Long-term Monitoring System Research Articles

Machine-learning-driven automatic application of the stochastic subspace identification method

Vibration-based operational modal analysis (OMA) methods have been proven effective in identifying dynamic properties of existing structures and infrastructures under operational conditions. Nevertheless, the provision and installation of continuous monitoring systems for long-term structural health monitoring (SHM) purposes potentially applicable to the entire infrastructure networks or to the regional scale of existing vulnerable building heritage require significant economic planning efforts. Nowadays research trends are oriented toward developing effective automatic OMA (AOMA) methods for setting up novel and efficient long-term SHM solutions. The current study illustrates a new recent paradigm for the automatic output-only modal identification of linear structures under ambient vibrations called intelligent automatic operational modal analysis (i-AOMA). The proposed approach relies on the covariance-based stochastic subspace identification (SSI-cov) algorithm and effectively integrates a machine learning intelligent core, i.e. a random forest (RF) classifier, in a conceptually two steps procedure, i.e. an explorative phase and an intelligently-driven phase. The i-AOMA procedure provided a new framework that requires a minimum intervention to the user and is potentially able to deliver uncertainty measures of the modal parameters’ estimates based on the explored SSI-cov control parameters. An application on a shear-type RC frame building typical of existing heritage in Italy is herein discussed and reported.

Maintenance-free antifouling polymeric membrane potentiometric sensors based on self-polishing coatings.

Polymeric membrane ion-selective electrodes (ISEs) have been widely used in environmental monitoring. However, in complicated marine environments, marine biofouling usually becomes a sticky problem for these electrodes. Herein, for the first time, a novel maintenance-free antifouling potentiometric marine sensor based on a self-polishing coating (SPC) is proposed. The SPC is synthesized by using the seeded emulsion polymerization method based on the triisopropylsilyl methacrylate monomer as the regulator of the self-renewal rate. This coating can be simply modified onto the electrode surface by drop-casting. The silyl acrylate side groups of the obtained SPC on the sensor surface can be hydrolyzed in the marine alkaline medium. The shear movement of seawater driven by sea waves, wind, gravity, or vibration removes the leftover (fouled) brittle polymer backbone and thus the fouling marine microorganisms. As a proof-of-concept experiment, a polymeric membrane Ca2+-ISE is chosen as a model. Compared to the unmodified electrode, the SPC-coated Ca2+-ISE exhibits remarkable improved antifouling properties in terms of superior anti-adhesive abilities towards marine microorganisms, such as bacterial cells and algae and excellent long-term stability even in the presence of high levels of marine microorganisms. Since no additional manual maintenance is required for maintaining the antifouling abilities of the sensor, the proposed self-polishing sensor may lay an important foundation for construction of unattended long-term potentiometric monitoring systems in real marine environments.

Long-term indirect monitoring system for short and medium span bridges

Bridge health monitoring has been widely discussed for establishing the rational maintenance strategies of existing bridges. Meanwhile, we need to provide discussions and recommendations on how to establish fully automatic data collection system and how to manage the big data for holistic bridge maintenance decisions. The author developed a vehicle-based (coupling of a moving vehicle and a bridge) bridge health monitoring system by using public buses which is one practical solution to the problem for condition assessment of short and medium span bridges (called “bus-based monitoring system”). The proposed system includes some new ideas about making use of the under rear-wheel spring acceleration sensors installed on an in-service fixed-route bus operating on the public transit system (long-term and indirect methods), define “characteristic deflection” as an indicator that may be useful in efficiently detecting a structural anomaly of a bridge, etc. The main objectives of this paper are 1) to introduce the details of how to assess the bridge condition based on the results of long-term application (total more than 10 years) to actual bridges on Ube City bus network in Japan, as a specific example for verify the system, 2) to discuss what is possible steps in realizing practical application of the system to existing bridges for consideration systematically into the maintenance strategies in the future. This paper is a continuation of previously published ones with new materials from the practical application viewpoint.

Optimal sensor placement for bridge structural health monitoring: Integration of physics-based models with data-driven approaches

Optimization of sensor configurations for cost-efficient structural health monitoring (SHM) plays a pivotal role in ensuring the safety and longevity of critical infrastructures such as bridges while minimizing maintenance expenses. Infrastructure systems may evolve over time, and their monitoring needs may change. Hence, the best sensor configuration must be designed to be scalable and adaptable to future requirements without major overhauls.The present work explores this optimization problem as an essential step of the development and implementation of a robust digital twinning strategy, by combining the strengths of physics-based models with data-driven insights. Conventional methods for sensor placement typically rely on expert knowledge, oftentimes resulting in suboptimal configurations and excessive installation costs. Conversely, physics-based models provide a rigorous comprehension of the structural behavior and can inform sensor placement procedures by assessing the impact of sensor locations on the monitoring accuracy. However, these models have limitations, such as simplifying assumptions, uncertainties, and computational complexities. To overcome these issues and upgrade the sensor placement process, data-driven approaches can be integrated to extract patterns, correlations, and unforeseen anomalies.To demonstrate the effectiveness and benefits of this hybrid framework, a case study of an existing bridge instrumented with sensors is presented. First, a simplified physics-based model is developed and calibrated through real-world data obtained from an extensive dynamic identification campaign comprising 108 instrumented degrees of freedom. Then, a reduced number of sensors are selected through a data-driven optimization strategy as best candidates for the deployment of a long-term monitoring system. Finally, the virtual model is employed to simulate varying damage scenarios and validate whether the sensor location experimentally identified as optimal would remain the best even when structural conditions change. The ultimate goal is to foster proactive maintenance strategies by striking a balance between data quality, sensor coverage, and SHM cost.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy

BackgroundRecent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PurposeThe study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. Materials and methodsAdults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. ResultsTwelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. ConclusionsEEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Comparative experiment on the efficiency of water cooling in photovoltaic modules in the climatic conditions of Southern Russia

The problems of ensuring the safety of operation of nuclear power plants are always paid increased attention. In addition to the self-contained diesel generator sets used to maintain the operation of safety systems in case of loss of external power supply, it is also advisable to consider the use of more environmentally friendly self-contained photovoltaic units at this stage. The work is aimed at a comparative experimental study of the efficiency of water cooling in real natural climatic conditions of Southern Russia. In this experiment, cooled and uncooled photovoltaic modules are simultaneously exposed to a complex of variable weather factors: solar radiation, cloudiness, wind, pressure, temperature and humidity of the environment. Both modules have loads connected via MPPT controllers. The effect of water cooling on the energy efficiency of photovoltaic modules assembled from silicon heterojunction technology (HJT) solar cells was studied. The solar panels were made from 130 micron thick HJT cells interconnected using SmartWire contact technology. It reduces power loss due to possible defects such as cracks. The conditions for ensuring the highest degree of similarity between the parameters of the cooled and uncooled modules have been met. A comparative experimental study was conducted in Astrakhan State University using a long-term monitoring system for the characteristics of photovoltaic modules. This is a test photovoltaic system (TPS), built on the basis of the Paragraph PL2 electronic recorder. A significant increase in module output when working with cooling was established. At insolation of 987.5 W/m2, the power generated by the cooled module was 93.0297 W, while the power of the module without cooling was 79.306 W. The difference comprised 13.7237 watts. Power increased by 17%. In the experiment, the average efficiency value when the module was cooled was 0.15977. When uncooled, it was 0.13764. The efficiency intensified by 2.21%. This increase is significant. The results obtained confirm the fairly high efficiency of water cooling in photovoltaic modules in real natural operating conditions for regions with high ambient temperatures, Southern Russia, in particular

A Low-Power Data Logger With Simple File System for Long-Term Environmental Monitoring in Remote Areas

A Low-Power Data Logger With Simple File System for Long-Term Environmental Monitoring in Remote Areas

DEEP LEARNING FRAMEWORK FOR HUMAN ACTIVITY DETECTION BASED ON WIRELESS SENSORS.

Recognizing human athletic behaviors has grown in importance as a component of analysis during the last few years. For upcoming training, FPGA-based recognition has been built to perfectly predict human actions. Intelligent environments where human athletic movements may be automatically recognized are required to ensure the viability of such long-term athletic training monitoring systems. The system discussed here employs an Artificial Neural Network (ANN) based Field-Programmable Gate Array (FPGA). In order to infer human behavior from the data collected by a smart environment, machine learning algorithms must first be trained on annotated datasets. To effectively extract characteristics from unprocessed data and develop a machine learning model for anticipating an individual's movement, conventional signal processing techniques and domain knowledge are required. The purpose of this work is to show how a hybrid deep learning model may be applied to recognize human behavior. Convolutional neural networks and continuous neural networks, for example, are deep learning techniques that will extract the features and help with categorization. The proposed model forecasts human activity using wireless sensor data mining datasets. Accuracy, training loss, testing loss, the confusion matrix, and other metrics have all been used to evaluate the model's performance.

Balanced Definition of Thresholds for Mode Tracking in a Long-Term Seismic Monitoring System

The catastrophic events of recent years have strengthened the awareness of the fragility of the built heritage and the importance of careful and targeted maintenance. This, in combination with the development of modern techniques for the analysis of large datasets, has favoured the diffusion of long-term seismic monitoring systems for the protection of structures. In the field of structural health monitoring, data-driven techniques allow crucial information to be extracted from measurements without the need to model the physical phenomena involved, circumventing potential limitations that may arise. On the other hand, however, the results of data-driven approaches are based entirely on the measured structural response; this is why a high reliability of the procedure for extracting diagnostic parameters is essential. In this perspective, a Mode Tracking procedure is proposed to obtain coherent time histories of the modal frequencies of a structure as environmental conditions vary. The procedure is applied to the Sanctuary of Vicoforte, an important monumental structure located in Piedmont, known for its imposing oval dome and characterized by a permanent structural monitoring system. This study aims to disentangle the frequency time series and obtain a rigorous database on which to set up damage identification processes.

Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place.

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.

RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINE

Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.

Noncontact Monitoring of Heart Rate Variability Using a Fiber Optic Sensor

Heart rate variability (HRV) is widely investigated to provide early warning signs for cardiovascular diseases (CVDs). However, traditional HRV monitoring methods are inconvenient in daily long-term continuous monitoring due to the uncomfortable direct contact of the devices with the skin. In this work, we present a ballistocardiogram (BCG)-based system for long-term HRV monitoring in a non-contact mode. BCG signals can reflect the mechanical activity of the heart, from which the beat-to-beat interval (BBI) can be extracted for HRV analysis. An effective solution is demonstrated for accurate and steady BCG signal detection using a highly-sensitive fiber optic sensor. A robust heartbeat extraction algorithm based on the deep learning method is proposed to improve the accuracy of the BBI extraction. Continuous wavelet transform (CWT) is introduced to facilitate feature extraction against the variation of morphological characteristics of BCG signal. The accuracy of BBI estimation presents a median error of 4.4 ms, indicating the effectiveness and feasibility of the proposed system. This work is expected to pave a new and practical pathway for household HRV monitoring during sleep.

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array.

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

Solar, fuel, and battery cell-based small-scale hybrid power systems for long-term environmental monitoring using wireless sensors

Solar, fuel, and battery cell-based small-scale hybrid power systems for long-term environmental monitoring using wireless sensors

Sustainable Soundscape Monitoring of Modified Psycho-Acoustic Annoyance Model with Edge Computing for 5G IoT Systems

Next-generation IoT systems will allow sustainable performance in long-term monitoring systems. This sustainability concept applies to soundscape description, as it allows monitoring in urban environments. In this work, the implementation of psycho-acoustic annoyance models in a 5G-enabled IoT system is proposed, applying two edge-computing approaches. A modified Zwicker’s model is adopted in this research, introducing a term that takes into account the tonal component of the captured sound. These implementations have been validated in a measurement campaign where several IoT devices have been deployed to evaluate different sound environments of a university campus. Then, the analysis of the sound-quality metrics is conducted in a different location, showing that if tonality is present in a noisy environment, it results in greater subjective annoyance. Moreover, the Just-Noticeable Difference of these results is derived from Zwicker’s psycho-acoustic annoyance to establish a limitation for this metric.

Comparison of the corrugated steel web composite box-girder and traditional girders regarding temperature field under solar radiation

The corrugated web composite box-girder (CWCB) is a novel structural form and has been widely adopted in bridge engineering due to its excellent mechanical properties and construction convenience. However, researches on the temperature field in CWCB is very limited, which may augment the risk of concrete cracking and steel web bucking and deteriorate the durability of bridge structures. This paper thereby experimentally investigates the time-varying and spatial temperature distribution features of CWCB based on a long-term temperature monitoring system. The experimental results indicate that the temperature field in CWCB is non-uniformly distributed and the vertical and horizontal temperature differences can reach up to 13.4 ℃ and 19.1 ℃, respectively. Meanwhile, the observed temperature distribution profile of the experimental CWCB is varying with seasons owing to the different exposure to solar radiation. Then, a refined numerical thermal simulation approach is developed and its reliability and accuracy have been validated by the experimental temperature data of three types of box-girders, i.e., CWCB, ordinary composite box-girder (OCB), and conventional concrete box-girder (CCB). Based on the validated numerical approach, the temperature fields of three full-sized box-girders are therewith compared and the results demonstrate that the temperature field in CWCB is much more complicated than the traditional box-girders. Besides, the obtained cross-sectional temperature differences of CWCB are significantly larger than that of the traditional box-girders. Finally, two temperature profiles have been proposed to characterize the thermal actions in CWCB, which are composed of parabolas and polylines. The outcome of this study can provide a useful tool for analyzing the temperature field and calculating the design thermal load of CWCB.

An improved system for long-term monitoring of full-bridge traffic load distribution on long-span bridges

Vision-based traffic load monitoring has attracted considerable attention in recent years due to its promise to enable continuous identification of full-bridge traffic load (FBTL). However, previous FBTL monitoring approaches have limits in small object detection and occluded object tracking, and have never been implemented in the whole range of long-span bridges. This paper proposes a long-term oriented FBTL monitoring system by introducing three improvements in vehicle detection, tracking and position correction to the existing framework that is based on the information fusion of weigh-in-motion (WIM) systems and multiple cameras. In the new system, vehicle detection is achieved by a YOLO-v4-based model, while vehicle tracking is realised by a dual-metric strategy combining vehicle motion and its appearance features. In addition, an efficient correction method for transverse vehicle position is derived from projective geometry, enabling the system to process a large number of vehicles at the same time. As the first complete implementation of FBTL monitoring on long-span bridges, the proposed system has been applied and verified on a cable-stayed bridge. The results suggest the proposed system is expected to provide long-term FBTL distribution, accompanied by faster calculation speed, higher small object detection accuracy, and better capacity to cope with the occlusion problem.

Toward a Smart Sensing System to Monitor Small Animal's Physical State via Multi-Frequency Resonator Array.

This article presents a highly scalable and rack-mountable wireless sensing system for long-term monitoring (i.e., sense and estimate) of small animal/s' physical state (SAPS), such as changes in location and posture within standard cages. The conventional tracking systems may lack one or more features such as scalability, cost efficiency, rack-mount ability, and light condition insensitivity to work 24/7 on a large scale. The proposed sensing mechanism relies on relative changes of multiple resonance frequencies due to the animal's presence over the sensor unit. The sensor unit can track SAPS changes based on changes in electrical properties in the sensors near fields, appearing in the resonance frequencies, i.e., an Electromagnetic (EM) Signature, within the 200 MHz-300 MHz frequency range. The sensing unit is located underneath a standard mouse cage and consists of thin layers of a reading coil and six resonators tuned at six distinct frequencies. ANSYS HFSS software is used to model and optimize the proposed sensor unit and calculate the Specific Absorption Rate (SAR) obtained under 0.05 W/kg. Multiple prototypes have been implemented to test, validate, and characterize the performance of the design by conducting in vitro and in vivo experiments on Mice. The in-vitro test results have shown a 15 mm spatial resolution in detecting the mouse's location over the sensor array having maximum frequency shifts of 832 kHz and posture detection with under 30° resolution. The in-vivo experiment on mouse displacement resulted in frequency shifts of up to 790 kHz, indicating the SAPS's capability to detect the Mice's physical state.

Statistical Analyses of the Non-Uniform Longitudinal Temperature Distribution in Steel Box Girder Bridge

The frequently conventional assumption that bridge temperature is uniformly distributed on long-span bridges could lead to uncertainty when analyzing temperature effects. This study investigated the surface temperature of steel box girders on a long-span suspension bridge, emphasizing the distribution characteristics in the longitudinal (spanwise) direction. The girder surface temperature distribution was monitored using the long-term structural health monitoring system (SHMS). First, the probability density functions (PDF) of the girder surface temperature were analyzed. The results showed that the PDFs had bimodal characteristics and could be well-fitted using the weighted superposition of two normal distributions. Meanwhile, there was an obvious difference between the PDFs of the measuring points at different longitudinal sections of the bridge, which is inconsistent with the assumption that the temperature was uniformly distributed in the longitudinal direction. Subsequently, the longitudinal distributions of the girder surface temperature were statistically analyzed, and polynomial functions were introduced to fit the distribution curves along the left and right sides of the mid-span. A correlation analysis was then performed, highlighting the variability in temperature in the longitudinal direction. Additionally, the longitudinal temperature distribution pattern could be summarized as (i) the highest in the mid-span, the lowest in the tower, and increasing along the side span; (ii) there were also significant differences between the left and right sides of the mid-span. Finally, the time- and space- distributions of the temperature were studied, and a contour map was displayed. The results showed that the girder surface temperature had significant three-dimensional spatial characteristics and was not only non-uniformly distributed in space but also in time. This work is useful for a more accurate analysis of temperature effects on long-span bridges.

Border in Belovezha forest: what will happen to biodiversity and natural ecosystems?

The authors assessed the impact of the barrier structure on the border of Poland and Belarus on the biodiversity of the UNESCO World Heritage Site "Belovezhskaya Pushcha", predicted the degree of impact on the natural complex by species and sources, compiled a potential system for long-term monitoring of the state of natural ecosystems, and drawn up a plan of urgent measures to minimize the negative impact.