Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner

Enhanced variable reluctance energy harvesting for self-powered monitoring

With the rapid development of microelectronic technology, wireless sensor nodes have been widely used in rotational equipment for health condition monitoring. However, for many low-frequency applications, there still remains an open issue of harvesting sufficient electrical energy to provide long-term service. Therefore, in this paper an enhanced variable reluctance energy harvester (EVREH) is proposed for self-powered health monitoring under low-frequency rotation conditions. A periodic arrangement of magnets and teeth is employed to achieve frequency up-conversion for performance enhancement under a specific space constraint. In addition, the permeance of the air gap is calculated by the combined magnetic field division and substituting angle method, and the output model of the EVREH is derived for parametric analysis based on the law of electromagnetic induction. Simulations and experimental evaluations under a range of structural parameters are then carried out to verify the effectiveness of the proposed model and investigate the output performance of the proposed harvester. The experimental results indicate that the proposed energy harvester could produce a voltage of 8.7 V and a power of 726 mW for a rotational speed of 200 rpm, with a power density of 0.545 mW/(cm3∙Hz2). Moreover, a self-powered wireless sensing system based on the proposed energy harvester is demonstrated, obtaining a vibration spectrum of the rotating motor and stator which can determine the health state of the system during low rotational speeds. Therefore, this autonomous self-sensing experiment verifies the potential of the EVREH for self-powered monitoring in low-frequency rotation applications.</p

WIRELESS OFFSHORE PLATFORM STRUCTURAL HEALTH MONITORING

Oil platforms are known for their operation in dangerous environments. The most recent technology adapted is the unmanned platform which is a remotely operated platform without any workers on the platform during the operation to lessen the losses occurs in the platforms. To ensure the safety and the reliability of the new platforms a safety monitoring system is required to be developed. In this report, a new structural health and safety monitoring system for unmanned platforms is proposed and developed. The objectives of the project are to develop a system which processes electrical signals to represent structural parameters, develop the proper communication between different parts of the system and test the feasibility of the system. The new system integrates microprocessor technologies and communication technologies to meet the objectives of the proposed system. The project focused on testing the proper connection between the microprocessor and the measuring devices, and studying and simulating the wireless and underwater communication. The system was tested using strain gages to measure strain and half-cell to measure corrosion. The readings obtained were validated against commercial acquisition systems. The results show the efficiency of the system in different applications to measure different structural parameters. The underwater transmission was simulated using OMNET++. The simulation results show low efficiency of acoustic communication which requires further study and investigation

Wireless Sensor Networks for Condition Monitoring in the Railway Industry : a Survey

In recent years, the range of sensing technologies has expanded rapidly, whereas sensor devices have become cheaper. This has led to a rapid expansion in condition monitoring of systems, structures, vehicles, and machinery using sensors. Key factors are the recent advances in networking technologies such as wireless communication and mobile adhoc networking coupled with the technology to integrate devices. Wireless sensor networks (WSNs) can be used for monitoring the railway infrastructure such as bridges, rail tracks, track beds, and track equipment along with vehicle health monitoring such as chassis, bogies, wheels, and wagons. Condition monitoring reduces human inspection requirements through automated monitoring, reduces maintenance through detecting faults before they escalate, and improves safety and reliability. This is vital for the development, upgrading, and expansion of railway networks. This paper surveys these wireless sensors network technology for monitoring in the railway industry for analyzing systems, structures, vehicles, and machinery. This paper focuses on practical engineering solutions, principally,which sensor devices are used and what they are used for; and the identification of sensor configurations and network topologies. It identifies their respective motivations and distinguishes their advantages and disadvantages in a comparative review

Energy Harvesting Based Wireless Sensor Nodes for The Monitoring Temperature of Gearbox

Temperatures are effective indicators of the health of many ma-chines such as the wind turbine gearboxes, bearings, engines, etc. This paper pre-sents a novel wireless temperature sensor node powered by a thermal harvester for monitoring the status of gearboxes. A thermoelectric generator module (TEG) is optimized to harvest the electrical power from a heat source such as the gear-box undergoing such monitoring. The power generation from this method is ob-tained based on temperature gradients emanated by sandwiching the TEG be-tween the two aluminum plates. One plate is exposed to the heat source and has the role of a heat collector, whereas the other plate, mounted with a low profile heat-sink, acts as a heat spreader. The harvested power is then used to power a wireless temperature node for condition monitoring, resulting in a powerless and wireless monitoring system. To evaluate the system, an industrial gearbox is monitored by the designed temperature node. The node is fabricated using a TEG module; an LTC3108 DC-DC converter for boosting the voltage, a super-capacitor for energy storage and a CC2650 sensor tag for measuring the temperature of the gearbox. The temper-ature data is transferred via the Bluetooth Low Energy and then monitored using portable monitoring devices, such as a mobile phones. The results obtained show the system can provide a continuous monitoring of the temperature information

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

Wearable electroencephalography for long-term monitoring and diagnostic purposes

Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.Open Acces