Static Monitoring Research Articles

Indoor tests of sensor-enabled piezoelectric geocable–geogrid composite structure for slope rehabilitation and monitoring

Sensor-enabled piezoelectric geocables were combined with a geogrid to acquire a sensor-enabled piezoelectric geogrid (SPGG) based on the impedance–strain relationship. Tension, pullout, and straight shear tests were conducted on this SPGG configuration. The tension test results indicated that the tensile strain–normalized impedance curves were exponential in form within the first 7 % of strain and the rate of shift in impedance was independent of the tension loading rate. An excellent correspondence between the peak strength and impedance inflection point was observed in the results of the pullout and straight shear tests. Additional validation of the proposed SPGG was conducted through a collapse test of the reinforced soil slope model. The results indicated that the SPGG-obtained strains were similar to actual strain gauge measurements but provided a larger measurement range and that the SPGG was able to sense real-time vibrations during the slope collapse using a voltage analysis, confirming that the proposed SPGG can simultaneously provide soil reinforcement, strain monitoring of reinforcement materials, and vibration sensing. This research is expected to inform the development of a dynamic and static monitoring, large range, and accurate method for monitoring the conditions of reinforced soil over their entire lifecycles.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Instantaneous Tiltmeter Triggered by Dynamic Wetting Behavior.

A novel instantaneous tiltmeter with dynamic and static monitoring functions is reported that is based on liquid metal dynamic wetting behavior in a bio-fabricated anisotropic microchannel. The proposed system achieves instantaneous tiltmeter functionality, offering a broad detection range (-90°-90°) with high precision (0.05°), a rapid reaction time (0.11 s), and enhanced durability. Moreover, a seamless integration has enabled water wave detection, language programming, and human limb monitoring. Especially, the integration of tiltmeter and a t3D motion platform results in a surface structure scanning system capable of effectively performing large area (>200 cm2) and height difference scanning functions. This innovative approach holds great potential for transformative changes in the fields of advanced manufacturing, flexible robotics, and the flexible sensing, further facilitating widespread adoption.

Study on characteristics, efficiency, and variations of water flooding in different stages for low permeability oil sandstone

AbstractWater flooding is an important way to improve recovery in low‐permeability sandstone oil reservoirs. How to decouple the water flooding process using dynamic and static information is a hot topic. In this paper, taking the Paleocene low‐permeability oil sandstone, BY area, eastern Nanxiang Basin as an example, the microscopic water flooding process in the low‐permeability sandstone matrix was systematically investigated, and the characteristics of water channeling under the conditions of fracture existence were analyzed using the dynamic and static monitoring data. The results show that the target layer mainly develops frequently thin stacked composite sand bodies. Under the combined influence of matrix and fracture seepage, the low‐permeability sandstone developed by water flooding shows that there is a single direction of efficiency. The direction of advantageous water advancement is 45° north–east, and the speed of water flooding advancement is 2.57 m/day. Microscopic water‐drive oil experiments show that bound water is mainly distributed in the original low‐permeability sandstone as a membrane in the pore wall and as short rods in the throat. Differences in pore structure and petrophysical properties affect the residual oil content and degree of oil recovery. For sandstones with good petrophysical properties, mild water flooding can improve crude oil recovery. The increase in oil production is mainly concentrated in the early stage of water flooding development, and the increase in oil recovery degree is not significant with the increase in injection multiples in the middle and late stages. However, for sandstones with relatively poor petrophysical properties, water flooding is more effective in the early and late stages than in the middle stages. Therefore, it is necessary to adjust the water flooding measures according to the differences in the petrophysical properties of the sand body. Local tectonics and natural fracture strikes are important factors affecting the direction of the expansion of water flooding fractures. Overall, the prevention of water channeling in low‐permeability sandstones has to take into account the complex coupling between water flooding fractures, natural fractures, and hydraulic fractures.

Comparison of Neck Pain and Posture with Spine Angle Tracking System between Static and Dynamic Computer Monitor Use

This study investigates the effect of dynamic changes in monitor height and tilt on neck pain and posture of computer users. Using a wearable device, we aim to compare neck pain and spine angle between static and dynamic monitors. A spine angle tracking system using the Inertial Measurement Unit (IMU) was proposed, and the accuracy was validated. Eight office workers participated for five hours over two days, and each day used either a static monitor or a dynamic monitor that changed height and tilt every 30 min. The angles of C0, C7, L1, and S1 endplates were estimated using the proposed system. Changes in neck pain and spine angle with time were compared in static and dynamic monitors. The intraclass correlation coefficient confirmed a high concordance between the estimated and actual angles (p < 0.001). Rehabilitation Bioengineering Group (RBG) score increased less in the dynamic monitor compared to the static monitor (p = 0.003). Spinal curvatures are bent in the static monitor compared to the dynamic monitor. The estimated angles aligned well with X-ray measurements. A dynamic monitor that changes height and tilt at regular intervals may reduce neck pain increase and reduce bend forward posture compared to a static monitor.

An AUV-Enabled Dockable Platform for Long-Term Dynamic and Static Monitoring of Marine Pastures

An AUV-Enabled Dockable Platform for Long-Term Dynamic and Static Monitoring of Marine Pastures

Assessment of Particulate and Gaseous Fluoride in Phosphate Fertilizer Industry

Fluorides are emitted in both gaseous and particle forms in the industrial sector. However, studies usually only report total fluoride content. Therefore, the study aimed to assess the particulate, gaseous fluoride and correlate it with the respirable dust particles in Single Super Phosphate (SSP), Granular Single Super Phosphate (GSSP), and administration divisions of the industry. Respirable dust particles, particulate fluoride, and hydrogen fluoride in the work environment were collected on a filter cassette containing an MCE filter paper (0.8 micron 37-mm) and Na2CO3 impregnated backup pad, respectively, using a personal sampler. The fluoride samples were analyzed using Ion Selective Electrode (ISE) and expressed as milligrams per meter cube (mg.m-3). The respirable dust, particulate, and gaseous fluoride content were found to have statistically significant differences (p<0.001) between the divisions (SSP, GSSP, and administration) in the static monitoring, whereas, in the case of personal monitoring, no significant differences were observed. Average airborne respirable, particulate, and gaseous fluoride levels in static monitoring were 1.37, 1.03, 0.20 mg.m-3, 0.018, 0.008, 0.001 mg.m-3, and 0.808, 0.403, 0.026 ppm in SSP, GSSP and administration respectively, whereas in personal monitoring the average respirable, particulate and gaseous fluoride concentrations were 1.18, 0.85, 0.30 mg.m-3, 0.0013, 0.007, 0.002 mg.m-3 and 0.356, 0.258, 0.011 ppm in SSP, GSSP and administration respectively. The present study observed that the levels of fluoride decreased with an increase in distance from SSP, followed by GSSP and administration. It indicates that the fluoride exposure was inversely proportional to the distance of the source. This study outcome will help to design a policy and intervention to mitigate fluoride exposure among workers.

Dynamic Security Assessment Framework for Steel Casting Workshops in Smart Factory

Security assessment (SA) system is crucial to ensure the production safety of a smart factory with rapid development of artificial intelligence. In this article, we propose a novel SA framework. Different from the conventional static monitoring systems based on traditional sensing technologies, the proposed framework can automatically detect objects via visual sensing. We use a skeleton-based graph convolutional network to generate action vocabulary for the intermediate representations of action-to-action cooccurrence relations. These representations are encoded into the sequential interaction models to form the interaction representations. Integrating the states of molten steel levels as the reference labels, the sequential representations are fed into a recurrent neural network model with multilayer gated recurrent units (GRUs) to capture the key interactions leading to the accidents, in which an attention mechanism is used to reweight the actions and eliminate the invalid interactions. The predicted labels and the hidden states of the scenes are passing among multilayer GRUs. Finally, we optimize the global output to dynamically assess the security by calculating a joint objective function with a regularized cross-entropy loss. On the self-collected dataset from our partner Iron and Steel company and on-line video clips, the proposed framework performs better than the existing SA schemes.

Prediction of postoperative complications after hepatectomy with dynamic monitoring of central venous oxygen saturation

BackgroundThe usefulness of static monitoring using central venous pressure has been reported for anesthetic management in hepatectomy. It is unclear whether intra-hepatectomy dynamic monitoring can predict the postoperative course. We aimed to investigate the association between intraoperative dynamic monitoring and post-hepatectomy complications. Furthermore, we propose a novel anesthetic management strategy to reduce postoperative complication.MethodsFrom 2018 to 2021, 93 patients underwent hepatectomy at our hospital. Fifty-three patients who underwent dynamic monitoring during hepatectomy were enrolled. Flo Trac system was used for dynamic monitoring. The baseline central venous oxygen saturation (ScvO2) was defined as the average ScvO2 for 30 min after anesthesia induction. ScvO2 fluctuation (ΔScvO2) was defined as the difference between the baseline and minimum ScvO2. Postoperative complications were evaluated using the comprehensive complication index (CCI).ResultsPatients with ΔScvO2 ≥ 10% had significantly higher CCI scores (0 vs. 20.9: p = 0.043). In univariate analysis, patients with higher CCI scores demonstrated significantly higher preoperative C-reactive protein-to-lymphocyte ratio (7.51 vs. 24.49: p = 0.039), intraoperative bleeding (105 vs. 581 ml: p = 0.008), number of patients with major hepatectomy (4/45 vs. 3/8: p = 0.028), and number of patients with ΔScvO2 ≥ 10% (11/45 vs. 6/8; p = 0.010). Multivariate logistic regression analysis revealed that ΔScvO2 ≥ 10% (odds ratio: 9.53, p = 0.016) was the only independent predictor of elevated CCI.ConclusionsCentral venous oxygen saturation fluctuation during hepatectomy is a predictor of postoperative complications. Anesthetic management based on intraoperative dynamic monitoring and minimizing the change in ScvO2 is a potential strategy for decreasing the risk of post-hepatectomy complications.

Application of SLAM in bolt tightening monitoring process based on intelligent data algorithm analysis

Abstract In this paper, firstly, according to the requirements of static monitoring of bolt tightening quality, the bolt tightening process can be divided into four stages: snug, elastic deformation, yielding and plastic deformation, and the SLAM technology is applied to bolt monitoring modeling and building engineering modeling. Next, the pre-tightening force for the bolt is measured using the waveform expansion method, and the bolt tightening index can be determined. Then, all the collected bolt data are randomly combined into two categories of loosening and tightening to form a training data set, and the improved intelligent data algorithm is used to train and predict them. The results show a linear correlation between tightening degree x |τ ɛ| and preload force change ΔF under 0.31kN and 0.22kN loading gradients, and the linear correlation coefficient of the fitted curve is greater than 0.9215. The results of the data analysis verify the correctness of the theoretical analysis.

Conceptual Foundations of Monitoring Sustainable Development of Socio-Economic Systems

The article considers the issues of monitoring the sustainable development of socio-economic systems, by which we mean the process of continuous control of effective functioning and sustainable development of socio-economic systems, including the collection of data tracking the dynamics of changes in the state of the system and identifying trends in its development. Two types of monitoring of sustainable development of socio-economic systems are identified. In the static monitoring model, a comparative analysis of socio-economic systems, positioning of the system under study and determining the degree of its sustainability in comparison with others is carried out. The dynamic monitoring model analyzes the trends in the sustainable development of socio-economic systems based on the information for different periods. The proposed methodology combining elements of correlation analysis and elasticity theory allows advancing in the issue of short- and medium-term forecasting of economic crises.

An enhanced outlier processing approach based on the resilient mathematical model compensation in GNSS precise positioning and navigation

The abnormal measurements are widely existent in Global Navigation Satellite System (GNSS) precise positioning and navigation mainly because of the diffraction, reflection, refraction, and even non-line-of-sight reception. However, when multiple outliers exist in GNSS measurements, traditional methods including test procedure or robust estimation usually cannot work well. This study proposed an enhanced outlier processing approach based on the resilient mathematical model compensation. Specifically, first, to avoid excessive deletion, the total number of measurements is considered in the adaptive test procedure with the help of a scale factor. Second, in adaptive robust estimation, the total number of remaining measurements is also considered, thus making it more compatible with the adaptive test procedure. In addition, to overcome the potential inappropriate reweighting operator, different shrinking factors are adopted for code and phase measurements according to their precision, respectively. To verify the effectiveness of the proposed method, one static monitoring experiment and one kinematic vehicle experiment were conducted, where the method without outlier processing, traditional test procedure, traditional robust estimation, and the proposed method were all used. For the static experiment, the ambiguity resolution and positioning solutions of the proposed method perform best. The positioning accuracy of the float and fixed solutions can be improved by approximately 67.4% and 77.6% on average under challenging environments, respectively. For the kinematic experiment, the performance is also the best in terms of positioning availability and accuracy by using the proposed method.

Concurrent Static and Dynamic State Monitoring Using a Global-Local Model for the Fluid Loop in Space Stations

The fluid loop is the core of thermal control system in the space station. Owing to the complex and changeable operating conditions and long time in orbit, the fault rate and performance degradation rate increase. Therefore, it is of practical significance to timely and accurately monitor the operating state of the fluid loop. This article proposed a combined strategy of canonical variate analysis and slow feature analysis to concurrently monitor the static deviation and process dynamics for the operating state. First, multiple sensors are used to measure the data in all condition of the space station, and typical faults are simulated by fault seeding to obtain fault data. Following that, the serially correlated canonical subspaces for each normal operating condition are modeled, and they are further explored to extract slow features to establish the multiple local static monitoring models. Besides, a global model is built using differential slow features in all condition to detect the dynamic anomalies by monitoring the temporal variations. To illustrate the feasibility and efficacy, the proposed monitoring strategy is applied to monitor the test data consisting of normal samples and fault samples.

Turnkey Fibre Bragg Grating interrogator for real-time static and dynamic monitoring applications

Fibre Bragg Grating (FBG) sensors have been widely known for the capability of per -forming static and dynamic measurements. However, the commercially available interrogators have several limitations such as low sampling frequency, lack of complete product that is ready for immediate use and the need of highly skilled FBG experts to interpret the readings. Therefore, this study demonstrated on the improved FBG interrogator known as Universiti Malaysia Pahang -Fibre Bragg Grating Interrogator (UMP-FBGI). Prediction of flexural load on composite beam is carried out for static monitoring. The results revealed that UMP-FBGI possessed good repeatability with the highest percentage of error at 7.51%. For dynamic monitoring evaluation, natural frequency of the composite beam is determined. From the comparison with accelerometer, the highest percentage of error is obtained at 9.82%. All in all, the developed FBG interrogator is a complete product with realtime monitoring graphical user interface (GUI) ready for immediate use.

EaLDL: Element-Aware Lifelong Dictionary Learning for Multimode Process Monitoring.

With the rapid development of modern industry and the increasing prominence of artificial intelligence, data-driven process monitoring methods have gained significant popularity in industrial systems. Traditional static monitoring models struggle to represent the new modes that arise in industrial production processes due to changes in production environments and operating conditions. Retraining these models to address the changes often leads to high computational complexity. To address this issue, we propose a multimode process monitoring method based on element-aware lifelong dictionary learning (EaLDL). This method initially treats dictionary elements as fundamental units and measures the global importance of dictionary elements from the perspective of the multimode global learning process. Subsequently, to ensure that the dictionary can represent new modes without losing the representation capability of historical modes during the updating process, we construct a novel surrogate loss to impose constraints on the update of dictionary elements. This constraint enables the continuous updating of the dictionary learning (DL) method to accommodate new modes without compromising the representation of previous modes. Finally, to evaluate the effectiveness of the proposed method, we perform comprehensive experiments on numerical simulations as well as an industrial process. A comparison is made with several advanced process monitoring methods to assess its performance. Experimental results demonstrate that our proposed method achieves a favorable balance between learning new modes and retaining the memory of historical modes. Moreover, the proposed method exhibits insensitivity to initial points, delivering satisfactory results under various initial conditions.

Spatio-Temporal analysis of sensor based air pollutants in Raipur city, India


 
 
 
 Introduction: Particulate matter pollutants and gaseous pollutants are some of the most hazardous pollutants released into the atmosphere. In order to determine how these pollutants impact the living being at breathing height, the current study measures these pollutants using wireless sensors at a height of 1 m above the ground.
 Materials and methods: In this study, horizontal monitoring of the air pollutants using wireless sensors at twenty-four locations covering four zones including industrial, residential, public-place and transportation of the Raipur city has been evaluated. Spatial variation has been obtained using Inverse Distance Weighting (IDW) method in Geographic Information System (GIS).
 Results: The obtained results indicate that as compared to the monsoon and post-monsoon seasons, the concentrations of air pollutants are highest in the winter. It was observed that Particulate Matters (PM2.5 and PM10) are the main causes of declining air quality, but Nitrogen dioxide (NO2) and Sulfur dioxide (SO2) concentrations were below Central Pollution Control Board (CPCB) guidelines except for NO2 in winter. The Carbon monoxide (CO) concentration has been above the standard limit in all three seasons. The main finding of this study is to evaluate how air contaminants vary in space and time near the ground surface which is not possible through the static monitoring instruments in the study area.
 Conclusion: The primary benefit of the obtained results is their great resolution in a compact area, effectively addressing the air quality issue. The findings suggest that seasonality has a substantial impact on the amount of pollutants in the city. According to the temporal distributions of the air pollutants, monsoon had the best air quality, followed by post-monsoon while the winter season has the highest concentration of pollution.
 
 
 

An elevation stochastic model constrained by C/N0 for GNSS real-time kinematic positioning in harsh environments

When the global navigation satellite system (GNSS) is used for positioning in harsh environments with high occlusion and strong reflection, the signals are easily reflected, diffracted, or even blocked, which can lead to multipath and non-line-of-sight (NLOS) errors. Existing stochastic models do not fully consider the significant unmodeled errors, such as multipath and NLOS, in specific scenarios such as canyons. This paper systematically establishes the carrier-to-noise ratio (C/N0) template functions of the low-cost receiver for the first time and proposes an elevation stochastic model constrained by C/N0 (elevation-C/N0 model) by combining the two indicators (elevation angle and C/N0) with the idea of robust estimation. Then, real-time kinematic positioning (RTK) experiments are conducted to verify the effectiveness of the new model, and both static monitoring and urban kinematic situations are included. The results showed that: (a) in the static data, the average ambiguity fixed rate of the elevation-C/N0 model is 95.79%, which is 10.05%, 15.74%, and 12.57% higher than that of the equal-weight, elevation-based, and C/N0-based stochastic models, respectively. At the same time, only the new model consistently meets centimeter-level accuracy requirements in harsh environments. (b) In the kinematic data, compared with the three traditional stochastic models, the ambiguity fixed rate of the elevation-C/N0 stochastic model is increased by 58.33%, 19.79%, and 28.13%, respectively. The motion trajectory calculated by the elevation-C/N0 model is the smoothest out of all the models. In conclusion, compared with the traditional stochastic models, the elevation-C/N0 stochastic model is more applicable to both static and kinematic measurements in harsh environments. It can effectively mitigate the adverse effects of errors such as multipath and NLOS, increase the ambiguity fixed rate, and improve the positioning reliability to a certain extent, hence the proposed method has a better positioning performance.

BIM-based structural health monitoring and early warning for heritage timber structures

An early warning system in the structural health monitoring (SHM) of an engineering system ensures its survival with protective measures. However, most existing SHM systems have problems in dealing with a large amount of collected data and difficulties in identifying the damaged locations with little or no capability to visualize the problems. Building Information Modelling (BIM) technology has great advantages in information integration, visualization and secondary development, and its application to SHM can help solve these problems. A BIM-based management platform is developed in this paper with multi-dimensional BIM model with multi-source information integration for more relevant and convenient protection of heritage timber structures. A dual-indicator early warning mechanism for long-term static monitoring of structures is proposed. A strategy to backtrack the cause leading to the warning raised is also proposed. The effectiveness of the above system is illustrated with studies on a Chinese heritage timber structure.

Data science and IoT based mobile monitoring framework for hyper-local PM2.5 assessment in urban setting

Ambient air quality shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. In order to ascertain individual exposure, pollutant information is required at high resolution. Current air quality monitoring approaches utilize static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. Such stations allow only for gross assessment of air quality across a city. This work proposes a comprehensive framework for hyperlocal assessment of ambient air quality through a mobile monitoring approach. PM2.5 concentration is considered in this work as a specific air pollutant to demonstrate the framework. In order to test the efficacy of the approach, two case studies were undertaken in the city of Chennai, India as a part of this work. The first study aims at locating diurnal, spatio-temporal PM2.5 hotspots in an urban environment. These hotspots are then associated with daily anthropogenic activity. The second study enables an event centric understanding of spatio-temporal short-lived extremities in PM2.5 concentration. The results are then retrospectively associated with land use profile and anthropological activities. The mobile sensing paradigm provides a unique perspective in the assessment of such localized events. This micro-level assessment offers significant insights on the spatio-temporal variations within a city in a regular case and an extreme, event centric case. Real world applications of hyperlocal air quality assessment, such as least exposure route estimation, are also explored at the end of this work.

Dynamic monitoring frequency for energy-efficient data collection in Internet of Things

With growth of Internet of Things, number of connected sensors increases as well, along with data being collected by those sensors. Most sensors are battery powered and commonly collect data in short and equally spaced time periods resulting with large amount of redundant and often irrelevant data. In this paper, we propose a dynamic monitoring frequency (DMF) algorithm that aims at collecting data only when sensor readings change by more than a predefined value between consecutive readings. Thus, a sensor is turned on only when a change in monitored phenomenon value exceeds a predefined threshold. Two algorithms are analyzed, namely statistical and machine learning. DMF shows notable performance, resulting either with up to ∼70% less missed readings, or it collects up to ∼40% less data compared to the baseline algorithm that collects data with static monitoring frequency.