Health Monitoring Method Research Articles

Fetal ECG extraction using temporal convolutional encoder-decoder network

To extract weak fetal ECG signals from mixed ECG signals recorded from maternal abdominal wall for accurate analysis of fetal heart rate and fetal ECG patterns. By exploiting the superior nonlinear mapping ability of deep convolutional network, we developed a nonlinear adaptive noise cancelling (nonlinear ANC) extraction framework based on a temporal convolutional encoder-decoder network for extracting fetal ECG signals. We first constructed a deep temporal convolutional network (TCED-Net) model for fetal ECG signal extraction, and with the maternal chest ECG signal as the reference signal, the maternal ECG component in the abdominal mixed signal was estimated using this model. The estimated maternal ECG component was subtracted from the mixed abdominal ECG signals to obtain the fetal ECG component. Experimental analyses were performed using synthetic ECG signals (FECGSYNDB) and clinical ECG signals (NIFECGDB, PCDB) to test the performance of the propose method. The results of experiments on the FECGSYNDB dataset showed that the proposed approach achieved good performance in F1-score (98.89%), mean-square-error (MSE; 0.20) and quality signalto-noise ratio (qSNR; 7.84). The F1- score reached 99.1% on the NIFECGDB dataset and 98.61% on the PCDB dataset. The R peak detection accuracy index of the proposed method was higher than the existing best-performing algorithms such as EKF (F1=93.84%), ES-RNN (F1=97.20%) and AECG-DecompNet (F1=95.43%) by 5.05%, 1.9% and 3.18%, respectively. The fetal ECG signals extracted using the proposed method are clearer than those by the existing algorithms, suggesting the potential value this method for effective fetal health monitoring during pregnancy.

Fusion of Laser Scans and Image Data—RGB+D for Structural Health Monitoring of Engineering Structures

A novel method for structural health monitoring (SHM) by using RGB+D data has been recently proposed. RGB+D data are created by fusing image and laser scan data, where the D channel represents the distance, interpolated from laser scanner data. RGB channel represents image data obtained by an image sensor integrated in robotic total station (RTS) telescope, or on top of the telescope i.e., image assisted total station (IATS). Images can also be obtained by conventional cameras, or cameras integrated with RTS (different kind of prototypes). RGB+D image combines the advantages of the two measuring methods. Laser scans are used for distance changes in the line of sight and image data are used for displacements determination in two axes perpendicular to the viewing direction of the camera. Image feature detection and matching algorithms detect and match discrete points within RGB+D images obtained from different epochs. These way 3D coordinates of the points can be easily calculated from RGB+D images. In this study, the implementation of this method was proposed for measuring displacements and monitoring the behavior of structural elements under constant load in field conditions. For the precision analysis of the proposed method, displacements obtained from a numerical model in combination with measurements from a high precision linear variable differential transformer (LVDT) sensor was used as a reference for the analysis of determined displacements from RGB+D images. Based on the achieved results, we calculated that in this study, the precision of the image matching and fusion part of the RGB+D is ±1 mm while using the ORB algorithm. The ORB algorithm was determined as the optimal algorithm for this study, with good computing performance, lowest processing times and the highest number of usable features detected. The calculated achievable precision for determining height displacement while monitoring the behavior of structural element wooden beam under different loads is ±2.7 mm.

Advancing measurement of zero-group-velocity Lamb waves using PVDF-TrFE transducers: first data and application to in situ health monitoring of multilayer bonded structures

Driven by the rapid advancement in manufacturing technologies, engineering structures with complex geometries are increasingly applied in various industries, posing challenges to the applicability and adaptability of existing structural health monitoring methods based on guided ultrasonic waves. To fulfill the characterization of defects in complex structures, a novel approach featuring a conjunction of zero-group-velocity (ZGV) Lamb waves and polarized poly(vinylidenefluoride-co-trifluoroethylene) (PVDF-TrFE) transducers is proposed. In this approach, the PVDF-TrFE solvent is deposited and in situ polarized on the structure surface to form thin and flexible coatings, with which the ZGV waves can be excited efficiently and measured reliably. On this basis, the defect can be characterized by investigating the defect-induced alteration in ZGV wave features. In experimental validations, disbond defects in multilayer bonded structures are evaluated using the ZGV waves measured with fabricated PVDF-TrFE transducers. For the first time, the ZGV waves are measured in a contact and in situ manner. Compared with conventional noncontact measurement of ZGV waves, the proposed approach features a remarkably improved reliability, convenience for narrowband excitation, immunity to measurement uncertainty and capability of in situ monitoring. The proposed approach can advance the ZGV wave-based methods toward the in situ health monitoring and enable the defect evaluation in emerging complex structures.

Non-Contact Detection of Delamination in Composite Laminates Coated with a Mechanoluminescent Sensor Using Convolutional AutoEncoder

Delamination is a typical defect of carbon fiber-reinforced composite laminates. Detecting delamination is very important in the performance of laminated composite structures. Structural Health Monitoring (SHM) methods using the latest sensors have been proposed to detect delamination that occurs during the operation of laminated composite structures. However, most sensors used in SHM methods measure data in the contact form and do not provide visual information about delamination. Research into mechanoluminescent sensors (ML) that can address the limitations of existing sensors has been actively conducted for decades. The ML sensor responds to mechanical deformation and emits light proportional to mechanical stimuli, thanks it can provide visual information about changes in the physical quantity of the entire structure. Many researchers focus on detecting cracks in structures and impact damage with the ML sensor. This paper presents a method of detecting the delamination of composites using ML sensors. A Convolutional AutoEncoder (CAE) was used to automatically extract the delamination positions from light emission images, which offers better performance compared to edge detection methods.

A New Steel-Joint Precast Concrete Frame Structure: The Design, Key Construction Techniques, and Building Energy Efficiency

Assembled methods play a critical role in the construction of precast concrete structures. However, conventional dry-connections-like sleeve grouting joints in precast concrete structures lagged at a low construction and management efficiency with poor quality control. In this study, a novel steel joint for precast reinforced concrete beam-column components is proposed to improve constructability. New joints transform the assembled method from reinforced concrete members into a steel structure by setting a pre-embedded steel connector at both ends of reinforced concrete beams and columns, showing outstanding economic, durability, and fire resistance capabilities. The construction process, construction efficiency, economy, and energy consumption were discussed based on the material, structure, and construction hybrid characteristics. Numerical simulation and structural health monitoring methods are used to monitor and evaluate the deformation and stress state of the proposed system in the whole construction process, so as to optimize the construction scheme and ensure safe and orderly construction. The results reveal that the FEA-simulated values of key building components during construction are in good agreement with the actual monitoring values, which verifies the feasibility of the FEM models and provides a guarantee for construction safety; the construction period of the proposed assemble system is reduced by approximately 56% and 40%, compared with the conventional reinforced concrete frame structure and cast-in-place joints in the precast concrete frame structure, respectively. Meanwhile, the energy consumption of buildings decreases by 20%. This research provides a theoretical basis for the design, calculation, and application of assembled precast structural systems.

Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges.

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset.

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data

A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI’s quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI’s quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI’s quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior.

STRUCTURAL HEALTH MONITORING METHODS OF BRIDGESTRUCTURES: AREVIEWOFEXISTING METHODS WITH RECOMMENDATIONS

Bridges are a crucial part of the transport infrastructure and represent an important asset of a society. One of the crucial parts of that asset goes to monitoring and maintenance since the bridge damage could yield to severe consequences in human lives and material damage. Periodic inspection keeps these structures operational and increases bridge integrity. Different methods have been proposed by different researchers for bridge damage detection based on different theories and methodologies. Various review papers on bridge damage detection are published in the literature in the last few decades. In this paper, a concise review of literature on damage detection in bridge structures based on various methodologies is carried out. The purpose of this paper is to critically examine various methods that have been proposed for bridge damage identification along with some recommendations to improve these methods. The gaps, limitations, and superiority of the existing methods have been identified, and future applications which are important for bridge inspection are covered.

Efficacy and Damage Diagnosis of Reinforced Concrete Columns and Joints Strengthened with FRP Ropes Using Piezoelectric Transducers.

Recent research has indicated that the implantation of a network of piezoelectric transducer patches in element regions of potential damage development, such as the beam-column joint (BCJ) area, substantially increases the efficacy and accuracy of the structural health monitoring (SHM) methods to identify damage level, providing a reliable diagnosis. The use of piezoelectric lead zirconate titanate (PZT) transducers for the examination of the efficiency of an innovative strengthening technique of reinforced concrete (RC) columns and BCJs is presented and commented on. Two real-scale RC BCJ subassemblages were constructed for this investigation. The columns and the joint panel of the second subassemblage were externally strengthened with carbon fiber-reinforced polymer (C-FRP) ropes. To examine the efficiency of this strengthening technique we used the following transducers: (a) PZT sensors on the ropes and the concrete; (b) tSring linear variable displacement transducers (SLVDTs), diagonally installed on the BCJ, to measure the shear deformations of the BCJ panel; (c) Strain gauges on the internal steel bars. From the experimental results, it became apparent that the PZT transducers successfully diagnosed the loading step at which the primary damage occurred in the first BCJ subassemblage and the damage state of the strengthened BCJ during the loading procedure. Further, data acquired from the diagonal SLVDTs and the strain gauges provided insight into the damage state of the two tested specimens at each step of the loading procedure and confirmed the diagnosis provided by the PZT transducers. Furthermore, data acquired by the PZT transducers, SLVDTs and strain gauges proved the effectiveness of the applied strengthening technique with C-FRP ropes externally mounted on the column and the conjunction area of the examined BCJ subassemblages.

A Review of the Synthesis, Performance, and Mechanism of Strontium Based Mechanoluminescence Material

Various types of sensors are used for structural health monitoring systems, some of these systems are strain sensors, vibration sensors, etc. which are based on the resistance of material whose change can be recorded via a microcontroller and plotted to understand the real-time behavior of a structure and the effects of various loads acting on it. But these sensors require expensive equipment and skilled personnel, external power, as well as the acquired data needs to be processed such that some sense can be made out of it. Thus, a new method of Structural Health Monitoring using Mechanoluminescence materials that don't require external power seems quite promising, but being in the initial stage of research and development it comes with its own set of challenges. This study tries to understand the synthesis methods available to synthesize Strontium-based Mechanoluminescence sensors, their working, the embedment processes, and their performance when subjected to loading.

Innovative methods for health monitoring in Europe: results of a cross-sectional study

Abstract Background Innovative solutions are used to monitor the spread of COVID-19, to research and develop vaccines, and to ensure online privacy and security. The aim of the study is to investigate which innovative methods, including algorithms and digital tools (e.g., social media, artificial intelligence, contact tracing applications) are used to monitor health issues related to COVID-19 in Europe, and who is using them. Methods A questionnaire was developed and administered online to European countries’ representatives and stakeholders of the project Population Health Information Research Infrastructure (PHIRI). The survey investigated the use of innovative solutions and digital tools in Europe to monitor COVID-19 and vaccination programs, to research and develop diagnostics and teleconsultations, and to fight online disinformation. Legislative and ethical aspects were also considered. A descriptive data analysis was performed. Results 19 responses were collected from 14 countries. Digital tools are used to monitor COVID-19 (13/14 countries), vaccination programs (12/14), for telemedicine (7/14), and to fight disinformation (10/14). Specific algorithms to detect the patterns of the pandemic spread are available in five countries. The main target groups of the tools are the general population, healthcare providers, patients and epidemiologists. The uptake rate of the tools ranged 5-100% across countries. Measures to evaluate the impact of digital tools (e.g., user surveys, reviews, evaluation teams) have been adopted in seven countries. Information on legislative and ethical aspects related to the use of digital solutions are available in 10 countries. Conclusions The development and use of innovative methods for population health monitoring and research purposes have been the key to mitigate the COVID-19 pandemic. Improving the uptake rate, impact assessment of digital tools and fight against disinformation could enhance countries’ preparedness for future pandemics. Key messages • Given that digital solutions are deployed in population health monitoring, research, and for online privacy and security, they have a key role in mitigating the COVID-19 pandemic. • Enhancement of the uptake rate and assessment of digital tools, and fight against disinformation could strengthen countries’ preparedness for future pandemics.

Nonlinearity detection using new signal analysis methods for global health monitoring

Nonlinearity detection using new signal analysis methods for global health monitoring

WSN-Based SHM Optimisation Algorithm for Civil Engineering Structures

With the development of economy and the improvement of architectural aesthetics, civil structure buildings show a trend of diversification and complexity, which brings great challenges to the Structural Health Monitoring (SHM) of civil structure buildings. In order to optimise the structural health monitoring effect of civil structures, reduce monitoring costs, and improve the ability of civil structures to deal with risks, a civil structure health monitoring method combining Variational Modal Decomposition (VMD) and the Gated Recurrent Unit (GRU) is proposed. The gated neural network algorithm of modal decomposition is used, and then a wireless sensor network (WSN) civil structure health monitoring model is constructed on this basis. Finally, the application effect of the model is tested and analysed. The results show that the network energy consumption of this model can reach a minimum of 0.05 J, which is 0.05 J less than that of the Gate Recurrent Unit (GRU) model. The minimum loss value is 0.08. Its Mean Absolute Error (MAE), Root-Mean-Square Error (RMSE), and Mean Absolute Percent Error (MAPE) are 0.03, 0.04, and 0.06, respectively; the prediction error is the smallest, the overall amplitude difference monitored by the model remains at a low level of less than 0.01, and the changes are closest to the real situation. This shows that the model improves the operation efficiency, improves the accuracy of health monitoring, enhances the adaptability of building structural health monitoring to complex structures, provides a new way for the development of building structural health monitoring technology, and is conducive to enhancing civil structures. The safety and stability of buildings promote the high-quality development of civil and structural buildings.

Evaluation of exhaust air dust polymerase chain reaction as a supplement method for soiled bedding sentinel monitoring in specific pathogen free mouse facility using two different individually ventilated cage racks.

Health monitoring is essential for ensuring animal health and reliable research results. Each animal facility should establish adequate health monitoring methods, and microbiological quality control should be implemented through regular health surveillance. Recently, specific pathogen free (SPF) mice have been housed in individually ventilated cage (IVC) racks in the majority of mouse facilities globally, and health monitoring is implemented using a soiled bedding sentinel (SBS). Even though SBS monitoring is a standard method, it has a limitation in that some pathogens are not sufficiently transmitted to the sentinel housed in the IVC. The exhaust air dust polymerase chain reaction (EAD PCR) method has been reported to be a reliable complementary method to SBS monitoring based on research findings. In Korea, health monitoring programs using EAD PCR have not yet been applied to laboratory animal facilities. The microbiological status of mouse colonies housed in the two IVC racks was compared using SBS and EAD PCR monitoring in our SPF mouse facility. Except for Helicobacter spp. and Staphylococcus aureus, the detection of 16 pathogens did not differ between the two methods. In the detection of Helicobacter spp., EAD PCR was found to be more sensitive than SBS. Helicobacter spp. were not found by SBS, whereas four S. aureus positive samples were detected by either SBS or EAD PCR test. According to our findings, EAD PCR can be used as a supplement to SBS monitoring. Moreover, EAD PCR can reduce the number of animals used, making it a 3R (Replacement, Reduction, Refinement)-consistent method.

Novelty detection approach for the monitoring of structural vibrations using vision-based mean frequency maps

Damage detection is an important part of modern-day engineering. Early damage detection is facilitated by Structural Health Monitoring methods, which may be employed using numerous modalities, such as vibration, guided waves, thermography, or computer vision. These methods produce information that can then be interpreted to detect and localize damage or quantify its extent. Novelty Detection (ND) is a data interpretation approach that enables damage detection without prior knowledge of damage-related influences on gathered data. ND can be conveniently performed using computer vision methods, which allow continuous, non-contact monitoring of large structures with the possibility of relying on such quantifiers as deflection, vibration, or strains.In this work, we present an ND method for monitoring the structural changes in rotary machinery equipment using vision-based data. The proposed technique detects changes in the characteristic frequencies of vibrations due to damage. Using the optical flow calculated for the videos acquired using a high-speed camera, the maps of mean frequency can be estimated and used for evaluating the differences between the reference data set and the data obtained during the monitoring. The Optimal Baseline Selection is used to compensate for the varying operational conditions under which the structure is monitored. The approach was tested on the air compressor working under variable pressure, and the damage introduced to the structure was successfully detected.

Acoustic emission monitoring of wood materials and timber structures: A critical review

• Different NDE methods applied to wood and timber structures were explained. • Concept of AE and its experimentation was introduced. • Impact of wood characteristics on AE wave (velocity, attenuation) was discussed. • Applications of AE in monitoring of wood materials were critically reviewed. • Opportunities, challenges, and outline of future research were discussed. The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied.

Smartphone based structural health monitoring using deep neural networks

In order to create a low-cost and easy-to-implement structural health monitoring method, this paper uses smartphones to collect data on the movement of a building to determine whether it has suffered any damage. Smartphones have easy handling, sensors embedded, and data processing properties. The smartphone-based structural health monitoring system have many advantage over the classical methods. Recurrent fuzzy neural network of long- and short-term memory is applied to determine the damage of the structure. Comparisons with classical neural networks and fuzzy systems are given to show the proposed deep neural networks have better performances. Finally, several experiments are applied to evaluate the proposed methods for the determination of structural damage. • A smartphone-based building structural health monitoring system is developed. • This system uses fuzzy long and short term memory (LSTM) to deal the data obtained from the smartphone. • This system is implemented in a prototype of structural health monitoring.

Analysis of Force Sensing Accuracy by Using SHM Methods on Conventionally Manufactured and Additively Manufactured Small Polymer Parts.

Fabricating complex parts using additive manufacturing is becoming more popular in diverse engineering sectors. Structural Health Monitoring (SHM) methods can be implemented to reduce inspection costs and ensure structural integrity and safety in these parts. In this study, the Surface Response to Excitation (SuRE) method was used to investigate the wave propagation characteristics and load sensing capability in conventionally and additively manufactured ABS parts. For the first set of the test specimens, one conventionally manufactured and three additively manufactured rectangular bar-shaped specimens were prepared. Moreover, four additional parts were also additively manufactured with 30% and 60% infill ratios and 1 mm and 2 mm top surface thicknesses. The external geometry of all parts was the same. Ultrasonic surface waves were generated using three different signals via a piezoelectric actuator bonded to one end of the part. At the other end of each part, a piezoelectric disk was bonded to monitor the response to excitation. It was found that hollow sections inside the 3D printed part slowed down the wave travel. The Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) were implemented for converting the recorded sensory data into time–frequency images. These image datasets were fed into a convolutional neural network for the estimation of the compressive loading when the load was applied at the center of specimens at five different levels (0 N, 50 N, 100 N, 150 N, and 200 N). The results showed that the classification accuracy was improved when the CWT scalograms were used.

Mechanic-Electric-Thermal Directly Coupling Simulation Method of Lamb Wave under Temperature Effect.

Lamb Wave (LW)-based structural health monitoring method is promising, but its main obstacle is damage assessment in varying environments. LW simulation based on piezoelectric transducers (referred to as PZTs) is an efficient and low-cost method. This paper proposes a multiphysics simulation method of LW propagation with the PZTs under temperature effect. The effect of temperature on LW propagation is considered from two aspects. On the one hand, temperature affects the material parameters of the structure, the adhesive layers and the PZTs. On the other hand, it is considered that the thermal stress caused by the inconsistency of thermal expansion coefficients among the structure, the adhesive layers, and the PZTs affect the piezoelectric constant of the PZTs. Based on the COMSOL Multiphysics, the mechanic–electric–thermal directly coupling simulation model under temperature effect is established. The simulation model consists of two steps. In the first step, the thermal-mechanic coupling is carried out to calculate the thermal stress, and the thermal stress effect is introduced into the piezoelectric constant model. In the second step, mechanic–electric coupling is carried out to simulate LW propagation, which considers the piezoelectric effect of the PZTs for the LW excitation and reception. The simulation results at −20 °C to 60 °C are obtained and compared to the experiment. The results show that the A0 and S0 mode of simulation signals match well with the experimental measurements. Additionally, the effect of temperature on LW propagation is consistent between simulation and experiment; that is, the amplitude increases, and the phase velocity decreases with the increment of temperature.