed model. ETROMI is expected to be extensively used for various smart city, smart home, and smart healthcare applications in which the transmission range of the sensor nodes is a key concern.     

事件驱动型传感器网络能量有效数据融合算法

从降低网络能耗和平衡网络负载的角度,提出了网络的一种能量有效的数据融合算法EFDAA,可应用于节点数量及覆盖度均较大的事件驱动型无线传感器网络.该算法采用正六边形网格划分方法,基于全网能量消耗模型计算所需的融合节点数,解决由于无规则选取融合节点数量而造成的网络能耗增加问题,并且能够优化融合节点的分布;为平衡网格内节点负载,以节点剩余能量、邻节点度和移动性作为选取融合节点的权重因子,基于距离信息自适应调整网格内节点间的单跳通信级别.仿真实验结果表明,融合节点数量的优选,降低了网络总的能量消耗;相比较于HEED算法,EFDAA有效延长了网络生命期.     

Optimum Sensor Placement for Impact Location Using Trilateration

A key problem associated with structural health monitoring (SHM) is the placement of sensors upon a structure to detect the existence, location, and the extent of any damage. Because input data coming from the sensors are groups of measurements, it is arguable that the most widely used approach to SHM nowadays is to consider it as a statistical pattern recognition problem. Artificial neural networks have made a great impact on pattern recognition practice. A problem associated with this monitoring strategy is to find a good compromise between the quality of information achieved by the sensor network, increasing with the sensor density, and the need to keep the minimum weight and instrumentation cost. Thus, the number of sensors must be kept under control, and a search of the optimal location of such sensors needs to be performed. All these aspects have been taken into account in the present work, dealing with the problem of optimum sensor placement for impact location on a multilayered composite structure. Multilayered composite structures may suffer particularly relevant trauma when subject to low‐velocity impacts, as they may produce non‐visible or barely visible damage on the structure surface, while remarkable subsurface delaminations may be present. Such hidden damage, when remaining undetected, may grow to catastrophic failure. To overcome this issue, a neural network approach has been used here to predict the impact locations on a composite panel from time‐dependent data recorded on a set of surface‐mounted piezoelectric sensors during an experimental impact test. A genetic algorithm has been used to find the optimal sensor layout that minimised the error in predicting the impact location. A new approach, based on trilateration, is discussed and compared with the traditional one and is shown to provide the same degree of accuracy at reduced computational cost.     

RF Energy Transmission for a Low-Power Wireless Impedance Sensor Node

The proper management of energy resources is essential for any wireless sensing system. With applications that span industrial, civil, and aerospace infrastructure, it is necessary for sensors and sensor nodes to be physically robust and power efficient. In many applications, a sensor network must operate in locations that are difficult to access, and often these systems have a desired operational lifespan which exceeds that of conventional battery technologies. In the present study, the use of microwave energy is examined as an alternate method for powering compact, deployable wireless sensor nodes. A prototype microstrip patch antenna has been designed to operate in the 2.4 GHz ISM band and is used to collect directed radio frequency (RF) energy to power a wireless impedance device that provides active sensing capabilities for structural health monitoring applications. The system has been demonstrated in the laboratory, and was deployed in field experiments on the Alamosa Canyon Bridge in New Mexico in August 2007. The transmitted power was limited to 1 W in field tests, and was able to charge the sensor node to 3.6 V in 27 s. This power level was sufficient to measure two piezoelectric sensors and transmit data back to a base station on the bridge.      

A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

There are numerous internet-connected devices attached to the industrial process through recent communication technologies, which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things (IIoTs). Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services, external trusted authorities, and centralized architectures; they have high computation and communication costs, low performance, and are exposed to a single authority of failure and bottleneck. Blockchain technology (BC) is widely adopted in the industrial sector for its valuable features in terms of decentralization, security, and scalability. In our work, we propose a decentralized, scalable, lightweight, trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application. The proposed scheme offers a double layer of data encryption, device authentication, scalability, high performance, low power consumption, and improves the industry’s operations; provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage. We also address data aggregation operations, which are considered challenging in electric power smart grids. We utilize a multi-chain proof of rapid authentication (McPoRA) as a consensus mechanism, which helps to enhance the computational performance and effectively improve the latency. The advanced reduced instruction set computer (RISC) machines ARM Cortex-M33 microcontroller adopted in our work, is characterized by ultra-low power consumption and high performance, as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm (ECDSA). This improves the computational execution, increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer. Our experimental results show that the proposed scheme achieves excellent performance, data security, real-time data processing, low power consumption (70.880 mW), and very low memory utilization (2.03% read-only memory (RAM) and 0.9% flash memory) and execution time (0.7424 s) for the cryptographic algorithm. This enables autonomous network reconfiguration on-demand and real-time data processing.     

An Integrated Sensing/Communication Architecture for Structural Health Monitoring

In this paper, a novel sensor network architecture for structural health monitoring is presented. The system is based on the adoption of purposely developed contactless sensors that make use of near-field coupling to both sense the structure displacements and deploy a local communication network. The key features of the adopted technology are the low-realization costs and the lower power consumption that nodes feature as compared to traditional wireless communication. In addition, the use of a fully decentralized positioning algorithm, running on each node, is demonstrated as a viable mean for the local mapping of extended structures, thus crediting the architecture for the evaluation of structural in-plane deformations.     

A Parallel Simulated Annealing Architecture for Model Updating in Wireless Sensor Networks

In recent years, wireless sensing technologies have provided a much sought-after alternative to expensive cabled monitoring systems. Wireless sensing networks forego the high data transfer rates associated with cabled sensors in exchange for low-cost and low-power communication between a large number of sensing devices, each of which features embedded data processing capabilities. As such, a new paradigm in large-scale data processing has emerged; one where communication bandwidth is somewhat limited but distributed data processing centers are abundant. By taking advantage of this grid of computational resources, data processing tasks once performed independently by a central processing unit can now be parallelized, automated, and carried out within a wireless sensor network. By utilizing the intelligent organization and self-healing properties of many wireless networks, an extremely scalable multiprocessor computational framework can be developed to perform advanced engineering analyses. In this study, a novel parallelization of the simulated annealing stochastic search algorithm is presented and used to update structural models by comparing model predictions to experimental results. The resulting distributed model updating algorithm is validated within a network of wireless sensors by identifying the mass, stiffness, and damping properties of a three-story steel structure subjected to seismic base motion.     

Integrated channel assignment and data aggregation routing problem in wireless sensor networks

In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.     

Self‐Powered Pulse Sensor for Antidiastole of Cardiovascular Disease

Cardiovascular diseases are the leading cause of death globally; fortunately, 90% of cardiovascular diseases are preventable by long‐term monitoring of physiological signals. Stable, ultralow power consumption, and high‐sensitivity sensors are significant for miniaturized wearable physiological signal monitoring systems. Here, this study proposes a flexible self‐powered ultrasensitive pulse sensor (SUPS) based on triboelectric active sensor with excellent output performance (1.52 V), high peak signal‐noise ratio (45 dB), long‐term performance (10⁷ cycles), and low cost price. Attributed to the crucial features of acquiring easy‐processed pulse waveform, which is consistent with second derivative of signal from conventional pulse sensor, SUPS can be integrated with a bluetooth chip to provide accurate, wireless, and real‐time monitoring of pulse signals of cardiovascular system on a smart phone/PC. Antidiastole of coronary heart disease, atrial septal defect, and atrial fibrillation are made, and the arrhythmia (atrial fibrillation) is indicative diagnosed from health, by characteristic exponent analysis of pulse signals accessed from volunteer patients. This SUPS is expected to be applied in self‐powered, wearable intelligent mobile diagnosis of cardiovascular disease in the future.     

基于太阳能供电的无线振动传感器试验研究

针对铁路、桥梁、船舶等结构振动巡查中监测节点的自供电问题,研制了一种利用太阳能供电的振动信号无线传输模块.并对一悬臂梁的振动信号进行了室外发射与接收试验.试验结果与理论计算结果基本吻合,验证了设计方案的可行性.     

Fatigue Life Estimation of Structural Components Using Macrofibre Composite Sensors

Abstract: Recently, a number of different structural health monitoring (SHM) techniques have been developed for the online inspection of air, land and sea engineering structures. Various smart materials are employed for detecting eminent damage in situ. Fatigue cracks in structural components are the most common cause of structural failure when exposed to fatigue loading. Fatigue design of structural components is typically accomplished either using a set of stress cycle (S‐N) data obtained from prior fatigue tests or using the fracture mechanics approach. The fracture mechanics approach considers the fatigue life of structures as a summation of crack initiation life and crack propagation life. The stress intensity factor (SIF) is required for the estimation of fatigue crack propagation life from the linear elastic fracture mechanics (LEFM) perspective. However, the accurate prediction of the SIF is difficult especially when the geometry or the boundary conditions of a structure becomes complex. In this study, a SHM application of macrofibre composite (MFC) sensors is presented. A set of MFC sensors is used for the real‐time measurement of the SIF. The measured values of the SIF are later used for the prediction of the crack propagation life. The impedance‐based SHM technique using the same set of MFC sensors is employed for the detection of crack initiation life.     

Optimality properties and performance analysis of co-operative time-reversal communication in wireless sensor networks

The properties and performance of a technique, called time reversal, for co-operative communication on power-constrained wireless sensor networks are studied. A brief discussion of the optimality properties of this approach is presented, and performance is studied experimentally via a simulated indoor environment containing multiple wireless sensors. Using numerical simulation, the behaviour of the peak power received at a target sensor as a function of the number of co-operating transmitting sensors as well as the level of transmitted signal distortion and timing synchronisation errors, is studied. The simulation results demonstrate that, subject to some rather stringent synchronisation requirements, time reversal is an effective generalisation of beamforming that provides an efficient basis for co-operative communication on broadband multipath channels     

An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks With Energy-Hungry Sensors

Energy conservation techniques for wireless sensor networks generally assume that data acquisition and processing have energy consumption that is significantly lower than that of communication. Unfortunately, this assumption does not hold in a number of practical applications, where sensors may consume even more energy than the radio. In this context, effective energy management should include policies for an efficient utilization of the sensors, which become one of the main components that affect the network lifetime. In this paper, we propose an adaptive sampling algorithm that estimates online the optimal sampling frequencies for sensors. This approach, which requires the design of adaptive measurement systems, minimizes the energy consumption of the sensors and, incidentally, that of the radio while maintaining a very high accuracy of collected data. As a case study, we considered a sensor for snow-monitoring applications. Simulation experiments have shown that the suggested adaptive algorithm can reduce the number of acquired samples up to 79% with respect to a traditional fixed-rate approach. We have also found that it can perform similar to a fixed-rate scheme where the sampling frequency is known in advance.      

Wireless Industrial Monitoring and Control Using a Smart Sensor Platform

A wireless smart sensor platform (based on patent pending technologies, Ramamurthy ) targeted for instrumentation and predictive maintenance systems is presented. The generic smart sensor platform with "plug-and-play" capability supports hardware interface, payload and communications needs of multiple inertial and position sensors, and actuators, using a RF link (Wi-Fi, Bluetooth, or RFID) for communications, in a point-to-point topology. The design also provides means to update operating and monitoring parameters as well as sensor/RF link specific firmware modules "over-the-air." Sample implementations for industrial applications and system performance are discussed     

某钢结构梁高温切割全过程的监测与分析

为保证某大型生产车间钢框架平台梁在设备荷载作用下改造过程中的安全性,采用ANSYS软件对改造方案进行了有限元分析,并采用光纤光栅传感器对其改造全过程进行了实时可视化监测。通过钢梁的有限元结构分析以及高温切割过程中钢梁温度场分析,确定了钢梁的切割方案,同时为相应高温环境下的结构实时监测方案设计、传感器的布设以及实时监测预警阀值的设定提供了依据。监测过程中实时获取了现场钢梁的工作状态,并对实时监测数据进行了快速分析和评价,从而判断钢梁的安全性,以保证钢梁切割过程的顺利进行。监测结果表明,根据监测方案可以快速评价钢梁的受力状态,为切割机的前进速度提供指导,同时表明光纤光栅应变和温度传感器完全满足高温环境下的测量要求。可视化监测方案在快速预警方面的成功应用可为类似的结构改造提供结构健康监测依据。     

An overview of wireless structural health monitoring for civil structures

Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring and infrastructure asset management. This paper is a summary of research efforts that have resulted in the design of numerous wireless sensing unit prototypes explicitly intended for implementation in civil structures. Wireless sensing units integrate wireless communications and mobile computing with sensors to deliver a relatively inexpensive sensor platform. A key design feature of wireless sensing units is the collocation of computational power and sensors; the tight integration of computing with a wireless sensing unit provides sensors with the opportunity to self-interrogate measurement data. In particular, there is strong interest in using wireless sensing units to build structural health monitoring systems that interrogate structural data for signs of damage. After the hardware and the software designs of wireless sensing units are completed, the Alamosa Canyon Bridge in New Mexico is utilized to validate their accuracy and reliability. To improve the ability of low-cost wireless sensing units to detect the onset of structural damage, the wireless sensing unit paradigm is extended to include the capability to command actuators and active sensors.     

Highway Bridge Assessment Using an Adaptive Real-Time Wireless Sensor Network

A real-time wireless sensor network platform capable of maintaining lossless data transmission over several minutes of continuous, high-rate sampling is presented in this paper. The platform was designed specifically to provide the capability to enable expeditious system identification, as well as load rating of highway bridges without compromising the typical data acquisition parameters employed in comparable cable-based tests. Consequently, the hardware signal conditioning interface permits data collection from a variety of sensors typical to structural health monitoring, including accelerometers, strain transducers, and temperature sensors. The embedded software features a proprietary network transmission protocol capable of lossless, real-time delivery of up to 40 measurement channels at an effective sampling rate of 128 samples per second per channel. Documented in this paper is a field study on an end-of-service highway bridge in which ambient vibration monitoring was performed using 60 accelerometers interfaced with 30 wireless sensor nodes operating within one of two simultaneously operating star topology networks. In addition, an experimental load rating of the entire structure was performed through large-scale strain measurement facilitated by the same wireless sensor network platform.     

Structural health monitoring for smart composites using embedded FBG sensor technology

Abstract

Fibre-optic Bragg grating (FBG) sensors have been recognised as one of the smart localised and globalised structural health monitoring devices for many structural applications. A particular interest has been placed on embedding these sensors into advanced composites for in situ manufacturing process monitoring and then, lifetime structural health monitoring (SHM). There is no doubt that the need of maintaining structural integrity of these composites has been increased owing to an increasing use of carbon and glass fibre composites in real life structural and engineering applications. In the public transportation, the structural components of Airbus 350 XWB and Boeing 787 are made by over 50% of composite materials to replace traditional aluminium alloys. Electric vehicles have used lightweight carbon fibre composites as their chassis to overcome the weight penalty from batteries. With the advantages of high specific stiffness to weight ratio and good damping properties of polymer based composites, these composites are also used to reinforce and strengthen civil concrete structures that are located on the earthquake zones. In some critical engineering components, glass fibre composites with embedded shape memory alloy (SMA) wires are used for stiffness and shape controls during marginally operational conditions. Therefore, developing better SHM technologies is an urgent need to ensure the structural integrity and safety of structures. In this paper, FBG sensors for different SHMs are introduced and discussed. The use of the sensors with appropriate design for smart composites with sensing and actuating capacities is also presented.