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Failure in dynamic structures poses a pressing need for fault detection systems. Interconnected sensor nodes of wireless sensor networks (WSN) offer a solution by communicating information about their surroundings. Nonetheless, these battery-powered sensors have an immense labor cost and require periodical battery maintenance and replacement. Batteries pose a significant environmental threat that is expected to cause irreversible damage to the ecosystem. We introduce a fully integrated vibration-powered energy harvester sensor system that is interfaced with a custom-developed fault detection app. Vibrations are used to power a radio frequency (RF) transmitter that is integrated with the vibration sensor subunit. The harvester-sensor unit is comprised of dual moving magnets that are bordered by coil windings for power and signal generation. The power generated from the harvester is used to operate the transmitter while the signal generated from the sensor is transmitted as a vibration signal. Transmitted values are streamed into a high precision fault detection app capable of detecting the frequency of vibrations with an error of 1%. The app employs an FFT algorithm on the transmitted data and notifies the user when a threshold vibration level is reached. The total energy consumed by the transmitter is 0.894 µJ at a 3 V operation. The operable acceleration of the system is 0.7 g [m/s2] at 5–10.6 Hz.

Structural vibration testing is an effective guarantee for the Structural Health Monitoring (SHM) of large-scale civil engineering. Traditional vibration testing has drawbacks such as difficulties in wiring and picking up low-frequency signals, low communication speed, and susceptibility to testing site conditions. In order to improve the universality of wireless vibration sensors, this article develops a wireless vibration sensor, introduces the module composition and basic principles of the sensor, and conducts standard vibration table performance comparison tests between wired acceleration sensors and wireless vibration sensors, verifying the accuracy of wireless vibration sensors. In order to further explore the feasibility of wireless vibration sensor applications, the wired acceleration sensor and wireless vibration sensor were used to analyze the structural dynamic characteristics of the four-layer steel frame structure model in the laboratory, and the comparison was made based on ABAQUS finite element simulation. Finally, the field vibration test was carried out outdoors. The results show that the natural frequency identification results of the wireless vibration sensor and the wired acceleration sensor for the four-story steel frame structure through fast Fourier transform, short-time Fourier transform, and wavelet transform are basically the same, the half-power bandwidth method and logarithmic decrement rate method are used to identify the damping, and wavelet transform is used to identify the vibration mode with minimal error and high accuracy. It shows that the wireless vibration sensor is feasible in practical engineering, has stable and reliable transmission capacity, and can provide certain reference values for earthquake monitoring, building Structural Health Monitoring, etc.

Suspension bridges are supported by main cables that continue beyond the pillars to deck-level supports and must be anchored at each end of the bridge. The dynamic characteristics of the main cables are key indicators used to assess the structural health status of a bridge. In situ real-time health monitoring is an effective way to assess the dynamic characteristics. This paper presents a case study using vibration-based wireless smart sensors deployed on the main cables of a large-scale three-pylon suspension bridge to obtain its dynamic features. The methods of anti-aliasing filtering, statistical analysis and main cable tension force estimation were proposed and embedded into wireless smart sensors to provide autonomous data processing. According to the analysis of the vibration data from the main cables, the results demonstrate that the main cables have been in a stable state over time, and wireless smart sensors are promising for autonomous main-cable monitoring of large-scale three-pylon suspension bridges.

In order to study the stability and safety of the foundation pit supporting structure system, the wireless tilting vibration sensor is used to discuss the underground monitoring of the underground engineering, the horizontal displacement monitoring of the foundation pit support and the deep foundation pit supporting structure. The actual measured data is compared with the mathematical simulation values. The results show that the foundation pit engineering and shield tunnel engineering can use the wireless vibration tilt sensor for underground engineering monitoring. The width of the foundation pit and the way of excavation are the main factors affecting the internal force and stability of the steel support. With the layered excavation of the foundation pit, the deformation of the retaining structure also has obvious segmental features. The sub-section excavation according to the space-time effect can effectively reduce the lateral displacement of the retaining structure. Therefore, for the structural characteristics of underground engineering, the wireless vibration tilt sensor and the method of finite element numerical simulation are combined. The joint support system of steel support and retaining pile has a coordination relationship between deformation and force. The steel support can effectively control the deformation of the envelope structure. When the steel support is erected and 60% of the design axial force value is added, the deformation of the retaining structure can be better reduced. Furthermore, the safety and stability of the structure is increased.

Traditional structures adopt a split design with vibration control, energy harvesting and monitoring, which is difficult to meet the needs of technological development. The development of new structure from a single function to a multifunctional integration structure requires that the structure not only has the characteristics of low-frequency vibration control and energy harvesting, but also takes into account functions such as sensing, fault diagnosis and health monitoring. Given the continuously growing trend of multifunctional integration research, this paper presents the latest review on multifunctional integration of nonlinear vibration control, energy harvesting and monitoring. This is an interdisciplinary topic related to structural dynamics, mechanical design and power electronics, which has great prospects in various potential applications. The nonlinear design of new multifunctional integration structure is an essential step in the development of vibration control and energy harvesting, and is one of the most effective technical means to improve performance in low-frequency excitation environments. Therefore, the main implementation methods of nonlinear vibration control are discussed in detail. Subsequently, different strategies for nonlinear vibration energy harvesting and the challenges faced by wireless sensor network monitoring are described. On this basis, the research status, engineering applications and research trends of multifunctional integration structure are introduced in detail.

Wireless sensor nodes (WSNs) are the fundamental part of an Internet of Things (IoT) system for detecting and transmitting data to a master node for processing. Several research studies reveal that one of the disadvantages of conventional, battery-powered WSNs, however, is that they typically require periodic maintenance. This paper aims to contribute to existing research studies on this issue by exploring a new energy-autonomous and battery-free WSN concept for monitor vibrations. The node is self-powered from the conversion of ambient mechanical vibration energy into electrical energy through a piezoelectric transducer implemented with lead-free lithium niobate piezoelectric material to also explore solutions that go towards a greener and more sustainable IoT. Instead of implementing any particular sensors, the vibration measurement system exploits the proportionality between the mechanical power generated by a piezoelectric transducer and the time taken to store it as electrical energy in a capacitor. This helps reduce the component count with respect to conventional WSNs, as well as energy consumption and production costs, while optimizing the overall node size and weight. The readout is therefore a function of the time it takes for the energy storage capacitor to charge between two constant voltage levels. The result of this work is a system that includes a specially designed lead-free piezoelectric vibrational transducer and a battery-less sensor platform with Bluetooth low energy (BLE) connectivity. The system can harvest energy in the acceleration range [0.5 g–1.2 g] and measure vibrations with a limit of detection (LoD) of 0.6 g.

Construction activities often generate intensive ground-borne vibrations that may adversely affect structure safety, human comfort, and equipment functionality. Vibration monitoring systems are commonly deployed to assess the vibration impact on the surrounding environment during the construction period. However, traditional vibration monitoring systems are associated with limitations such as expensive devices, difficult installation, complex operation, etc. Few of these monitoring systems have integrated functions such as in situ data processing and remote data transmission and access. By leveraging the recent advances in information technology, an Internet of Things (IoT) sensing system has been developed to provide a promising alternative to the traditional vibration monitoring system. A microcomputer (Raspberry Pi) and a microelectromechanical systems (MEMS) accelerometer are adopted to minimize the system cost and size. A USB internet dongle is used to provide 4G communication with cloud. Time synchronization and different operation modes have been designed to achieve energy efficiency. The whole system is powered by a rechargeable solar battery, which completely avoids cabling work on construction sites. Various alarm functions, MySQL database for measurement data storage, and webpage-based user interface are built on a public cloud platform. The architecture of the IoT vibration sensing system and its working mechanism are introduced in detail. The performance of the developed IoT vibration sensing system has been successfully validated by a series of tests in the laboratory and on a selected construction site.

Due to resource constraints in wireless sensor networks and the presence of unwanted conditions in communication systems and transmission channels, the suggestion of a robust method which provides battery lifetime increment and relative security is of vital importance. This paper considers the secure communication in wireless sensor networks based on new robust adaptive finite time chaos synchronization approach in the presence of noise and uncertainty. For this purpose, the modified Chua oscillators are added to the base station and sensor nodes to generate the chaotic signals. Chaotic signals are impregnated with the noise and uncertainty. At first, we apply the modified independent component analysis to separate the noise from the chaotic signals. Then, using the adaptive finite-time sliding mode controller, a control law and an adaptive parameter-tuning method is proposed to achieve the finite-time chaos synchronization under the noisy conditions and parametric uncertainties. Synchronization between the base station and each of the sensor nodes is realized by multiplying a selection matrix by the specified chaotic signal which is broadcasted by the base station to the sensor nodes. Simulation results are presented to show the effectiveness and applicability of the proposed technique.

A Wireless Smart Sensor (WSS) has an embedded processor, which is employed for signal processing, communication, and integration capabilities. A state-of-the-art review of recent articles on the WSS technologies employed in Structural Health Monitoring (SHM) is presented in this paper. Different types of WSS and communication technologies are reviewed, and their advantages and disadvantages are pointed out. WSS networks provide a number of advantages for SHM such as robust data management, higher flexibility, low cost, and high potential for providing data for a better understanding of structural response and behavior. Hybrid platforms, fusing different technological platforms, appear to be promising schemes as the strengths of each technology are exploited. Next-generation WSS must consume less power, integrate more with new sensors, have improved noise immunity, and be capable of working with a huge quantity of data without losses produced by wireless communication. Power harvesting based on wind, solar, and structural vibration energy needs to be explored further for a long-term period. Truly smart sensors should have an inherent pattern recognition and machine learning capabilities. Authors advance the research ideology of integrating the sensor technology with recent advances in machine learning technologies.

In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor.