Smart Sensor Systems Research Articles

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.

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.

Highly sensitive colorimetric detection of ascorbic acid using molecularly imprinted photonic crystal hydrogel sensor

Molecular imprinted photonic crystal hydrogel (MICPH) sensors have gained recognition as an efficient platform for selectively detecting analyte molecules. In the present work, we report the development and implementation of a colorimetric sensor based on MIPCH for the selective detection of Ascorbic acid (AA). The fabrication process of the MIPCH sensor involves the photo-polymerization of a hydrogel precursor solution containing AA molecules, which is encapsulated in the interstitial spaces of photonic crystal (PC) opal film. Following the encapsulation, these molecules are selectively removed from the hydrogel network, forming an AA-MIPCH sensor. The sensor exhibits a vivid structural color that redshifts upon rebinding with different concentrations of AA molecules. This alteration in structural coloration serves as a straightforward and visually discernible indicator of the sensor response and enables the quantitative measurement of the target molecule. The sensor exhibits remarkable sensitivity, featuring a low limit of detection (LoD) of 0.011 µM with high selectivity, minimal response time, and promising long-term stability. In addition, the sensor demonstrates its capability to detect the AA even from the complex matrix. A CNN-based deep learning algorithm in MATLAB® was employed to quantitatively predict the concentration of the target molecule. The integration with the machine learning algorithm yields a highly efficient and accurate smart sensor system that is well-suited for real-time sensing of ascorbic acid.

Application of digital transformation in real-time bridge monitoring systems: a case study with GNSS, accelerometer and IoT solution

Abstract Digital transformation is a term that has recently become more popular in all industries such as economics, financial fields, and engineering. The development of the Internet of Things, smart sensor systems, and cloud computing is the basis for forming effective solutions in construction monitoring from cyclic monitoring and post-processing to real-time monitoring and processing; from physical storage systems to virtual systems, and connecting every component in the project. This article presents a study and application of advanced devices including GNSS, acceleration sensors and IoT solutions in bridge construction monitoring. The research aims to apply smart devices and IoT to build real-time monitoring modules for bridge projects following the digital transformation trend. The study uses a GNSS N3 Comnav receiver with a sampling interval of 1Hz, an MPU 6050 acceleration sensor with a sampling frequency of 250 Hz, and an ESP Wi-Fi module. The result of the research is a real-time wireless monitoring system that operates effectively and economically and initially processes data with GNSS and high-frequency acceleration sensors.

Inkjet-Printed, High-Performance MoS2 Transistors and Unipolar Logic Electronics.

Two-dimensional (2D) semiconductor field-effect film transistors combine large carrier mobility with mechanical flexibility and therefore can be ideally suitable for wearable electronics or at the sensor interfaces of smart sensor systems. However, such applications require large-area solution processing as opposed to single-flake devices, where the critical challenge to overcome is the high interflake resistance values. In this report, using a narrow-channel, near-vertical transport device architecture, we have fabricated inkjet-printed sub-20 nm channel electrolyte-gated transistors with predominantly intraflake carrier transport. Therefore, the electronics transport in these transistors is not dominated by the high interflake resistance, and the intraflake material properties including doping density, defect concentration, contact resistance, and threshold voltage modulation can be examined and optimized independently to achieve a current density as high as 280 μA·μm-1. In addition, through the passivation of the sulfur vacancies with a tailored surface treatment, we demonstrate an impressive On-Off current ratio exceeding 1 × 107, complemented by a low subthreshold swing of 100 mV·decade-1. Next, exploiting these high-performance transistors, unipolar depletion-load-type inverters have been fabricated that show a maximum gain of 31. Furthermore, we have realized NAND, NOR, and OR gates, demonstrating their seamless operation at a frequency of 1 kHz. Therefore, this work represents an important step forward to realize electronic circuits based on printed 2D thin film transistors.

Amplification-free and label-free rapid detection of Staphylococcus aureus using solution-gated graphene transistor-based DNA biosensor with hybridization enhancement by interface engineering

Staphylococcus aureus (S. aureus) is one of the leading causes of foodborne illnesses worldwide, posing a significant risk to food quality and safety. However, conventional detection methods often require complex amplification and labeling procedures, which are time consuming and expensive. In this study, an extremely sensitive electrochemical biosensor using a solution-gated graphene transistor (SGGT) was constructed for the amplification-free and label-free rapid detection of S. aureus by utilizing specific single-stranded DNA (ssDNA) to modify the gold (Au) sensing gate. This modified ssDNA was designed to hybridize with the target S. aureus DNA sequence. Interface engineering of the ssDNA-modified sensing gate via optimization of specific target and probe sequences was proposed to improve its hybridization efficiency. This enabled the sensitive quantitative detection of S. aureus DNA. The resulting biosensor exhibited excellent specificity in distinguishing non-complementary, 3-base/9-base DNA mismatch targets of S. aureus DNA and the specific DNA sequences of other common pathogenic bacteria. It also enabled the rapid detection of extracted target DNA sequences in solution (within 27 min) and had a low limit of detection (LoD) of 10−17 M over a wide linear detection range (10−17–10−8 M). Furthermore, the newly-developed biosensor facilitated the real-time and on-site detection of the S. aureus genome (ATCC 6538) with LoD of 10−17 M (103 CFU/mL) in a portable smart-sensing system. This suggests a promising future for the development of rapid, label-free, and amplification-free detection of foodborne illnesses using low-cost, highly sensitive SGGT DNA biosensors with appropriate functionalization and interface engineering of the sensing electrode.

A Flexible Smart Sensor System Based on Hybrid Integration of Organic and Metal-Oxide Transistors

A Flexible Smart Sensor System Based on Hybrid Integration of Organic and Metal-Oxide Transistors

Development of an energy-autonomous eddy current sensor system for in-situ component monitoring

Energy-autonomous non-destructive testing (NDT) systems provide new opportunities for structural health monitoring. An NDT system that is energy-autonomous and transmits the recorded data wirelessly can be used in places that are not easily accessible for inspection. In addition, it can provide for continuous monitoring, which can improve the operational safety of various components and structures. This study presents a concept that enables energy-autonomous and wireless structural health monitoring using a splined shaft as a demonstrator. To monitor mechanical overloads, a material sensor is applied to critical areas of these components by a laser heat treatment. The structural change that occurs as a result of an overload can be monitored by the developed smart sensor system. This system consists of a material sensor, a low-cost eddy current sensor, an energy-efficient evaluation unit with an ultra-low-power microcontroller unit (MCU) and a Long-Range Wide Area Network (LoRaWAN) data transmission unit. Wireless data transmission via LoRaWAN provides access to the Industrial Internet of Things network, enabling the collection of NDT data from multiple smart sensors in a cloud and the processing of these data to assess the condition of the component from any location. It is shown that the MCU-based eddy current testing system is able to generate a similar signal quality for material characterisation compared to an industrial eddy current testing system, despite a much more compact design and a significantly higher energy efficiency. The energy autonomy of the smart sensor system is achieved by an energy harvesting system integrated into the splined shaft. In addition, three different testing scenarios are presented, which differ mainly in the scope of data processing and evaluation on the MCU, and thus also in the energy required.

IoT-based Design and Implementation of an Intelligent Water Quality Monitoring System

Abstract: Water is an essential requirement for human survival, and a scarcity of water renders us unable to sustain our lives. The issue of water pollution and shortage is a pressing worldwide concern that necessitates the adaptation of water resources and the establishment of guiding principles at both the international and individual levels. Water contamination is the primary contributor to global disease outbreaks. In the past, the process of monitoring water quality relied on the physical collection of data, lacking the implementation of a smart sensor system. This resulted in inefficiencies, particularly in the areas of real-time data retrieval, device surveillance, and less precise data gathering. The integration of a real-time water quality monitoring method is important in order to guarantee the provision of a secure drinking water supply. Therefore, the concept of the Internet of Things (IoT), which facilitates the interconnection of diverse devices for the purpose of data interchange and collection, has emerged

A miniaturized array microneedle tactile sensor for intelligent object recognition

Tactile sensors can provide intuitive information about the shape, size, and texture of objects, which helps robots make more accurate decisions. In this paper, based on the principle of triboelectrification and electrostatic induction, a miniature tactile sensor (MTS) for smart object recognition is designed using composite materials based on silica gel. Its diameter is only 6 mm, and the electrodes are arranged in a circumferential array, which has excellent shape adaptability and can recognize curved materials with curvature of 1/15 mm−1. By optimizing the sensor structure and process to obtain a microstructure with a spike-like shape, the sensitivity of the sensor is successfully improved, and the smallest and accurately detected surface material side length is 5 mm. In addition, the article provides a smart sensor system that can enhance the recognition function of robots with the help of machine learning technology, and the discrimination accuracy reaches 95.7%, which lays a solid foundation for practical applications.

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

A leap into the future: Towards an augmented reality learning environment in ski-jumping

Introduction
 Professional sports are fiercely competitive. In ski jumping, for example, even small changes in take-off and flight can make a decisive difference between victory and defeat (Elfmark et al., 2022). Within the short time of a jump, athletes must learn to solve complex motor control problems while being exposed to harsh environmental conditions, e.g., wind, snow, and low temperatures. The actual take-off occurs within the blink of an eye (~300 ms) and an aerodynamically favourable and stable flight position should be attained immediately. Fine control of the centre of gravity in the in-run favours high speeds to generate optimum momentum during take-off (Müller, 2008). In flight, athletes can voluntarily influence aerodynamics by changing their body position. However, non-optimal flight positions occur unintentionally or due to incorrect behaviour. Furthermore, as a non-cyclical sport, ski jumping suffers from low repetition rates, which impairs the effectiveness of training. Thus, increasing the learning rate for each jump is a key success factor. Biofeedback methods have been shown to accelerate motor learning in athletes (Mulder & Hulstijn, 1985). Current sensor technologies in ski jumping do not meet the requirements for a truly wearable system, which must be energy-efficient, unobtrusive and barely noticeable (so as not to interfere with natural movement behaviour and jumping technique) and, in particular, must be equipped with a wireless link (for real-time data analysis, e.g. on the trainer tower; Schulthess et al., 2023).
 Methods
 The proposed system consists of two multi-sensor nodes: One node is hidden in a modified ski jumping boot, integrating three force-sensing resistor sensors to measure the pressure distribution on the foot soles of ski jumpers. The second sensor node is located in the ski goggles and contains RGB LEDs that provide visual biofeedback in the peripheral vision.
 Results
 We have calculated the total power consumption of our systems to be 2.52 mW, meeting requirements for multi-day operation between battery recharges. Our on-device body position classification model achieves an accuracy of 92.7% in recognising body positions from data recorded in the laboratory.
 Discussion/Conclusion
 This is the first truly wearable training system in ski jumping, offering professional athletes a new augmented experience, aimed at accelerating motor learning. In addition, the real-time data transmission of biomechanically relevant characteristics facilitates the work of the training team and could in the future enable more informative and entertaining television broadcasts.
 References
 Elfmark, O., Ettema, G., & Gilgien, M. (2022). Assessment of the steady glide phase in ski jumping. Journal of Biomechanics, 139, 111139. https://doi.org/10.1016/j.jbiomech.2022.111139
 Mulder, T., & Hulstijn, W. (1985). Sensory feedback in the learning of a novel motor task. Journal of Motor Behavior, 17(1), 110–128. https://doi.org/10.1080/00222895.1985.10735340
 Müller, W. (2008). Performance factors in ski jumping. In H. Nørstrud (Ed.), Sport Aerodynamics (pp. 139– 160). Springer. https://doi.org/10.1007/978-3-211-89297-8_8
 Schulthess, L., Ingolfsson, T. M., Nölke, M., Magno, M., Benini, L., & Leitner, C. (2023). Skilog: A smart sensor system for performance analysis and biofeedback in ski jumping. https://doi.org/10.48550/arXiv.2309.14455

Smart and Selective Gas Sensor System Empowered With Machine Learning Over IoT Platform

Smart and Selective Gas Sensor System Empowered With Machine Learning Over IoT Platform

Entropy and Memory Aware Active Transfer Learning in Smart Sensing Systems

Entropy and Memory Aware Active Transfer Learning in Smart Sensing Systems

Review on Health And Quality of Life of Elderly People: A Meta-Analysis from The Medical and Health Sciences

In response to the increasing aging population, various initiatives aim to enhance the well-being of the elderly by addressing factors like physical and mental health, comorbid conditions, isolation, and relationships. Information and Communication Technology (ICT) systems have been developed to allow seniors to maintain independence, live comfortably at home, and enjoy better overall health. Internet of Things (IoT) systems, in particular, offer continuous remote surveillance, increased social engagement, improved sensory experiences, and support for an active lifestyle. This study proposes a framework for a unified IoT-based Smart Sensor system (IoT-SS) designed to discreetly observe and assist the elderly, complementing the care provided by trained medical professionals. The quality of life is crucial for the elderly, motivating them to stay resilient. The article introduces IoT and its application in improving seniors' health and quality of life in their homes and communities. This review is valuable for medical system developers and programmers working on technology-supported healthcare plans to serve and enhance the lives of the elderly in the future.

Monitoring soil elements for irrigation management using Internet of Things (IoT) sensors

AbstractSmart farming focuses on three crucial areas: soil quality, weather conditions, and crop health. Because of the significant impact of weather and irrigation factors, many smart farming solutions are tightly integrated with smart environment and smart water (pollution, turbidity, nutrients) systems for a holistic approach. The Internet of Things (IoT) is revolutionizing soil monitoring, giving farmers and growers the means to optimize yields, minimize disease risks, and rationalize resource use. Thanks to IoT sensors, it is possible to measure various parameters such as soil temperature, NPK (nitrogen, phosphorus, potassium) levels, and soil moisture. The information collected by these IoT sensors is then transmitted to a central point or stored in the cloud server for in‐depth analysis, visual representation, and trend identification. The application of the IoT in agriculture is widely referred to as smart farming. The IoT is an essential pillar of precision agriculture, enabling more targeted and efficient management of resources. Smart sensor systems provide more information about water and crop water requirements. This information can be used to mechanically manage the water supply system and help farmers optimize their irrigation system. The information acquired by the first sensors is transferred to the cloud server. This article proposes and evaluates the concept of remote sensing systems. Three sites were selected to test the IoT system. At each site, five sensors were employed, specifically designed for soil and irrigation water analysis. The sensors dedicated to soil pH, moisture, and NPK demonstrated commendable accuracy; equally, the irrigation water pH and turbidity sensors exhibited notably precise measurements. The results were noteworthy, with an R2 value surpassing 90% across all sensor measurements. These IoT sensors could facilitate effective crop monitoring without excessive expenditures and serve to safeguard groundwater from nutrient contamination.

Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning

Accurate and selective detection of target gas/volatile organic compounds (VOCs) is of utmost importance. The chemiresistive gas sensors have been a desirable candidate due to their compact footprint and ease of fabrication, but they show poor selectivity. This work presents a combination of nanomaterials-based chemiresistive gas sensors with machine learning (ML) techniques to achieve sensitive, selective, and quantified detection of tested VOCs. The sensor array consists of four separate sensing layers over interdigitated electrodes-based platform. The sensing materials were comprised of silver, gold, palladium, and platinum nanoparticles decorated on tin oxide hollow-sphere structures which were successfully synthesized through chemical routes and characterized accordingly. Surface decoration of different metal nanoparticles has produced sensitive and diverse sensing patterns among the tested VOCs. The sensing mechanism and related gas sensing kinetics were then analyzed to explain high sensitivity and diverse sensing phenomena. The subsequent incorporation of ML models has resulted in qualitative and quantitative detection of VOCs. A comparative analysis was carried out among different types of applied features and ML models with reasoning. Particularly, a deep neural network (DNN) model with time series (TS) response sequence as input information, delivered the best performance. The DNN_TS model presented an average classification accuracy of 98.33 %, in conjunction with excellent concentration prediction. The DNN_TS model showed a very fast prediction time of 2.74 µs with adaptive learning while utilizing minimum computing resources, which favors the real-time sensing capability. The reported results promote the development of an autonomous, smart, and selective gas sensor system for real-time applications.

Temperature Sensors Integrated with an Electrochromic Readout toward Visual Detection.

Temperature sensors have attracted great attention for personal health care and disease diagnosis in recent years. However, it is still a great challenge to fabricate reliable and highly sensitive temperature sensors that can convert physiological signals into easily readable signals in a convenient way. Herein, an integrated smart temperature sensor system based on a traditional temperature sensor and electrochromic display is proposed for real-time visual detection of temperature. Significantly, a voltage-regulated electrochromic device (ECD) based on tungsten oxide (WO3) and polyaniline (PANI) as the real-time visualization window was integrated into the platform to provide feedback on the temperature change. The ECD would change its color from green to blue based on the electrical signal of the temperature sensor, resulting in a visualized readout that can be monitored through our naked eye. Additionally, the smart temperature sensor system possesses an extremely durable property and cycle stability, remaining around 90% of the initial value even after 15,000 s continuous cycle. Thus, the novel design and low power consumption advantages make it a good candidate to pave the way for developing interactive wearable electronics and intelligent robots as real-time temperature feedback systems.

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

AbstractImage analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants.

Comparison Research on Characterization and Evaluation Approaches for Paint Coated Corrosion Using Eddy Current Pulsed Thermography.

Paint coated corrosion detection and evaluation is a big challenge for steel performance and structure health. Eddy current pulsed thermography (ECPT) technique is investigated because it can reflect the corrosion physical properties through paint coating by the infrared signal. This paper proposes skewness method, which presents the feature of temperature curve's shape automatically, and compares it with principal component analysis (PCA), phase analysis, and kurtosis feature extraction methods for paint coated corrosion characterization and evaluation. The averaged skewness shows the best sensitivity for 0-6 months corrosion. The normalized second principal component (PC) presents good sensitivity and the best measurement scale for corroded time. Furthermore, the temperature curve analysis proves that the electrical conductivity dominates the induced heating and heat distribution. The corrosion height is utilized to explain why ECPT technique is valid within 10 months corroded time. ECPT technique is proved as a smart sensor system for paint coated corrosion detection and characterization.