Installation Of Sensors Research Articles

Industrializable industrial internet of things (I3oT) for a massive implementation of industry 4.0 applications: a press shop case example

This article presents a new concept for generating industrializable IIoT applications, Industrializable Industrial Internet of Things ( I 3 oT ). There is an important limitation that is significantly slowing down its proliferation in the IIoT application industry. The installation of sensors, their wiring and data extraction. The increasing number of machines to be sensorised prevent the proposed solutions from being applied in the industry in a massive way, due to the high cost of their installation. The I 3 oT philosophy implies to use the available installation in factories to develop proliferative IIoT application, with the main feature to be easily extrapolated and scalable to other systems at a very low cost. This paper defines this concept based on a previous research group development, the Mini-Terms, and explores the use of the concept I 3 oT in stamping process by developing the routing called Criterion-360. A routine that allows obtaining stamping data available in the PLC easily. This data can provide sufficient information to develop a multitude of useful applications for the industry. An I 3 oT application is presented for predictive maintenance based on tonnage sensors and we explore other tools for energy saving and the feasibility of developing a digital twin for the stamping process.

A tool wear monitoring method driven by workpiece surface quality inversion and EfficientNet

Abstract Tool wear status monitoring plays an important role in the safety of machine and the efficiency of production. Due to the limitation of sensor installation and complex operation conditions, traditional machine vision methods are difficult to directly act on the tool itself to obtain corresponding wear status information. Meanwhile, the surface quality of the machined workpiece can provide indirect insights into the wear status of the tool to a significant degree. Therefore, an indirect tool wear status monitoring method is proposed in this paper by workpiece surface quality inversion and EfficientNet. Firstly, the surface texture data of workpiece are obtained in the form of image by an industrial camera, and the representative features that indirectly reflect tool wear status are further highlighted by using the weighted average image grayscale and the Laplacian operator. Secondly, to monitor tool wear effectively, we've optimized three key settings, width, model depth, and input image resolution, of the EfficientNet backbone network. This optimization boosts its ability to understand and generalize, ensuring it can handle various surface textures and extract crucial information accurately. This, in turn, enhances the accuracy of our tool wear monitoring system. Finally, the improved EfficientNet network is further utilized as a pre-trained network to achieve tool wear state monitoring. Utilizing the powerful representation ability of the pre-trained model and the surface texture features of workpiece, the model can accurately identify and judge the wear degree of the tool. The tool experiment results show that the proposed tool wear monitoring method has achieved remarkable results with recognition accuracy of 97.77%. While other popular methods such as VGG19, ResNet50, and ResNet152, whose training accuracies are 95.66%, 96.37%, and 91.27%, respectively. The comparative results highlight the excellent performance of the proposed method in recognition rate, training time and generalization performance.

An Analysis of Rainfall-Derived Infiltration and Inflow Using Water Level-to-Flow Conversion

During rainfall, the rise in groundwater levels, coupled with defects in pipe connections and manhole joints, increases Rainfall-Derived Infiltration and Inflow (RDII) in separate sewer systems. This results in exceedances of planned wastewater volumes and a surge in Separate Sewer Overflows (SSOs). To implement effective measures in the sewer system, it is crucial to monitor the long-term sewage flow under both dry and wet weather conditions to identify RDII sources within the sub-basins. However, because of the cost disparity between installing flow meters and water-level sensors, many municipalities opt for the latter, which is more economical but limits the accuracy of flow measurement. Therefore, this study developed a methodology to estimate flow rates based on measured water-level data, which were validated using adjacent flow measurement data. This water level-to-flow conversion method is expected to be utilized through the installation of water-level sensors owing to their ease of deployment, in addition to budgetary constraints that limit the use of flow meters. This approach enables the application of water-level data for flow estimation.

Applying Photoelectric Sand Meter for Monitoring of Suspended Solid Matter in Rivers

River ecosystems are integral to sustainable environmental development, playing a crucial role in understanding suspended solid matter (SSM) transport dynamics and soil conservation. Accurate monitoring of SSM concentrations in watersheds is foundational for these studies. This research introduces and evaluates a novel HHSW·NUG-1 photoelectric sand meter, specifically designed for SSM measurement. Its reliability was validated at three hydrological stations, including Xiaolangdi. The instrument, based on light scattering principles, is optimized for environments with high SSM loads and rapid flow rates. Laboratory tests indicate a measuring range of 0 to 730 kg/m3, and field trials show effective operation within 0 to 375 kg/m3, meeting the monitoring needs of hydrological stations. Through comparative analysis of measurement data, we established conversion relationships for various SSM concentration ranges, confirming that the instrument’s system error is less than 1%. The photoelectric sand meter adheres to standards outlined in the “Guidelines for SSM Test in Rivers”, demonstrating stability in reliability, calibration methods, observation accuracy, real-time monitoring, data storage, and continuous operation. For optimal use, adherence to relevant hydrological instrument standards is recommended, particularly in stations requiring SSM analysis. Standard sampling and calibration of conversion coefficients should be conducted, and proper sensor installation is crucial to avoid interference from flow conditions. In conclusion, the HHSW·NUG-1 optoelectronic sand meter exhibits stable and reliable performance in practical applications, with broad potential for rapid deployment in other river hydrological stations.

Evaluating a Smart Textile Loneliness Monitoring System for Older People: Co-Design and Qualitative Focus Group Study.

Previous studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development. The use of these technologies and infrastructural questions have been insufficiently addressed. Systems generally consist of sensors or software installed in smartphones or homes. However, no studies have attempted to use smart textiles, which are fabrics with integrated electronics. This study aims to understand the design requirements for a smart textile loneliness monitoring system from the perspectives of older people and stakeholders. We conducted co-design workshops with 5 users and 6 stakeholders to determine the design requirements for smart textile loneliness monitoring systems. We derived a preliminary product concept of the smart wearable and furniture system. Digital and physical models and a use case were evaluated in a focus group study with older people and stakeholders (n=7). The results provided insights for designing systems that use smart textiles to monitor loneliness in older people and widen their use. The findings informed the general system, wearables and furniture, materials, sensor positioning, washing, sensor synchronization devices, charging, intervention, and installation and maintenance requirements. This study provided the first insight from a human-centered perspective into smart textile loneliness monitoring systems for older people. We recommend more research on the intervention that links to the monitored loneliness in a way that addresses different needs to ensure its usefulness and value to people. Future systems must also reflect on questions of identification of system users and the available infrastructure and life circumstances of people. We further found requirements that included user cooperation, compatibility with other worn medical devices, and long-term durability.

Fibre Optic‐Based Patch Sensor for Crack Monitoring in Concrete Structures

ABSTRACTIn the context of the necessity to monitor civil engineering structures for accident prevention, this paper presents the performance of an innovative optical fibre patch sensor that can be embedded or glued on the concrete's surface. The sensor is composed of a fabric comprising an optical fibre network. When employed in concrete structures, it is capable of detecting opening cracks in the order of 0.01 mm, thereby satisfying the regulatory standards for limit states (Eurocode 2). This sensor is capable of accurately tracking the evolution of crack, including cyclic changes, by sensing both the increase and decrease in crack width. The procedure outlined in this paper includes a preliminary investigation into the influence of fibre diameter and density within the patch, which has revealed that fibres of 750 μm in diameter exhibit optimal performance, as well as the attachment method to concrete surfaces. Subsequently, the process of calibration is detailed, followed by numerous laboratory tests that were conducted on reinforced concrete samples of varying sizes. Comparisons and validations of optical fibre patch sensor measurements were performed using digital image correlation, resulting in a sensitivity of the sensors reaching 0.01 mm/K, and an accuracy ranging between 0.1% and 1%. The discussion within this paper considers the installation of this new sensor both externally and internally within real reinforced concrete structures.

Implementation of integrated IoT-based monitoring system to improve clean water services at KP-SPAMS Tirta Jatisari

The Tirta Jatisari Water Supply and Sanitation Management Group (KP-SPAMS) faced challenges ensuring transparency in water usage and efficiency in billing due to the limitations of conventional water meters. These meters lacked real-time monitoring capabilities, hindering leak detection and leading to billing inaccuracies. To address this, an Internet of Things (IoT)-based monitoring system integrated with a web application was implemented to improve transparency and operational efficiency. The methodology involved needs analysis and system design, IoT device development, installation of flow meter sensors, development of a user-friendly web application for both KP-SPAMS managers and consumers, and comprehensive system training and socialization. Results demonstrated improved billing accuracy and speed, along with real-time water usage monitoring for both KP-SPAMS and consumers, facilitating timely leak detection. The implementation successfully increased consumer satisfaction and fostered the development of technologically adept human resources.

Defining the Best Strain Gauge Placement through Numerical Simulations of the Mechanical Behavior of the Modified-WOL Specimen

In structural steels, cracks are an inevitable occurrence, demanding an examination of their ability to resist crack propagation under expected loads and various environmental conditions. The process of experimentally determining fracture toughness comes at a significant expense, involving the use of multiple specimens and extended testing periods, which hinders the swift acquisition of crucial parameters. An alternative approach entails the execution of a constant displacement test using the Modified-WOL specimen, which is equipped with an instrumented bolt for self loading. The primary objective of this method is to provide the Threshold Stress Intensity Factor for Environment-Assisted Cracking, utilizing only a single specimen. The incorporation of electronic instrumentation is essential for obtaining reliable, real-time data. Despite its importance, a consensus is lacking in the literature regarding the best location for sensor installation on the Modified-WOL specimen, potentially influencing its mechanical response. This study aims to explore the mechanical behavior of the three components constituting the Modified-WOL specimen test in accordance with the guidelines outlined by ISO 7539-6. Employing the Finite Element Method with two- and three-dimensional models, this investigation utilizes the Augmented Lagrangian method for formulating the contact regions. Additionally, quarter-point singular elements are employed to accurately represent stress singularity behavior at the crack tip. The numerical computation of fracture parameter (KI) is conducted using the Displacement Correlation Technique. Comparative analyses are then carried out between the numerical and the analytical values of KI determined based on the ISO 7539-6 standard. The findings demonstrate alignment with empirical literature values, ultimately contributing to the identification of the best location for strain gauge installation among the highlighted regions of considerable interest.

Development of an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle.

To enhance meteorological detection methods, an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle (UAV) was proposed. Computational fluid dynamics (CFD) was employed to model the surrounding airflow distribution during UAV hovering, thereby determining the optimal positions for sensor installation. A novel radiation shield was designed for the temperature sensor, offering both excellent radiation shielding and superior ventilation. To further improve temperature measurement accuracy, an error correction model based on CFD and neural network algorithms was designed. CFD was used to quantify the temperature measurement errors of the sensor under different environmental conditions. Subsequently, random forest and multilayer perceptron algorithms were employed to train and learn from the simulated temperature errors, resulting in the development of the error correction model. To validate the accuracy of the detection system, comparative experiments were conducted using the measurement values from the 076B temperature observation instrument as a reference. The experimental results indicate that the mean absolute error, root mean square error, and correlation coefficient between the experimental temperature errors and the algorithm-predicted errors are 0.055, 0.066, and 0.971 °C, respectively. The average error of the corrected temperature data is 0.05 °C, which shows substantial agreement with the reference temperature data. During UAV hovering, the average discrepancies between the temperature, humidity, and air pressure data of the detection system and the ground-based reference data are 0.6 °C, 1.6% RH, and 0.77 hPa, respectively.

A technical survey on practical applications and guidelines for IoT sensors in precision agriculture and viticulture

Climate change pose significant challenges to modern agriculture management systems, threatening food production and security. Therefore, tackling its effects has never been so imperative to attain sustainable food access and nutrition worldwide. In the case of viticulture, besides jeopardizing grape production, climate change has severe impact in quality, which has becoming more challenging to manage, due to the increasingly frequent fungal contamination, with consequences for relevant quality parameters such as the aromatic profiles of grapes and wines and their phenolic compounds. This has been leading to a reconfiguration of the wine industry geostrategic landscape and economy dynamics, particularly in Southern Europe. To address these and other emerging challenges, in-field deployable proximity-based precision technologies have been enabling real-time monitoring of crops ecosystems, including climate, soil and plants, by performing relevant data gathering and storage, paving the way for advanced decision support under the Internet of Things (IoT) paradigm. This paper explores the integration of agronomic and technological knowledge, emphasizing the proper selection of IoT-capable sensors for viticulture, while considering more general ones from agriculture to fill gaps when specialized options are unavailable. Moreover, advisable practices for sensor installation are provided, according to respective types, data acquisition capabilities and applicability.

Research on hydrogen diffusion and sensor arrangement strategy in a marine engine room

This paper investigates the diffusion behavior and spatiotemporal concentration distribution of hydrogen in the marine engine room under different leakage conditions. By analyzing the response times and alarm rates of sensors, it identifies the optimal positions for sensor installation. When hydrogen leaks in the upper and lower layer sequentially, the maximum diffusion volumes within 150s are 1804.0 m3 and 2281.9 m3, while the minimum diffusion volumes are 784.7 m3 and 1377.9 m3. These findings reveal significant differences in hydrogen diffusion behavior under different leakage locations and directions. Sensor arrangement recommendations are made for specific height ratios (h/H) where alarm rates are highest and response times shortest. The recommended installation positions are S4∼S6, S11∼S12 at h/H = 0.30, and S6, S12∼S11, S16, S25 at h/H = 0.52, and S3, S19, S2, S5, S10, S14, S20∼S22 at h/H = 0.97, as well as near the interfaces of hydrogen pipelines, valves, and gas equipment interfaces with a high risk of leakage.

Evaluating Thin-Film Thermocouple Performance on Additively Manufactured Turbine Airfoils

Abstract As gas turbine engine designs continue to target higher turbine entry temperatures for increased thermal efficiency, gas turbine manufacturers and operators require additional feedback from life-limited engine components. Moreover, additively manufactured (AM) hardware is becoming more prevalent in the engine development cycle to reduce component lead times and associated costs. Although additive manufacturing unlocks unique capabilities for sensor integration, the inherent roughness from additive surfaces poses unique challenges to the direct-write sensor installation processes. The current study addresses these realities by demonstrating sensor operation on additively manufactured vane hardware in a turbine research facility operating at scaled conditions. Thin-film thermocouples were deposited on fully-cooled AM turbine vanes and tested over a range of operating conditions in a one stage research turbine at Penn State University. The low-profile surface-mounted sensors were compared with traditional small-diameter embedded thermocouples in terms of calibration accuracy and durability. A comparison between traditional masked thermal spray and direct-write installation was also evaluated as part of the sensor integration strategy for the AM vanes. Ultimately, this study shows thin-film thermocouples on additively manufactured airfoils can operate reliably over an extended test campaign in rig-scaled conditions. Furthermore, the measurement accuracy of thin-film thermocouples demonstrated through this study is equivalent to traditional mineral-insulated thermocouple sensors showing the utility of these technologies for future turbine research and development applications.

Multi-Cavity Nanorefractive Index Sensor Based on MIM Waveguide.

Within this manuscript, we provide a novel Fano resonance-driven micro-nanosensor. Its primary structural components are a metal-insulator-metal (MIM) waveguide, a shield with three disks, and a T-shaped cavity (STDTC). The finite element approach was used to study the gadget in theory. It is found that the adjustment of the structure and the change of the dimensions are closely related to the sensitivity (S) and the quality factor (FOM). Different model structural parameters affect the Fano resonance, which in turn changes the transmission characteristics of the resonator. Through in-depth experimental analysis and selection of appropriate parameters, the sensor sensitivity finally reaches 3020 nm/RIU and the quality factor reaches 51.89. Furthermore, the installation of this microrefractive index sensor allows for the quick and sensitive measurement of glucose levels. It is a positive contribution to the field of optical devices and micro-nano sensors and meets the demand for efficient detection when applied in medical and environmental scenarios.

Model Test on Acoustic Emission Monitoring of Loess Slope Failure.

The three stages of loess collapse are characterized by notable concealment and sudden onset due to the sudden nature of loess collapse and the prolonged duration of the peristaltic deformation stage. Traditional displacement monitoring methods struggle to detect early signals of instability and failure, leading to poor timeliness in disaster warnings. This project begins by examining non-force field information related to the loess collapse process. It focuses on acoustic emission monitoring and employs model tests to identify effective waveguide rods for monitoring loess collapse. Additionally, the project investigates the evolution anomalies of acoustic emission parameters before and after loess collapse failure, aiming to establish early warning criteria for loess collapse based on acoustic emission. This work provides a theoretical basis for monitoring and early warning of loess collapses. This study evaluates five parameters of the active waveguide system: sensor installation method, filling material, waveguide rod wall thickness, outer wrapping material, and outer wrapping wall thickness. The densities of the filler materials were tested using the optimal parameters derived from the tests to identify the best configurations for active acoustic emission (AE) waveguide systems suitable for monitoring loess collapse. Subsequently, a one-sided connected loess collapse model was employed for indoor tests, integrating real-time AE monitoring with the active waveguide method. This model facilitates the exploration of AE response characteristics during loess collapse and the analysis of destructive forms of loess collapse and time-sequence evolution of AE ringing counts throughout the deformation and destruction process. Results indicate that using filler materials with high elasticity modulus, high compactness, and low Poisson's ratio, along with thin outer wrapping and waveguide rod walls, leads to strong AE signals. As deformation damage of loess collapse intensifies, the number of AE ringing counts notably increases. A rapid rise in cumulative ringing counts can indicate a "sudden increase", or the b-value may stabilize, providing precursor information for loess collapse.

A Summative Review of Advances in Sensor Technology for Precision Agriculture

Sensor technology has become a cornerstone of precision agriculture, offering a wide array of applications that enhance farming efficiency, productivity, and sustainability. By providing real-time data on soil health, crop growth, and environmental conditions, sensors enable farmers to optimize inputs such as water, fertilizers, and pesticides, reducing waste and improving yields. The development of advanced sensors, such as multi-spectral and hyper-spectral imaging, combined with artificial intelligence (AI) and machine learning (ML) algorithms, has revolutionized crop monitoring and disease detection, allowing for timely interventions that prevent significant losses. Additionally, the integration of wireless sensor networks (WSNs) and the Internet of Things (IoT) facilitates the seamless collection and transmission of data, enabling remote and automated farm management. This technology has proven instrumental in addressing the challenges of food security by increasing agricultural productivity and reducing input costs. Sensor technologies also promote sustainable land and water management by improving irrigation efficiency and reducing chemical runoff, thus minimizing environmental impact. Economic benefits are substantial, with farmers experiencing cost savings, increased yields, and higher profitability. Moreover, sensor technology contributes to climate-smart agriculture by improving resource use efficiency and reducing greenhouse gas emissions. However, challenges remain, including the high cost of sensor installation and maintenance, limited accessibility in remote regions, data privacy concerns, and the lack of interoperability between different sensor platforms. The future of sensor technology lies in the development of low-cost, biodegradable sensors, the increased use of autonomous and robotic platforms, and enhanced AI integration for more accurate data interpretation. Expanding the use of sensors in smallholder and developing world agriculture will be crucial for global food security and environmental sustainability, offering a promising path toward a more resilient and efficient agricultural system.

Monitored and Cared for at Home? Privacy Concerns When Using Smart Home Health Technologies to Care for Older Persons

Background States and families are facing growing challenges provide adequate care for older persons. Smart home health technologies (SHHTs) in the forms of sensor or robotic devices have been discussed as technical solutions for caregiving. Ethical and social concerns are raised with the use of such technologies for caregiving purposes, a particularly prominent one being privacy. This paper contributes to the literature by distinguishing privacy concerns into both the type of technologies and conceptual dimensions. Methods Data for this paper stem from sixty semi-structured interviews with older persons, informal, and formal caregivers living in the German-speaking regions of Switzerland. All information related to privacy, that were initially inductively coded, were thematically sorted into four dimensions of privacy (physical, psychological, social, and informational) and by the type of technologies studied. Results Participants were especially concerned about privacy intrusions from smart wearables and ambient sensors than robotic technologies, which may be due to the relative lack of familiarity with the latter. Informational privacy was evident in the context of data collection capacities and potential for misuses of data. The installation and implementation of both visual and ambient sensors induced discomfort to their senses of physical space. Alerts of smart wearables and obtrusive sightings of SHHTs garnered worries related to stigmatization and manipulation, indicating intrusions into end-users’ psychological privacy. Little discussions of social dimensions of privacy were evident in the data, even toward robotic technologies for their functions to promote social interactions for older persons. Conclusions This paper is one of the first that use the stratification approach on empirical data to highlight the multi-faceted privacy concerns when technologies may be implemented in elder care. Our paper could thus supports potential end-users in deciding which technologies to use and how to balance different privacy concerns against other values that they may hold important.

Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems

The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.

Rapid diagnosis of proportional valve pilot stage stiction fault via dynamic model-based temporal features

As the key flow control elements in modern mechatronic systems, electrohydraulic proportional valves determine the total system operating status. Due to effects such as mechanical wear, micro scale particles may accumulate in the oil and eventually cause stiction in the valve spool. Therefore, early prognosis of valve faults via spool displacement sensing is critical for efficient and economical operations. However, the spool stiction is difficult to measure due to the fully enclosed design of valves, excluding the possible sensor installation options. Traditionally, the sensor-less fault diagnosis is done by machine learning to classify the features extracted using frequency domain methods, which can be time consuming and the trained model is computationally too heavy for the onboard controller. In this paper, a light-weight spool stiction diagnosis method is proposed, where time-series features extracted from coil current signals as well as spool dynamics can be used to quantitively determine the stiction severeness. A hardware-in-the-loop test bed is designed to perform valve spool stiction simulating experiments, and a piloted operated solenoid proportional valve is selected as the validation target. A three-layer regression neural network is established as the baseline. Initial results show the proposed model can detect spool stiction at various degrees, which accuracies are on par with the baseline neural network but nearly three orders of magnitude faster.

Experiment and detection method for loose bolts in sensor installation

Experiment and detection method for loose bolts in sensor installation

IoT-Enabled Street Dustbin Monitoring System Based on Machine Learning

Abstract In this article, a novel method is proposed by using the power of the Internet of Things (IoT) and Machine Learning (ML) to monitor and manage street dustbins effectively. The proposed innovative solution aimed at optimizing waste management processes and improper disposing of the waste in street dustbins. The system involves the installation of sensors in dustbins, which collect real-time data about the fill level of the bins. The capability of the proposed model allows for dynamic adjustment of waste collection schedules, reducing unnecessary trips and thus saving time, energy, and resources. In addition to that, the model is also trained in various waste disposal scenario such as improper disposal, massive garbage dumps, and e-waste. The proposed model is able to predict and report the trained conditions with the 100% accuracy. The system is implemented using Raspberry Pi 4, a single-board computer with the necessary hardware interface. A suitable web interface has been developed to oversee the status of dustbins and provide optimum travel route for collection of waste from the street dustbins. The proposed system not only enhances operational efficiency but also contributes to environmental sustainability.