State Monitoring Research Articles

Airborne GNSS reflectometry for coastal monitoring of sea state

Sea level rise and sea state variability, resulting from climate change and global warming, are critical research areas. However, current techniques for observing and monitoring these phenomena have limitations in terms of spatial and temporal resolution, particularly in dynamic coastal zones. GNSS Reflectometry (GNSS-R) is an emerging bistatic radar-based technique that utilizes the GNSS direct (transmitter-receiver) and reflected (transmitter-reflection point-receiver) signals to extract properties of the reflecting surface. This study explores the potential of airborne GNSS-R as a means to monitor sea state in coastal areas by using the Doppler spread and reflectivity as observables. The paper aims to derive a sea state factor from the reflected signal power and the Doppler shift distribution to analyze its correlation with wind speed and significant wave height data obtained from the ERA5 model. The experiment involved four flights conducted along the coast between Calais and Boulogne-sur-Mer, France, in July 2019. A GNSS software receiver processes the direct and reflected signals, tracking and re-tracking the reflected signals with the aid of a specular reflection model. The resulting in-phase and quadrature components are analyzed in the spectral domain every minute to estimate the power, the surface reflectivity, and the relative Doppler shift. The findings reveal that the sea state factor and Doppler spreading are sensitive to sea state conditions, correlated with the ERA5 parameters, and influenced by the elevation angle of GNSS satellites. At low elevations (E<10°), the sea state factor demonstrates an inverse relationship (anti-correlation) with the wind speed and significant wave height, while the Doppler distribution shows a correlation with these parameters. Both correlations decrease with increasing elevation angle. This research underscores the potential of airborne GNSS-R for monitoring sea state variability in coastal areas enhancing our understanding of the relationships between GNSS-R measurements and sea state parameters.

Multifunctional composite flexible films of hydroxypropyl cellulose and silver nanowires

Flexible wearable devices have aroused attentions since the booming flexible electronic technologies, as well as the requirements for improved comfortableness and real-time monitoring for complex motion state of human body. Developing a flexible integrated wearable device with multi functions provides a promising route for next-generation wearable devices. Meanwhile, transparency and biocompatibility are also considered a lot. Herein, hydroxypropyl cellulose/silver nanowires (HPC/AgNWs) flexible composite films were fabricated with a facile two-step method. The fabricated composite films exhibited sufficient transparency and mechanical strength. More importantly, the HPC/AgNWs composite films afforded good motion sensing performances with current sensitivity as high as 350 %. It can accurately identify different bending angles and frequencies, and remained stable for more than 1000 bending cycles. Apart from being employed as motion sensors, the fabricated HPC/AgNWs could be also used for electrical heating and electromagnetic shielding. The films could be heated to more than 60°C at 3.0 V in 57 seconds, and the electromagnetic shielding efficiency reached more than 99.9 %. This study contributed a feasible strategy for the preparation of multifunctional flexible films which are expected to be further applicated in the field of wearable devices.

Digital implementation method and research on thermal life loss of power transformers

AbstractThe report of the 20th National Congress of the Communist Party of China pointed out that it is necessary to accelerate the construction of digital China, and actively develop equipment intelligence, digitalization, and high‐end equipment manufacturing. With the construction of a new round of digital China, the safety of digital technology and high‐end equipment is particularly important for power grid. Transformer is an important link of power grid operation. The destructive thermal failure of transformer has become a hot research issue in the power industry. At the same time, there are more and more power electronic equipment in the power grid, which makes the regulation and control more and more complicated. Taking a 1000 kVA oil‐immersed transformer as an example, the magnetic, current, and thermal multi field coupling numerical analysis method is first used to simulate and analyse the operation status of the transformer. By comparing the simulation results with the monitoring data results, the error of the results is controlled within 5%. Finally, the multi‐state characteristic parameters of the transformer are monitored through sensors, and the numerical simulation analysis results are integrated with the state monitoring results to build a transformer thermal life loss assessment system. The method in this paper can evaluate and analyse the running state of transformer in real time, which is of great significance for the power company to formulate the treatment measures.

Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion

Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.

Hydrodynamic impact as a way of controlling the state of the coal-gas system: analysis and data processing

The research results on hydrodynamic impact during the outburst-hazardous coal seams opening at the Donbas Central District mines are considered. Emphasis is placed on the technological processes complexity related to coal mining and the importance of systematic monitoring of the stress-strain state of rock massif. It is noted that the research and optimization of the hydrodynamic impact process parameters and the coal-gas system state require the use of statistical and probabilistic methods, in particular descriptive statistics. An analysis of the hydrodynamic impact process parameters on the stress-strain state of rock massif in the conditions of steeply sloping seams was carried out using the example of 50 openings. A data array with different characteristics and parameters distribution was obtained. Methods of data distribution normality checking and their use in normality hypothesis verifying for process control are considered. Information on research methods and analysis of parameters related to hydrodynamic impact on the rock massif is summarized. The results of studies of hydrodynamic impact parameters and control of the gasdynamic state of outburst-hazardous coal seams during their opening are considered. The results will be used in further studies of the controlling process of stress-strain state of outburst-hazardous coal seams by hydrodynamic impact.

Wideband Measurement Approach for EIS of Lithium-Ion Batteries Using Low-Frequency Concentrated Disturbance

Precise and rapid measurement for electrochemical impedance spectroscopy (EIS) is conducive to the working state monitoring and fault diagnosis of lithium-ion batteries (LIBs). Although using a wideband signal can effectively reduce measurement time, it will deteriorate the measurement precision due to the insufficient Signal-to-Noise Ratio. Considering this issue, this paper proposes a wideband measurement approach using low-frequency concentrated disturbance for the EIS of LIBs. This approach can obtain high measurement accuracy and a wide measurement bandwidth under a small current disturbance. Besides, it effectively reduces the measurement time. First, LIBs are stimulated by a low-frequency concentrated binary sequence signal to enhance measurement precision in a low-frequency region. Then, in the entire frequency region, an accurate EIS can be obtained by a fast Morlet wavelet transform and covariance-based impedance calculation. Finally, the effectiveness of the proposed approach is verified on a buck-boost converter-based EIS measurement platform, and comparative analyses with literature methods are conducted. The experimental results illustrate that the maximum Normalized Root Square Error of the two types of LIBs at different state-of-charge is 0.37% under the response voltage amplitude of less than 10mV. Moreover, this approach is approximately 20 times faster than commercial electrochemical workstations in measurement time.

Disciplining risk: Governing through conditions on bail in the community pre-trial

Most people accused of a crime are released into the community pending the resolution of their charges. To manage perceived risk, conditions of release on bail modify and control behaviour while providing a mechanism and justification for state monitoring. Drawing on 120 interviews with accused people in Canada this paper develops a typology to theorize how bail is experienced by accused, providing a framework for understanding what motivates accused to comply or violate their bail conditions and what impact conditioning has on their lives. Our analysis reveals insights into how accused present themselves to the court, navigate conditions of release and evaluate being conditioned in the community under the threat of further criminalization. Consistent with Yule et al. (2023), accused report a complex, variegated experience of social control in the community prior to conviction. Insights from participants suggest that bail is part of a broader criminal court process, that differentiates and marks accused (Kohler-Hausmann, 2018) who lack the requisite docility and discipline as risky and requiring more intensive conditioning and monitoring.

Neuro-adaptive architecture: Buildings and city design that respond to human emotions, cognitive states

Neuro-adaptive architecture has emerged as an interdisciplinary field aiming to cultivate buildings and urban environments responsive to human emotions, cognition, and well-being. Technological advances now enable unprecedented monitoring of occupants' psychological states through unobtrusive sensors, as well as adaptive modulation of environments via “smart” architectural components. If developed responsibly, these advancements hold great potential to optimize human experience and flourishing within the built milieu. However, they also present complex ethical challenges regarding privacy, consent, data security, globalization and equitable access that require thoughtful consideration. This paper provides a comprehensive review and synthesis of the opportunities and dilemmas at the nexus of neuroscience, architecture, and urban planning. Drawing from research worldwide, it examines the multidimensional issues involved and strategies for addressing them through participatory and empathic design practices. Case studies of experimental neuro-adaptive projects are discussed and recommendations provided for longitudinal evaluation of impacts on health, social outcomes, and well-being. Concepts such as cognitive ergonomics, sensory perception and emotional design, restorative urbanism, and adaptive living interfaces are explored through diverse methodologies and design hypotheses are provided for future interdisciplinary collaboration. Overall, this paper argues that responsibly optimized neuro-adaptive architecture could enhance human thriving in complex urban environments, but precautions are necessary to avoid risks to autonomy, equity or unintended consequences. Continued rigorous interdisciplinary work is imperative to navigate these opportunities and challenges, with consideration of technical, social and ethical implications at individual and societal levels.

A Condition Monitoring Method of Hydraulic Gear Pumps Based on Multilevel Mechanism-Data Fusion

Pumps are important components in aviation fuel hydraulic systems, and thanks to the development of sensor technology and industrial intelligence technology, it is possible to achieve efficient state monitoring of pumps. However, when data quality is poor or the amount of data is small, a single data-driven model may not be able to meet diagnostic accuracy. A condition monitoring method for hydraulic gear pumps based on mechanism-data fusion is proposed. The method combines a mechanism model based on the volumetric efficiency formula with a data-driven model based on vibration signals. First, the parameters of volumetric efficiency are solved by fitting the pressure–flow relationship. Subsequently, a multichannel fusion and multikernel function-weighted ensemble support vector classification (MCMK-SVC) is developed, to establish a data-driven model. Finally, through data-level fusion, feature-level fusion, and decision-level fusion, a condition monitoring model based on mechanism-data fusion is built. Experimental verification shows that the accuracy of the three levels of fusion models exceeds 96.9%. Compared to the single data-driven model or other traditional data-driven models, the accuracy of the proposed method has improved by 3% to 33%, demonstrating the effectiveness of the mechanism-data fusion model.

A highly sensitive stretchable constraint fiber mat sensor with tailored near-zero Poisson’s ratio for very early stage of battery over charge/overdischarge monitoring

Battery state monitoring is crucial for safe operation and efficient management of lithium-ion battery (LIB) systems, but early stage of timely and accurate diagnosis remains challenging. Herein, we propose to utilize a highly sensitive stretchable strain sensor to in-situ monitor the slight strain variation of cells during charge and discharge, through which the state of the battery can be accurately reflected. The high sensing capability is realized by tailoring the Poisson’s ratio of low-modulus conductive fiber mats through a facile constraint manner. The non-uniform strain distribution together with the near-zero Poisson’s ratio synergistically result in the superior sensing performance, including ultralow strain detection limit (0.006%), high strain sensitivity particularly under small strains (gauge factor of 116.1 between 0 and 2% strain), and high strain resolution under large deformations. When being practically utilized for LIB monitoring, the constraint fiber mat sensor can produce instant resistance response reflecting the cell capacity, and offer timely warning signal once the cell is overcharged or overdischarged. The developed stretchable strain sensor possesses comprehensive advantages of low mechanical constraint, tailorable small size, easy integration and conformality to cells, and little temperature interference, which would serve as a promising candidate for future battery state analysis and safety monitoring.

The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions

Accurate and science-based prediction of bearing performance degradation has been a principal concern and a critical challenge issue in the sector of Prognostics and Health Management in the industry as the solution to promote the engineering system's reliability, availability, and maintainability. Deep Learning (DL) methods are currently a hotspot in Prognostics and Health Management. Nonetheless, with complex operating conditions, existing forecast models still inevitably suffer from three fatal flaws. Firstly, dynamic modern industrial systems and harsh operational environments make the degradation data of the bearing-rotor system highly stochastic and nonlinear. Secondly, in practice, the bearing-rotor system failure process will be governed by complex failure dynamics and degradation mechanisms, resulting in degradation data characterized by intense temporal order. Thirdly, deep learning models use a multi-layer or cellular design containing numerous weights and biases, generating more computational overhead. To fill this research gap, a new deep interval health monitoring and prediction framework named Lightweight Probabilistic Spatiotemporal (LPST-Net) is proposed, which is integrated with the concepts of lightweight and interval prediction and is capable of state monitoring and Remaining Useful Life (RUL) prediction for bearing-rotor systems under complex operating conditions. Mainly inspired by the improvement of the Gate Recurrent Unit (GRU), this paper designs a time series variable prediction algorithm and derives a new formulation named Weight Diminish Recurrent Unit (WDRU). It dramatically reduces the training parameters of the proposed LPST-Net framework and improves the convergence speed while ensuring prediction accuracy. The degradation data are obtained under 2 actual and complex operating conditions of bearing-rotor system unbalance and high temperature. The three metrics show that the proposed LPST-Net framework can achieve high-precision point prediction, suitable prediction intervals, and reliable probabilistic prediction results. It is also verified that the proposed LPST-Net framework has superior performance and more practical application value compared with seven mainstream methods, such as (b)Squeeze-WDRU-GPR-Net.

Comparative assessment of the terrain objects contours’ geometric complexity at implementing state land supervision and monitoring, an example of capital construction projects

Comparative assessment of the terrain objects contours’ geometric complexity at implementing state land supervision and monitoring, an example of capital construction projects

Liquid–Solid Triboelectric Probes for Bubbles Status Monitoring

AbstractBubble status monitoring is essential for designing material nanostructures and optimizing industrial processes. However, tracking the fluid dynamics of bubbles is challenging due to their random distribution and irregular dynamic changes. In this study, a tube liquid–solid triboelectric nanogenerator serves as a probe to investigate bubbles at the liquid–solid interface. The study explores the impact of bubble fluid dynamics on liquid–solid contact electrification, examining variables such as flow rates, volumes, and release intervals of bubbles. The study demonstrates that the liquid–solid triboelectric probe can work as a non‐intrusive and self‐powered sensor for monitoring bubble states in chemical fluid control. The sensor exhibits a sensitivity of 13.2 V·cm−3, a signal‐bubble volume correlation coefficient of 0.9964, and a rapid response time of 0.15 s. The sensor, integrated with a wireless module, enables continuous remote detection. This work provides a valuable tool for investigating tri‐phase contact electrification dynamics and presents promising technology for bubble state monitoring.

Fault Diagnosis for Reducers Based on a Digital Twin.

A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells).

DEVELOPMENT OF A DATABASE CONCEPT FOR DIGITALISING THE EXPERIENCE OF BUILDINGS AND STRUCTURES OPERATING

Problem statement. These days, the main method of monitoring the buildings and structures state is through periodic technical state assessment and monitoring. This is an important practice that guarantees the reliability and safety of the operation of buildings and structures, helping to identify and establish the obvious and possible causes of defects and damages. However, the current practice of storing the results of surveys and monitoring is often limited to records in paper reports, which makes it difficult to analyse over time. Thus, there is a need to create a digital information system designed to record and store the results of monitoring of buildings and structures. This will make the data accessible and organised for specialists, simplifying analysis and ensuring their effective use for decision-making. Such a system will significantly improve the safety and efficiency of buildings and structures management. The purpose of the article is the development of a database architecture for the digitalisation of information related to the inspection and monitoring of the technical state of buildings and structures. Conclusions. The developed database structure will allow storing information about buildings, their owners, location, structures, defects and damage identified during visual and instrumental inspections, as well as organisational and technical measures aimed at bringing structures back to normal technical state or reducing the impact of negative factors. This will contribute to more efficient management and help maintain the property in good working order, reducing the probability of emergency situations.

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

Study on the rigid-discrete coupling effect of scraper conveyor under different chain speed-load conditions

The complexity of the underground environment in coal mines often leads to varying load conditions during the operation of the scraper conveyor, which can affect the lifespan of its components and result in unnecessary energy consumption. A test platform for the scraper conveyor was constructed based on the similarity theory to measure torque, speed, chain tension, and scraper acceleration during transportation. A DEM-MBD model of the scraper conveyor was developed and validated through transport tests and similarity theories to analyze the rigid-discrete coupling effect under different chain speed-load conditions. The results revealed a stratification phenomenon and a Brazilian fruit effect in the movement of coal. The average velocity of the upper and lower coal layers gradually increased during the transportation, while the difference between them gradually decreased. As the load increased, the stacking density and height of coal between scrapers also increased, leading to a higher force exerted on the scraper and chain. As the chain speed increased, the stacking density and height of coal between scrapers decreased, along with a decrease in the force applied to the scraper and chain. The formation of three-body wear necessitates a specific positional condition. When the scraper (chain)- coal-deck plate (chute liner) forms a particle stagnation state, severe wear occurs on the parts. This study provides a foundation for analyzing the transport mechanism of scraper conveyor from the particle perspective, offers a simulation reference for analyzing the mechanical and tribological characteristics of the line pan and scraper chain, and serves as a guideline for the future development of transportation state monitoring and the optimization and enhancement of components under different working conditions.

Active and passive monitoring of corrosion state of reinforced concrete based on embedded cement-based acoustic emission sensor

In this paper, a cement-based acoustic emission sensor is embedded in reinforced concrete, and the corrosion damage inside the concrete is activated by swinging ball impact. The changes of low frequency and high frequency acoustic emission signals are captured and analyzed by the sensor, and the concept of low-high-frequency (LH) is proposed. The changes of acoustic emission characteristics during the corrosion of steel bars were studied, and the effects of protective layer thickness and activation site on the ratio of LH were analyzed. The results show that with the increase of the corrosion degree, the amplitude of the frequency signal in the range of 13 kHz–22 kHz decreases gradually, from 2.59 mV to 1.29 mV. The amplitude of the frequency signal near 170 kHz increases first and then decreases with the increase of corrosion time. The frequency signal near 22 kHz is used as the low frequency signal, the amplitude of the frequency signal near 170 kHz is used as the high frequency signal, and the ratio of the two is used as the low-high-frequency (LH). The evaluation result of this ratio is consistent with the ultrasound test results. As the sound source continues to move to both ends of the concrete sample, the distance between this sound source and the sensor gradually increases, the low frequency signal decreases significantly, and LH changes significantly, influenced by the reflection of the boundary.

Nonlinear recurrence analysis of piezo sensor placement for unmanned aerial vehicle motor failure diagnosis

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor.

An acoustic imaging recognition based cutting tools wear state prediction method

Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach.