Health Monitoring Network Research Articles

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.

Insight into excitation and acquisition mechanism and mode control of Lamb waves with piezopolymer coating-based array transducers: Analytical and experimental analysis

In-situ fabricated piezopolymer coating-based transducers have been developed to build large-area, lightweight and flexible networks for wave-based structural health monitoring (SHM). Meanwhile, their tunability can be realized by array electrodes for mode control with proper tuning methods. However, conventional standard tuning by phase matching seems not always effective. In this study, the excitation and acquisition mechanism of Piezopolymer Coating-based Array Transducers (PCATs) were first studied for Lamb waves. Distinctly different coupling mechanisms of PCAT actuators and sensors were discussed by analytical models and experimental verification, respectively. Then comprehensive parameter studies were performed to understand the filtering effect with finite temporal pulse duration of PCAT actuators, and finite spatial electrode span of PCAT actuators and sensors. Corresponding bias tuning methods were proposed with analytical solutions to improve mode control in Lamb-wave excitation and acquisition. This new guideline of designing array electrodes for PCAT actuators and sensors has been proven effective by successfully tuning the poor mode-controlled wavefield originated by the standard tuning method. Such tunability has great potential to be applied for detecting various damages in Lamb wave-based SHM, where sensitivity, accuracy, and signal interpretation can be improved with good control of particular frequency-mode selections.

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.

Deep subdomain generalisation network for health monitoring of high-speed train brake pads

Uneven wear at different locations in high-speed train brake pads creates inconsistent vibrations, which make the criteria learned by the intelligent model hard to standardise to determine friction block failure. Variable friction-induced vibration makes brake pad health monitoring a cross-domain diagnostic problem. This study proposes a deep subdomain generalisation network for online monitoring of the health status of train brake pads. In contrast to existing transfer learning methods, the proposed approach does not assume the availability of test data during training. The model is generalised using unsupervised learning to new scenarios with unknown specific locations of uneven wear. The network reduces the discrepancy explicitly among the distributions of relevant source subdomains using local maximum mean discrepancy, which helps subdomains with identical labels more accurately find an appropriate common subspace. Furthermore, Bayesian optimisation is used to unify feature extraction, domain generalisation and hyper-parameter optimisation into a framework. The vibration–acceleration signals of different friction blocks are collected, and generalisation experiments are performed on both braking-friction and rotating-machinery datasets. The results indicate that the proposed model outperforms the other domain generalisation approaches and accurately identifies the state of a target friction block.

Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.

With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient’s body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.

Urban safety network for long-term structural health monitoring of buildings using convolutional neural network

Structural response recovery and prediction technologies are required for long-term structural health monitoring (SHM). In this study, a structural response prediction method was proposed to evaluate the long-term safety of structures in which SHM system failure occurred by constructing a safety network of buildings in a given region. In this method, the correlation is defined by machine learning between the dynamic structural response of nearby buildings that are under the same load effects. Furthermore, a convolutional neural network was introduced in which the structural response correlation between two nearby buildings in a region is defined as a safety network. The safety network can be individually applied to multiple buildings, resulting in multiple networks that can be expanded to construct an urban safety network. To examine the applicability of the proposed technique, a safety network was constructed between two nearby buildings to predict the structural response of the target building.

Design and Implementation of a Quantitative NetworkHealth Monitoring and Recovery System.

A network health monitoring system focuses on the quantification of the network’s health by taking into account various security flaws, leaks, and vulnerabilities. A plethora of propriety tools and patents are available for network health quantification. However, there is a paucity of available research and literature in this field. Thus, in this study, we present an architectural design of a network health monitoring system. The design focuses on the quantification of the network health of each end-user as well as the entire network. The network health score for each end-user is quantified by identifying (1) illicit egress-ingress traffic, (2) anomalous fingerprints, and (3) system-network vulnerabilities based on the NVD-CVSS (National Vulnerability Database, Common Vulnerability Severity Score) standards. An overall network-health score is produced, along with a prevention and recovery mechanism that is triggered upon the detection of an anomaly. The proposed system is implemented in a local area network and has demonstrated to protect the network against various threats successfully. The study is concluded by comparing the proposed tool with the popular propriety tools available in the field. The results outline that the proposed system garners features of open-source tools and enriches them by introducing a state-of-the-art architecture coupled with multiple novel features like exhaustive identification of vulnerability and detection of network aberrations using timers.

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.

Sensor integration and data explotation of Structural Health Monitoring Network integrated on a Unmananned Aerial Vehicle (UAV)

Unmanned Aerial Vehicles (UAVs) are flying robots that require little or no human control while flying. The number of UAV has growth exponentially due to rise in applications such as video taking, surveillance and delivering cargo to customers. Despite the promise of the technology, UAV are not very reliable currently, either civil or military. They lack of sensing mechanisms to detect issues in operation, such impacts, hard landings and overload, together with the difficulties to detect structural problems during the UAV operation could compromise not only the safety of the UAV, also all along the entire flying area. This paper presents the design, development implementation, and validation of a Structural Health Monitoring (SHM) system applied to the rear fuselage of the UAV developed by INTA for R&D activities (MILANO). The rear fuselage, a 2.5 meters carbon/epoxy structure with frames and stringers, was instrumented with fiber optic sensors and PZT. Two different types of fiber optic sensors were considered: Fiber Bragg Gratings and distributed fiber sensing based on Rayleigh backscatter. The objective of the fiber optic sensor network is to detect the changes induced on the strain field due to damage appearance. This information will be employed to train a neural network which provides the self-diagnosis of the structural integrity. Additionally, the PZT network will be used as active system for structure interrogation through elastic waves.

A Review Approach to Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems

A Review Approach to Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems

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.

Femtosecond laser point-by-point inscription of an ultra-weak fiber Bragg grating array for distributed high-temperature sensing.

Ultra-weak fiber Bragg grating (UWFBG) arrays are key elements for constructing large-scale quasi-distributed sensing networks for structural health monitoring. Conventional methods for creating UWFBG arrays are based on in-line UV exposure during fiber drawing. However, the UV-induced UWFBG arrays cannot withstand a high temperature above 450 °C. Here, we report for the first time, to the best of our knowledge, a new method for fabricating high-temperature-resistant UWFBG arrays by using a femtosecond laser point-by-point (PbP) technology. UWFBGs with a low peak reflectivity of ∼ - 45 dB (corresponding to ∼ 0.0032%) were successfully fabricated in a conventional single-mode fiber (SMF) by femtosecond laser PbP inscription through fiber coating. Moreover, the influences of grating length, laser pulse energy, and grating order on the UWFBGs were studied, and a grating length of 1 mm, a pulse energy of 29.2 nJ, and a grating order of 120 were used for fabricating the UWFBGs. And then, a long-term high-temperature annealing was carried out, and the results show that the UWFBGs can withstand a high temperature of 1000 °C and have an excellent thermal repeatability with a sensitivity of 18.2 pm/°C at 1000 °C. A UWFBG array consisting of 200 identical UWFBGs was successfully fabricated along a 2 m-long conventional SMF with an interval of 10 mm, and interrogated with an optical frequency domain reflectometer (OFDR). Distributed high-temperature sensing up to 1000 °C was demonstrated by using the fabricated UWFBG array and OFDR demodulation. As such, the proposed femtosecond laser-inscribed UWFBG array is promising for distributed high-temperature sensing in hash environments, such as aerospace vehicles, nuclear plants, and smelting furnaces.

MIMO Operations in Molecular Communications: Theory, Prototypes, and Open Challenges

The Internet of Bio-nano Things is a significant development for next generation communication technologies. Because conventional wireless communication technologies face challenges in realizing new applications (e.g., in-body area networks for health monitoring) and necessitate the substitution of information carriers, researchers have shifted their interest to molecular communications (MC). Although remarkable progress has been made in this field over the last decade, advances have been far from acceptable for the achievement of its application objectives. A crucial problem of MC is the low data rate and high error rate inherent in particle dynamics specifications, in contrast to wave-based conventional communications. Therefore, it is important to investigate the resources by which MC can obtain additional information paths and provide strategies to exploit these resources. This study aims to examine techniques involving resource aggregation and exploitation to provide prospective directions for future progress in MC. In particular, we focus on state-of-the-art studies on multiple-input multiple-output (MIMO) systems. We discuss the possible advantages of applying MIMO to various MC system models. Furthermore, we survey various studies that aimed to achieve MIMO gains for the respective models, from theoretical background to prototypes. Finally, we conclude this study by summarizing the challenges that need to be addressed.

A self-tunable wind energy harvester utilising a piezoelectric cantilever beam with bluff body under transverse galloping for field deployment

Natural vibrations can be harnessed using piezoelectric transducers and converted into electrical energy to power small electronic devices such as sensors network for structural health monitoring. One promising wind energy harvester configuration is a piezoelectric cantilever beam with a bluff body attached at the free end. The strain-induced into the cantilever by the galloping motion, and therefore the power produced is dependent on the bluff body shape, wind speed and the incidence angle of the wind to the bluff body face. Thus far, most studies related to piezoelectric cantilever beam with transverse galloping bluff body were conducted in wind tunnels with laminar flow and graduated wind speed increase. There is no research conducted on natural and chaotic wind conditions. The effectiveness of the bluff body in varying wind conditions is not known. In this study, a self-tunable piezoelectric wind energy harvester was proposed by using a rotating base to account for the erratic nature of wind in the field. Three bluff body prisms (square, triangle and D-section), fitted to a piezoelectric cantilever with fixed and rotating bases, were sequentially studied under the natural wind condition. Results indicated that the rotating base configurations have a higher cut-in speed when compared with the motionless base but was able to produce higher output power, especially with the use of a square prism as the bluff body under typical urban windspeed (i.e. 3–6 m/s). This first-ever field study demonstrated the potential and advantage of employing the piezoelectric wind energy harvester with a rotating base under natural and erratic wind conditions.

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

High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring.

In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases.

Blockchain technology and smart contract for civil structural health monitoring system

AbstractThe rapid development of blockchain technology is exerting a profound impact on all walks of life. Blockchain, which is a decentralized and distributed technology architecture, has inspired people to think about the social development model. However, blockchain technology has not been applied to health monitoring. Therefore, this study proposes a distributed health monitoring system based on blockchain technology, which realizes data security, information transparency, efficient sharing, and independent decision making with smart contracts. First, the system is designed as a blockchain network for structural health monitoring (SHM), and a series of smart contracts for health monitoring is developed to achieve monitoring authority verification, structural damage identification, and other functions. Based on the strength of the Ethereum private chain and Guangzhou New TV Tower benchmark model, smart contracts for health monitoring were deployed, and a series of tests of the proposed system were performed. The results indicate that the proposed system can provide monitoring authority verification, generate abnormal alerts, immutability of data, attack resistance, and traceability query.

Design and Implementation of Routing Algorithm to Enhance Network Lifetime in Wireless Body Area Network for Health Monitoring

Design and Implementation of Routing Algorithm to Enhance Network Lifetime in Wireless Body Area Network for Health Monitoring