Sensor Networks For Health Monitoring Research Articles

Zigbee-Based Wireless Sensor Network of MEMS Accelerometers for Pavement Monitoring

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.

Economic Impact Assessment of Structural Health Monitoring Systems on the Lifecycle of a Helicopter Blade

Structural Health Monitoring Systems (SHMS) are currently widely investigated in the literature, however, their application to the aerospace industry is still limited. One of the reasons lies in the lack of methods aimed at evaluating their economic impact on the lifecycle of the structural element to be monitored [1]. In this work the economic impact of FBG-based SHMS was evaluated on a composite helicopter rotor blade, considering two perspectives: Beginning Of Life (BOL), concerning the blade production, and Middle Of Life (MOL), concerning its usage. The processes were modeled using the IDEF0 methodology, which allowed deriving the cost models for two scenarios: the current one, and the one including the SHMS. In BOL, the SHMS was supposed providing support to the development of new composite rotor blades, including the curing cycle definition and certification tests. In MOL the SHMS was supposed performing automatic scheduled inspections coupled with the traditional ones. The cost model was implemented in the Probabilistic Damage Tolerance Analysis [2] to compare the scenarios having the same blade Probability Of Failure and considering the performances of the SHMS in terms of Probability Of Detection and Probability of False Alarm. Results show that an economic benefit may be achieved using the SHMS in the development of new blades, potentially reducing the number of blades tested and number of autoclave cycles. Moreover, it was found that the traditional scheduled detailed inspection intervals could be extended if automatic SHMS inspections are performed in addition, maintaining the same Probability Of Failure. [1] Büchter, K. D. et al. (2022). Modeling of an aircraft structural health monitoring sensor network for operational impact assessment. Structural Health Monitoring, 21(1), 208-224. [2] Lin, K. Y., & Styuart, A. V. (2007). Probabilistic approach to damage tolerance design of aircraft composite structures. Journal of Aircraft, 44(4), 1309-1317.

Development of sustainable conductive cementitious composite using graphite-coated spent catalyst waste

This study presents an innovative approach to developing sustainable conductive composites by coating graphite onto the surface of spent catalyst waste through nano-surface engineering techniques. The process ensures uniform adsorption of graphite onto the surface of the spent catalyst waste particles, followed by oven treatment and milling. This results in better integrity and effective bonding, leading to the production of graphite-coated spent catalyst waste (G-SCW). Scanning electron microscopy indicates the successful coating of spent catalyst waste with graphite. The research investigates the effect of G-SCW on the cementitious properties of paste and mortar. Incorporating G-SCW results in acceptable workability and setting time, while the compressive strength increases at early and later stages, with up to 20 % G-SCW content. The addition of G-SCW in the mortar significantly reduces the electrical resistivity, resulting in a 63 % reduction in resistivity compared to the reference mix, thereby enhancing the conductivity. Hydration studies confirm the presence of pozzolanic reaction in blended paste, as evidenced by a decrease in calcium hydroxide content. The sustainability assessment indicates a substantial reduction in embodied carbon and possibly producing mortar with lower cement content. These findings suggest great potential for developing sustainable conductive mortar with G-SCW, enabling smart building construction, and supporting sensor networks for structural health monitoring.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures.

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson's correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

Wireless sensor network for structural health monitoring using RFID based data mules

The wireless system did the huge cost-cutting in monitoring the structure so, now it can be used permanently as an integral part of the system as a smart infrastructure that will give them real-time information the structure. The wireless devices transmit the collected data about cracks, displacement, and excess vibration in slab-tracks. The train which will collect the data and train will be used as a data mule in this paper which will upload the information to a remote-control centre. The data which will be collected stored in the database and to know the status of the track a query will be fired from an application. In this paper, many design for communication systems are proposed which are efficient, with fine accuracy, and most importantly it is a low-cost system.

Development and metrological characterization of cement-based elements with self-sensing capabilities for structural health monitoring purposes

Mortar specimens containing conductive additions (i.e., biochar and recycled carbon fibres – both alone and together, and graphene nanoplatelets) were characterized from a metrological point of view. Their piezoresistive capability was evaluated, exploiting the 4-electrode Wenner’s method to measure electrical impedance in alternating current (AC); in this way, both material and electrode-material polarization issues were avoided. The selected mix-design was used to manufacture scaled concrete beams serving as demonstrators. Additionally, FEM-based models were realized for a preliminary analysis of the modal parameters that will be investigated through impact tests conducted after different loading tests, simulating potential seismic effects. The results show that the combined use of recycled carbon fibers and biochar provide the best performance in terms of piezoresistivity (with a sensitivity of 0.109 (µm/m)-1 vs 0.003 (µm/m)-1 of reference mortar). Conductive additions improve the Signal-to-Noise Ratio (SNR) and increase the material electrical conductivity, providing suitable tools to develop a distributed sensor network for Structural Health Monitoring (SHM). Such a monitoring system could be exploited to enhance the resilience of strategic structures and infrastructures towards natural hazards. A homogeneous distribution of conductive additions during casting is fundamental to enhance the measurement repeatability. In fact, both concrete intrinsic properties and curing effect (hydration phenomena, increasing electrical impedance) cause a high variability.

Design and Implementation of a Wireless Sensor Network for Structural Health Monitoring

Design and Implementation of a Wireless Sensor Network for Structural Health Monitoring

Wireless sensor networks for bridge structural health monitoring: a novel approach

Wireless sensor networks for bridge structural health monitoring: a novel approach

Battery lifespan enhancement strategies for edge computing-enabled wireless Bluetooth mesh sensor network for structural health monitoring

Wireless sensor networks (WSN) offer an automated, convenient, and low-cost option for building IoT networks for monitoring the structural health of complex infrastructures. The limited bandwidth of a typical WSN is not favorable for intensive data transfer. Therefore, utilizing edge computing that allows raw data to be processed and compressed at IoT devices enables efficient data transfer to the central terminal for data storage and further analytics. However, edge computing requires an additional microcontroller unit (MCU), which significantly increases the edge device power consumption and thus reduces the device's battery lifespan. In this paper, three strategies are developed for saving the battery power of sensor devices with edge computing: (1) managing the connectivity settings of the wireless protocol settings, (2) managing the operations of the edge computing chip, and (3) adding battery capacity with parallel connections. The first strategy focuses on fine-tuning the trade-off between the WSN data throughput and power consumption. The second strategy develops two modes for power saving in certain computationally relaxing periods: Shutdown mode and Low-power mode. The Shutdown mode suspends the operations of the edge computing chip, allowing the edge computing chip to run for a limited time per day. The Low-power mode allows continuous operation of the edge computing chop at low clock speed, which consumes less current. The third strategy doubles the device lifespan by doubling the battery charge capacity using parallel connections. Combining all three strategies significantly enhances the sensor battery lifespan from three weeks to at least six months, notably reducing the frequency of on-site hardware maintenance while providing smooth data transfer due to edge computing.

Prediction of frequency and spatially dependent attenuation of guided waves propagating in mounted and unmounted A380 parts made up of anisotropic viscoelastic composite laminates

Monitoring damage in composite structures using guided wave-based techniques is particularly effective due to their excellent ability to propagate over relatively long distance and hence to cover a large area with few testing time and equipment. The industrialization of this method is highly tributary of the number and placement of the active elements. Yet, the optimal sensorization of a structure relies on the decrease in amplitude of guided waves over propagation distance. A reliable prediction of attenuation of guided waves is still a challenge especially for anisotropic viscoelastic composite materials which exhibit complex changes of attenuation with propagation direction and thus a spatial dependency of attenuation. In this paper, the damped global matrix method (dGMM), having stable and efficient merits, is developed to predict the frequency and spatially dependent attenuation of waves propagating in anisotropic composite materials. dGMM integrates three damping models (Hysteretic, Kelvin-Voigt, and Biot models) into the conventional undamped GMM to consider viscoelasticity of composite laminates. The proposed dGMM is first theoretically validated by numerical comparison with the semi-analytical finite element method. In addition, two industrial case studies, parts of an A380 nacelle at scale one, are employed to experimentally validate the proposed attenuation prediction method. The first one is a fan cowl structure and the second one is an inner fixed structure, both either unmounted or mounted on an actual instrumented A380 plane. This makes the validation extremely valuable for both the scientific and industrial communities. The proposed attenuation prediction method thus paves the way to optimally deploy sensor network for structural health monitoring of anisotropic viscoelastic composite structures.

Development of a Wireless Sensor Network for Structural Health Monitoring

The importance of wireless sensor networks for monitoring structural health is constantly developing in response to the rising need for security and safety in urban areas. As wireless technology has improved, wireless sensor networks have become an integral part of structural monitoring systems. When compared to traditional wired systems, structural health monitoring systems based on wireless sensor networks provide novel technology with tempting advantages, such as reduced installation and maintenance costs. Due to structural health monitoring, wireless sensor networks now have to deal with even more challenging network design problems. This report summarizes the extensive knowledge gained by researchers using wireless sensor networks for structural health monitoring. We look into the design, functioning, communication methods, and well-known operating systems of wireless sensor nodes, as well as the technologies used in both wired and wireless sensor systems. The most up-to-date summaries of academic and commercial wireless platform technologies are then tallied and evaluated in the context of testbed and field deployments for applications related to structural health monitoring. After this, the primary obstacles connected with using wireless sensor networks for structural health monitoring are outlined, and the feasibility of applying wireless technology for such applications is discussed at length. Finally, the issues that still need fixing in wireless sensor networks for structural health monitoring are analyzed.

A Comprehensive Review on Wearable Health Monitoring Systems

This paper presents a comprehensive review of the wearable healthcare monitoring systems proposed by the researchers to date. One of the earliest wearable recorders, named “a silicon locket for ECG monitoring”, was developed at the Indian Institute of Technology, Bombay, in 2003. Thus, the wearable health monitoring systems, started with the acquisition of a single signal/ parameter to the present generation smart and affordable multi-parameter recording/monitoring systems, have evolved manifolds in these two decades. Wearable systems have dramatically changed in terms of size, cost, functionality, and accuracy. The early-day wearable recorders were with limited functionalities against today’s systems, e.g., Apple’s iWatch which comprises abundant health monitoring features like heart rate monitoring, breathing app, accelerometers, smart walking/ activity monitoring, and alerts. Most of the present-day smartphones are not only capable of recording various health features like body temperature, heart rate, photoplethysmograph (PPG) signal, calory consumption, smart activity monitoring, stress measurement, etc. through different apps, but they also help the user to get monitored by a family physician via GSM or even internet of things (IoT). One of the latest, state-of-the-art real-time personal health monitoring systems, Wearable IoT-cloud-based health monitoring system (WISE), is a beautiful amalgamation of body area sensor network (BASN) and IoT framework for ubiquitous health monitoring. The future of wearable health monitoring systems will be far beyond the IoT and BASN.

Modeling of an aircraft structural health monitoring sensor network for operational impact assessment

Structural health monitoring (SHM) of aircraft components can improve maintenance operations, potentially reducing costs for inspections, unscheduled maintenance events, and unexpected delays. On the other hand, aircraft safety and net present value can be adversely influenced by false alarms, missed detections, system costs, and weight and power requirements of the SHM system. In order to gain a better understanding into the latter, we present a weight and power model for a sensor network, comprising sensors, interrogators, data collectors, and wiring. We assess the net benefit of using SHM in terms of reduced expenditure as function of network coverage, considering a corresponding potential in reducing the inspection effort.

Analysis of different cylindrical magnet and coil configurations for electromagnetic vibration energy harvesters

Electromagnetic vibration energy harvesting is a relatively new technology that transforms kinetic energy from mechanical vibrations into electrical energy, allowing the substitution of batteries or cables for powering ultra-low-power devices like wireless sensor networks for structural health monitoring. For this aim, different magnet and coil configurations have been proposed for the design of these harvesters by several researchers. In this paper, four cylindrical “Magnet in-line coil” configurations with back steel, which include a typical single-magnet, a double-magnet array, and two proposed cylindrical Halbach magnet arrays of three and five magnets, are analyzed using the finite element method and compared in terms of their magnetic flux linkage and transduction factor. The numerical simulations are conducted in all cases with the same materials properties, coil parameters, and geometrical boundaries, the latter consisting of the total cross-sectional area of the magnets and the coil, the air gaps, and the total volume of the transducer mechanism. Furthermore, the design that provides the best performance is analyzed with two different coil configurations. It is finally found that the proposed cylindrical Halbach magnet array with three magnets and one-center coil presents the best results, reaching an average transduction factor of 95.83 Vs/m and a normalized power density of 19.72 mW/cm3g2.

Analysis of different cylindrical magnet and coil configurations for electromagnetic vibration energy harvesters

Electromagnetic vibration energy harvesting is a relatively new technology that transforms kinetic energy from mechanical vibrations into electrical energy, allowing the substitution of batteries or cables for powering ultra-low-power devices like wireless sensor networks for structural health monitoring. For this aim, different magnet and coil configurations have been proposed for the design of these harvesters by several researchers. In this paper, four cylindrical “Magnet in-line coil” configurations with back steel, which include a typical single-magnet, a double-magnet array, and two proposed cylindrical Halbach magnet arrays of three and five magnets, are analyzed using the finite element method and compared in terms of their magnetic flux linkage and transduction factor. The numerical simulations are conducted in all cases with the same materials properties, coil parameters, and geometrical boundaries, the latter consisting of the total cross-sectional area of the magnets and the coil, the air gaps, and the total volume of the transducer mechanism. Furthermore, the design that provides the best performance is analyzed with two different coil configurations. It is finally found that the proposed cylindrical Halbach magnet array with three magnets and one-center coil presents the best results, reaching an average transduction factor of 95.83 Vs/m and a normalized power density of 19.72 mW/cm3g2.

Structural Health Monitoring of Multi-Storey Frame Structures using Piezoelectric Incompatibility Filters: Theory and Numerical Verification

In the present paper, we develop a novel method for structural health monitoring of multi-storey frame structures with the capability to detect and localise local damage. The method uses so-called spatial incompatibility filters, which are continuously distributed strain-type sensors only sensitive to incompatibilities. In the first part of the paper the concept of incompatibility filters is introduced for multi-storey frame structures and it is shown how these filters can be used to detect and localise local cracks in frame structures. In the second part of the paper we study the use of incompatibility filters put into practice by piezoelectric sensor networks for structural health monitoring of a three-storey frame structure. The design of the piezoelectric sensor network is based on an analytical analysis of the frame structure within the framework of the method developed in the first part of the paper and a numerical verification using three-dimensional Finite Elements completes the paper

Structural Health Monitoring of Bridges Using WSNs

Wireless sensor networks (WSNs) for Structural health monitoring (SHM) has earned immense attention in research as it has potential to decrease expenses associated with the maintenance cum installation of these monitoring systems. SHM systems, traditionally are being utilized to screen perilous substructures such as bridges, tall buildings, fields and is capable to increase structure lifetime and safety of public. Earlier wired networks were used by SHM. But this resulted in heavy cost in terms of maintenance and deployment. This project introduces a wireless sensor network using Bluetooth nodes for detection of damage if any in any type of structure in which the data is processed locally in real time and helps in sending alerts once the damage is identified.

Realtime soldier's health monitoring system incorporating low power LoRa communication

In this paper, we have implemented a low-cost embedded system developed for soldier's assistance. The system consists of interconnected body sensor networks for real-time health monitoring and environmental analysis of the soldier and the communication is done with the base station using long range (LoRa) module. The data collected from each sensor is processed using a robust and steady algorithm to decide the health and environmental conditions of the soldier. The health status is further classified into healthy, wounded or dead. All the collected data along with the obtained results about the soldier's health condition are then encrypted and transmitted to the base station securely. The system uses only 3.2 Wh energy making it energy efficient to extend the operational time. The information can be transmitted up to 700 metres at 2400 baud rate and could be increased further by reducing the baud rate.

Wireless Body Area Sensor Networks for Wearable Health Monitoring: Technology Trends and Future Research Opportunities

Wireless Body Area Sensor Networks for Wearable Health Monitoring: Technology Trends and Future Research Opportunities