Sensor System For Monitoring Research Articles

Data mining on uncertain data based on frequent probability

Uncertain data exists in many applications, such as sensor monitoring systems, location-based services, and biological databases. To handle a large amount of uncertain information, probabilistic databases have been proposed. The main research involves interpreting probabilistic databases using possible world semantics and discovering frequent patterns within probabilistic databases. Since the possible worlds in probabilistic databases grow exponentially, this mining process is a technical challenge.

Smart Sensor Placement For Thermal Monitoring Of Concrete

Structural health monitoring (SHM) has become an inevitable part in a life span of a structure due to its potential to ensure the public safety and to increase the life span of the structure. Monitoring any kind of structures for various parameters, using wireless smart sensors has gained popularity in recent past. This paper discusses the development of low cost real time wireless smart sensor monitoring system to monitor early age concrete temperature in real time. Temperature of two early age concrete mixes (Mix1, Mix2) were measured in real time for 24 hours by using DS18B20 sensors connected with the Node Mc u, which is an open source IOT platform. Temperature measurements were saved and visualized in real time using Thing Speak TM which is an open IoT online platform with MATLAB analytics. The temperature sensor DS18B20 was selected such that it is suitable to measure temperature readings of the concrete without any interference of the chemical reactions in concrete. Calibration methods and temperature variation with different concrete mixes are also discussed. It could be seen that the wireless temperature monitoring system performed adequately and it can be considered as a better low cost alternative for traditional wired temperature monitoring system.

Integration of lean manufacturing system with novel intuitive fuzzy syncretic lean frame work to improve the overall equipment effectiveness

Lean manufacturing embraces the principle of doing more with less by removing non-value-added activities from manufacturing processes to preserve effectiveness, flexibility, and profitability. Predictive maintenance in production stages are more critical since it involves machines which are prone to failure and a post failure maintenance stagnates the production which creates bottlenecks due to machine failures. Hence in this work a novel intuitive fuzzy syncretic lean frame work has been implemented to incorporate the need of intelligent systems. The framework is divided into phases which employs Fuzzy logic with embedded smart sensors for effective utilization of man machine and materials in order to improve the effectiveness of a production floor. A novel time based forecasting is done in the design phase which implements the lean tool Takt time. The manufacturing phase uses the sensors to determine the predictive maintenance of the machines thereby implementing continuous flow as the lean tool and the inspection phase uses smart sensor system for real time continuous monitoring of the machining process thereby incorporating Poka-yoke as a lean tool. Thus by implementing the framework the overall equipment effectiveness is achieved which helps in achieving continuous flow of products and defect free products in any production firm.

Self-powered wireless sensor system utilizing a thermoelectric generator for photovoltaic module monitoring application

In this work, we demonstrate a self-powered wireless PV module monitoring system that utilizes a thermoelectric generator (TEG) to convert residual thermal energy from the PV module into electrical power. We investigated the TEG performance with and without the heat sink. Results show that the temperature difference between the hot and cold sides of the TEG increased to 7.2 °C with the heat sink, compared to only 1 °C without it, at a hot side temperature of 50 °C. We integrated the TEG/heat sink with the PV module, which served as the heat source, achieving a maximum output power of 0.981 mW at a voltage of 0.06 V under a temperature gradient of 3.6 °C in a 1 sun condition. We successfully demonstrated a self-powered wireless PV monitoring sensor system by integrating a step-up voltage converter, microcontroller, IR thermometer, Bluetooth communication module, and the TEG/heat sink, which generated sufficient power for the monitoring system operation. The findings introduce a novel solution for wireless PV module monitoring that operates independently of grid connections or battery power. This innovation not only signifies advancements in renewable energy management but also opens new opportunities in the Internet of Things (IoT) sector.

A Study the Water Quality Measuring Equipment with Automatic Sensor System

This paper studied the water quality in Dong Khoai Village, Xaythani District, Vientiane Capital, which is the primary water pumping location for agriculture. The area has a population of over 1735 citizens and cover more than 450 hectares. Common issue found in Markhiaw river. Stream is its toxicity to certain rice field, causing it inability to grow rice despite using fertilizer. Furthermore, this stream gives dissatisfaction odor, negatively affects people who get into the water for fishing and occasionally found death fish in the areas. In order to assess the water quality and disseminate the results to the public. Therefore, this study aims to investigate the creating of an automated sensor system for high precision water quality monitoring in the water. Four sensors; pH (hydrogen potential), EC (electro conductivity), TDS (total dissolved solid), and temperature are used in this procedure. The sensor system was tested, and build a small station uses solar energy and saves information into the SD card and available online by Blynk, and compare the test results with national environmental standards. The research revealed that, in comparison to the standard instrument S5 pro parameter, the measuring stations for the water quality values of all four sensors are accuracy value of 92.91% to 99.34%. Based on the information gathered over the course of two months, from February 13 and March 18, the pH value will be low, at 5. The pH value is high, at 11, between 13, 17 and 18 February 2024. These numbers are considered to be in the standard ranks. Regarding the dissolved solids in the water vary from 3 to 100 mg/L, and the electrical conductivity is between 100 and 180 µS/cm. They are also fall upon the National Environmental Standard. The Temperature value is high, at 40.81 ºC on 4 February 2024 and from 02 March 2024, the Temperature value will be low, at 24.4ºC.

Development of a wireless smart sensor system and case study on lifting risk assessment

With the widespread adoption of Industry 4.0 and smart manufacturing concepts across industries, sensor development, system integration, and data analysis have become important aspects of efficient manufacturing operations. In addition to monitoring the performance of machines, significant importance is given to human condition monitoring in factories, using body-worn sensors to ensure the well-being of workers and for injury prevention. This research presents the development of a body-worn sensor system capable of sampling acceleration and rotation data up to 400 Hz and wirelessly transmitting the data over Bluetooth Low Energy (BLE). Further, the communication protocols for data acquisition, data communication within the device, Real Time Operating System (RTOS) programming, and multi-threading are described. This system is designed in such a way that multiple devices can be connected to the Data acquisition (DAQ) system simultaneously, and data is collected from the sensors in a synchronized manner. This information is valuable for the wider adoption of sensor systems for human condition monitoring in industry. Lastly, to test the system’s capabilities, a case study of lifting risk assessment is presented, where data collected from the accelerometer and gyroscope are used to determine a relative estimate of the physical stress associated with a manual lifting task by using different machine learning (ML) algorithms. The case study highlights how sensor placement, feature extraction, and sensor types influence machine learning models. As the sensor system can perform computations on the edge, a framework to carry out real-time lifting risk assessment using lightweight algorithms and the most important data features is proposed.

A novel low-cost sensors system for real-time multipollutant indoor air quality monitoring – Development and performance

Indoor air pollution poses a significant health risk, thus demanding effective indoor air quality (IAQ) monitoring strategies. Low-cost sensors (LCS) have gained attraction for IAQ monitoring, but their data accuracy and robustness remain key challenges. This study addresses gaps in the literature by developing and evaluating a novel low-cost monitor prototype for IAQ monitoring. A detailed design methodology based on requirements analysis was used to create two identical low-cost monitors (LCM) prototypes for multipollutant monitoring, including PM2.5, CO2, CO, O3, NO2, temperature and relative humidity. The LCM were deployed in an in-field monitoring campaign alongside research-grade instruments. The uncorrected sensor signals showed linear response compared to research-grade instruments with high Pearson Correlation Coefficients for 1-min mean: PM2.5 (0.97), CO2 (0.81–0.89), CO (0.95–0.98), and O3 (0.80–0.85). The concentration range of pollutants was observed to play a crucial role in the response and accuracy of the LCS. Kalman filter was implemented with optimised parameters to reduce noise from the signal of ozone sensors. Univariate and multivariate linear models were used to field calibrate the sensors. Linear regression models, with high coefficients of determination (>0.8) and low error values, imply that the developed LCM prototypes can be reliably used for indicative monitoring. In most of the cases, univariate linear models using only the sensor response of LCS as explanatory variables showed significant improvement. Future work will involve longer monitoring campaigns in different microenvironments, evaluation of calibration drift and to assess failure episodes in long-term use.

Carbon-based Wearable Pressure Sensor System for Real-time Step Frequency Monitoring

Abstract Wearable pressure sensors have attracted great attention in human motion and health monitoring due to their sensitivity, flexibility and portability that can provide continuous physiological information recording. However, current pressure sensors used for wearable motion monitoring suffer from low sensitivity, poor resistance to interference, and the inability to be mass-produced. In this study, we proposed the utilization of multi-walled carbon nanotubes (MWNTs) as sensing materials, fabric as substrates, and screen printing process for fabricating flexible pressure sensors. The fabricated sensors exhibited a high sensitivity at low pressure, and good stability of more than 3000 cycles. Moreover, we designed a wearable step frequency monitoring system for real-time recording of footstep frequency during human walking activities. The results demonstrated that the system accurately monitors frequency under different walking speeds and footstep activities during various movements, providing a promising strategy to develop wearable and wireless sensing system for real-time motion monitoring.

Dynamic Peripheral Hemoperfusion Distribution Monitoring Based on Janus Flexible Sensor System.

Peripheral vascular disease is a worldwide leading health concern. Real-time peripheral hemoperfusion monitoring during treatment is essential to plan treatment strategies to improve circulatory enhancement effects. The present work establishes a Janus flexible perfusion (JFP) sensor system for dynamic peripheral hemoperfusion monitoring. We develop a Janus structure with different Young's modulus to improve the mechanical properties for motion artifacts suppression. Besides, we propose a peripheral perfusion index (PPI) based on an optical perfusion model that is experimentally verified using an in-vitro model. The effectiveness of the system is assessed in three experimental scenarios, including motion artifact-robust test, induced vascular occlusion, and peripheral hemoperfusion monitoring with the intermittent pneumatic compression treatment. The noise level of the traditional rigid sensor is five times that of the JFP sensor within the effective signal frequency domain when there is movement. The PPI can effectively discriminate between different peripheral hemoperfusion states and has a correlation coefficient of 0.92 with the Laser Doppler flowmetry (LDF) mean values. The kappa statistic between the JFP sensor and LDF is 0.78, indicating substantial agreement to estimate the peripheral hemoperfusion improvements. The sensor system we proposed can monitor peripheral hemoperfusion variation in real-time and is insensitive to motion artifacts. The proposed sensing system provides a functional module for real-time estimation of peripheral hemoperfusion during clinical interventions.

An ultra-thin bolt tension sensor and online monitoring system: For application in hydropower plant unit

An ultra-thin bolt tension sensor and online monitoring system: For application in hydropower plant unit

CORS station for synergistic monitoring of multivariate surface parameters in expansive soils

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can’t obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as “precipitable rainfall, soil moisture, and three-dimensional deformation” and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards.

Distributed polymer optical fiber sensors using digital I-OFDR for geotechnical infrastructure health monitoring

We present a distributed polymer optical fiber sensor system for deformation monitoring of geotechnical infrastructure. The sensor system is based on the digital incoherent optical frequency domain reflectometry (I-OFDR) for the detection of local strain events along a perfluorinated polymer optical fiber (PF-POF) used as a sensing fiber. For the best possible load transfer, the PF-POFs were integrated onto geotextiles which pose a sensor carrier for the sensing fiber. By using elastic PF-POF instead of a standard glass fiber as a sensing fiber the strain range of geotextile-integrated fiber optic sensors could be extended up to 10 % in accordance with the end-user requirements. In order to reduce overall costs of the sensor system and achieve its robustness required for use on site, the standard vector network analyzer used by default was replaced by an integrated digital board for optical frequency domain analysis. This implementation showed a significant improvement in the dynamic range and sensitivity compared to the previous measurement system based on the conventional network analyzer. The functionality of the entire sensor system was proven in the field test during the installation of two geomats with integrated PF-POFs embedded into a road embankment in a section of the B 91 federal road near Werschen in the greater Leipzig area. To monitor the installation-related changes in the condition of both sensor-based geomats several control measurements on the embedded PF-POFs and co-integrated reference glass fibers were carried as the construction works progressed. The measurements recorded in the field confirm the great potential of the selected PF-POF used as the distributed fiber optic strain sensor. The future management of the remaining challenges of industrial strain-coupled sensor integration can result in a successful market launch of the PF-POF-sensor integrated into geosynthetics. The sensor system is ready to be used for practical application.

Destructive Testing of the Pinkabach Railway Bridge – Large scale Testing of Sensor Systems for Fatigue Crack Monitoring and System Identification

The work presents experiments carried out on an out-of-service steel railway bridge, the so called Pinkabach bridge. The objective was to test advanced and novel sensor systems, i.e. Distributed Fiber Optic Sensing (DFOS) and Acoustic Emission (AE), for the monitoring of fatigue cracks, both for crack initiation and subsequent crack growth, in steel railway bridges. Also, sensor systems for overall system identification, i.e. Tiny Motion (TM), Distributed Acoustic Sensing (DAS) and dynamic response analysis, have been tested. The Pinkabach bridge was a single span plate girder railway bridge with a span width of 21,5m that was taken out of service and transported to a secure environment that allowed for destructive testing with permanent access to the structure itself under controlled conditions. By harmonic excitation of the structure at its first resonance frequency, cyclic stress levels comparable to the stress levels during train passage were generated and it was possible to emulate the fatigue load from decades of train traffic within the limited period of the experiments. Crack initiation and growth happened at two locations of the flanges and crack growth was monitored till the crack length was half of the flange width of the main girder. To end the experiments a static load test showed the remaining capacity of the damaged structure. A conventional sensor system was used for validation and comparison with calculated predictions based on linear fracture mechanics, used for the design of the experiments. An overview over the whole set of experiments as well as (intermediate) results and findings on the applicability of the novel sensor systems are presented in this paper. The experiments are a collaborative work of multiple scientific and industrial partners and have been carried out as part of Area 3.1 of the COMET Project Rail4Future, funded by the Austrian Research Promotion Agency FFG.

Impact Identification Method for Structural Health Monitoring of Stiffened Composite Panels using Passive Sensing Systems

Identification of impact and its resulting damage is a complex inverse inferential procedure. Estimating the magnitude of an impact from sensor data is challenging and still needs to be fully understood. Furthermore, only a few studies have solved practical impact identification problems relevant to real-world structures with different geometries and structural inhomogeneities (e.g. variable thicknesses and stiffeners). Accordingly, the current research presents an impact categorisation analysis using passive measurements from piezoelectric (PZT) sensors for a geometrically complex composite aircraft part. Intermediate-mass impact tests were performed on a stiffened thermoplastic composite plate to generate signals representing various impact scenarios that may occur during the lifetime of an aircraft. The impacts were applied at critical locations, including the mid-bay, on top of the stringer, and at different thicknesses. This study investigated the effect of structural features on wave propagation in composite structures. The experimental results demonstrate that these features significantly change the wave field and, consequently, affect sensor readings. To differentiate between pristine and damaged states, an impact signal categorisation approach was employed. This analysis shows that a passive sensing system can categorise the impact using the transmitted energy from a time-domain signal. However, the spectral energy values showed low efficiency in categorising impacts. Overall, the results highlight the importance of considering structural features when deploying sensor systems for structural health monitoring and provide valuable insights into the effectiveness of impact identification and damage detection in realistic composite structures.

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier Detection.

In recent years, smart water sensing technology has played a crucial role in water management, addressing the pressing need for efficient monitoring and control of water resources analysis. The challenge in smart water sensing technology resides in ensuring the reliability and accuracy of the data collected by sensors. Outliers are a well-known problem in smart sensing as they can negatively affect the viability of useful analysis and make it difficult to evaluate pertinent data. In this study, we evaluate the performance of four sensors: electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH. We implement four classical machine learning models: support vector machine (SVM), artifical neural network (ANN), decision tree (DT), and isolated forest (iForest)-based outlier detection as a pre-processing step before visualizing the data. The dataset was collected by a real-time smart water sensing monitoring system installed in Brussels' lakes, rivers, and ponds. The obtained results clearly show that the SVM outperforms the other models, showing 98.38% F1-score rates for pH, 96.98% F1-score rates for temp, 97.88% F1-score rates for DO, and 98.11% F1-score rates for EC. Furthermore, ANN also achieves a significant results, establishing it as a viable alternative.

An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers

The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R2) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears ( ~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring.

SMS based reservoir condition monitoring sensor system for pumped hydro storage in mini-grids

Solar mini-grids are a cost-effective and efficient method of improving energy access in sub-Saharan Africa, particularly in low-income communities. However, conventional battery storage systems have reduced the affordability of solar mini-grid-generated electricity, necessitating the development of alternative energy storage systems such as pumped hydro storage to replace lithium-ion batteries. This paper introduces a model for an SMS-based fluid parameter monitoring system to provide visibility of reservoir conditions during peak and off-peak periods. The model uses hardware such as voltage regulators, sensors, microcontrollers, and C- programming language software. Sensors monitor various parameters in the reservoir and penstock, transmitting signals to a microcontroller for analysis. The results are relayed to a display unit and personnel's mobile phones for alert notifications, ensuring precise and accurate reporting. Simulation results show the reliability of the design to monitor water levels, water temperature, flow rate, and transmission through SMS to mobile devices for timely reporting. The paper recommends the development and commercialization of this model for larger applications, such as monitoring dams for irrigation and power generation.

Implanted potential sensing separator enables smart battery internal state monitor and safety alert

The current battery management system is limited to testing external characteristics, leaving the battery's internal status as a “black box”. Advanced characterization techniques and battery sensing technologies are needed to assess the battery's internal state. However, due to their short lifespan, low sensitivity, invasive nature, and high cost, these technologies face challenges in practical applications and commercialization. Here, we propose a smart battery implanted with a potential sensor for in-situ measurement of anode potential, enabling the recognition of severe side reactions and abnormal Li plating behavior. Specifically, the potential sensing material is directly integrated into the battery separator, which provides a reliable potential reference and serves as a sensing terminal. The porous structure of the separator facilitates lithium-ion transport while simultaneously enabling high-accuracy monitoring with non-destructive implantation. Additionally, the potential sensing separator can detect pre-existing or latent defects in the battery at an early stage, which are difficult to discern from the battery's external characteristics in a timely manner. Furthermore, we have developed a multi-point potential sensor monitoring system that can not only monitor the distribution of anode potential but also pinpoint the location of battery defects.

A Self-Powered Wireless Sensor System (SP-WSS) for Real-Time Propulsion Shaft Monitoring

A Self-Powered Wireless Sensor System (SP-WSS) for Real-Time Propulsion Shaft Monitoring