Sensor System Research Articles

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

Degradation assessment of wind turbine based on additional load measurements

In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Assessing the accuracy of a wireless sensor system for estimating lumbar moments during manual lifting tasks considering the effects of load weight, asymmetry, and height

This study assessed the accuracy of L5/S1 moment estimates calculated with an Inertial Motion Capture (IMC) system during an asymmetrical and variable height lifting task. The effects of load weight, asymmetry, and lifting height on estimates of lumbar moment have not been comprehensively considered in studies using IMC systems. Thirty-six participants engaged in tasks involving three loads, lifting heights, and trunk rotation angles. Lumbar moments were calculated using bottom-up and top-down biomechanical models. Gold-standard Optical Motion Capture (OMC) and Force Plates (FP) were used as the reference. A randomized block partially confounded design was used to compare the root mean square errors (RMSE) between the IMC and OMC-based reference estimates. The IMC system's estimated peak moments were 12%–13% lower than those estimated using the gold standard OMC-BU inverse dynamics, while the RMSE varied between 19 and 21 Nm. A Load*Height interaction was found; a trend was identified where the RMSE values increased as both the load and height levels increased. The angle did not show a significant effect on any of the tested scenarios. A close correspondence between the IMC and OMC-based moment estimates was established, with the load being the main factor affecting the differences between systems. The IMC system shows potential for use in occupational settings to capture data on the lumbar moments of workers, which could be utilized to assess ergonomic risk.

Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science.

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68-0.9, Uexp = 16-66%, BSU = 0.1-0.7 µg/m3) and BC (R2 = 0.82-0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38-0.55, Uexp = 111-614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75-0.83, Uexp = 37-44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

Internet of Things (IoT) Driven Water Management System for Efficient Level Control

Water is an essential and valuable resource in daily life, making its conservation crucial to prevent adverse effects. Storing water for domestic, industrial, agricultural, and other purposes is particularly important. Safe drinking water is increasingly becoming polluted due to the growing population and their demands for urbanization and industrialization. At the household level, some people leave electric water pumps running and either go to work or sleep, forgetting to turn off the pumps when the water container is full. This highlights the need for a reliable and continuous water supply. IoT plays a significant role in environmental monitoring, particularly in disaster management, early warning systems, and environmental data analytics. One major challenge in urban cities is water management, especially with the rapid growth of urbanization, necessitating sustainable urban development plans. To address these issues, we propose a "Water Level Monitoring System" solution. This paper presents an IoT-based water monitoring system for real-time applications. The system's sensors measure the water level in the tank, and the data is sent to a cloud server, allowing users to view it on a remote dashboard. This system can be used efficiently by both homeowners and industrial users, as well as other water utilities.

Design and Development of a Pipeline Maintenance System for Pipe Surge and Water Hammer Equipment Based on Siemens S7-1200 PLC

Fluids play a crucial role in the lives of living beings, as they are substances that can flow. In a piping system, a common issue is water pressure fluctuation caused by sudden closures of water flow (Idul, 2021). Pipe surge and water hammer equipment are utilized to demonstrate the transient pressure effect resulting from abrupt changes in flow velocity within pipes (David, 1981). Water hammer can lead to pressure surges throughout the piping system, potentially causing failures that result in leaks in pipes, valves, pipe joints, and even pumps if the pressure exceeds the pipe's capacity (Prasetya, 2016). The likelihood of pipe ruptures or leaks at joints increases significantly. Additionally, equipment movement, slope, and precision of pipe installation can also contribute to leaks during water hammer events. This research aims to develop a pipe leakage monitoring system to address the impact of water hammer. The system will be installed in areas deemed susceptible to this phenomenon, along with a periodic maintenance indicator system to minimize the impact of pipe leaks. The research will also investigate the potential risks associated with varying fluid flow rates in the piping system. The monitoring, indicator, and sensor systems will be integrated into a PLC and HMI system for data processing.

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology.

A hierarchical k-out-of-n optimization model for enhancing reliability of fire alarm systems

Fire alarm systems are crucial for mitigating the risks and damages associated with fires in process industries. These systems heavily depend on the performance of fire detection sensors to provide early detection and warnings, facilitating prompt evacuation and intervention measures. In order to improve the reliability of sensor systems typically requires the use of redundancy allocation techniques, such as a k-out-of-n configuration. In k-out-of-n configuration, the signals from all sensors are directed to a voter, which compares them and transmits a final signal once at least k signals are matched. However, challenges arise due to diverse failure modes with distinct impacts on the system. Integrating sensors through a single-layer k-out-of-n configuration, when there is only one voter, fails to fully highlight the potential advantages of redundancy in sensor systems. This paper addresses the limitations of the conventional configurations by introducing a hierarchical k-out-of-n system in which sensors can be integrated across multiple layers. In the k-out-of-n hierarchical system, sensors are organized into distinct groups, each employing its own k-out-of-n sub-system. Subsequently, the outputs of these sensor groups can be integrated with other groups through their respective voters in higher layers of the hierarchy. Ultimately, at the top layer, all the voters from the preceding layers must be connected to a top voter to transmit a final signal. Furthermore, an optimization model is developed to minimize the system cost while maximizing reliability, considering the best selection of sensors and the system configuration. The proposed optimization model takes into account a multi-sensor system with two competing failure modes and incorporates the probability of sensors' failure modes in the system. The hierarchical k-out-of-n system provides an opportunity to validate sensors by comparing them within their designated groups, allowing for the identification of the location of a failure and the planning of appropriate actions accordingly. The proposed optimization model is applied to a case of a fire detection system in storage warehouse to demonstrate its advantages over the conventional model.

Magnetic transfer piezoelectric wind energy harvester with dual vibration mode conversion

Wind-induced vibration piezoelectric energy harvesters have garnered significant interest in recent years as a means to power autonomous wireless sensor systems. A magnetic transfer piezoelectric wind energy harvester (MT-PWEH) is proposed and its durability, power generation performance and environmental adaptability are improved utilizing dual mode conversion. This MT-PWEH incorporated a downstream rectangular baffle, which facilitated the transition of the hollow cylinder from a traditional single vortex-induced vibration to a coupled vibration involving both vortex-induced vibration and galloping (i.e., the first vibration mode conversion). Besides, the vibration direction of the hollow cylinder was perpendicular to the vibration direction of the transducer, which achieved the purpose of limiting the amplitude (i.e., the second vibration mode conversion). The feasibility of MT-PWEH was confirmed through theoretical analysis, CFD simulation, fabrication and experiments. The experimental results demonstrated that the working characteristics of MT-PWEH were significantly affected by position and size of the baffle. Specifically, the ratio of the maximum cut-in wind speed of 12.5 m/s to the minimum cut-in wind speed of 1.2 m/s reached 10.4. Additionally, a maximum power output of 0.78 mW was recorded at 10 m/s with an optimal load resistance of 800 kΩ and 10 LEDs were drove successfully.

Disposable electrochemical aptasensor with mesoporous carbon spheres modified screen-printed dual-working electrodes for Pb2+ and Hg2+ detection

Electrochemical biosensing combined with screen-printed paper technology provides an effective strategy for the quantitative analysis of metal ions, particularly suitable for in-situ determination. In this study, a disposable, economical and user-friendly screen-printed dual-working electrodes (SPDEs) aptasensor system for the determination of lead ions (Pb2+) and mercury ions (Hg2+) was described. The sensor system consists of dual-working electrodes, a counter electrode and a reference electrode fabricated using screen printing technology. The aptasensor demonstrates high sensitivity and selectivity towards Pb2+ and Hg2+, owing to the larger specific surface area (1334.7 m2/g) and excellent conductivity of mesoporous carbon nanoparticles (MCNs). Additionally, the specific recognition ability and high affinity of G-quadruplex and T-Hg2+-T structures for Pb2+ and Hg2+, respectively, further enhance the performance. Under optimized experimental conditions, the aptasensor achieves a low limit of detection (LOD) of 0.088 ng/mL for Pb2+ and 0.0007 ng/mL for Hg2+ (S/N = 3), a wide linear range (0.1–1000.0 ng/mL for Pb2+ and 0.001–100.0 ng/mL for Hg2+), and high accuracy (relative standard deviation, RSD≤10.0 %) and selectivity. Importantly, the SPDEs aptasensor can detect Pb2+ and Hg2+ in a sample without competition and interference, achieving accuracy comparable to that of commercial inductively coupled plasma mass spectrometry (ICP-MS). These advantages make the SPDEs aptasensor highly suitable for practical applications in environmental monitoring, food safety, and clinical diagnosis.

Instability monitoring of molten pool in pure copper laser welding based on a multi-scale cascade model and spatial optical signals

During laser welding of pure copper, instabilities such as violent molten pool oscillation, large spatters, and melt ejection severely damage weld quality, which are caused by copper’s high reflectivity on commonly used infrared laser. The seriously unstable molten pool and keyhole and complicated laser-material interactions result in complex process signal emission waveforms, adding to the difficulties in process stability monitoring tasks. In this work, to break down different contents in the complex signals and deeply analyzing signal-process relation, combinative spatial optical sensor system was designed, and time-frequency signal analysis in multi-scale windows was performed. It was found that the infrared radiation at the front and end side of molten pool indicates the oscillation behavior of liquid metal surface, and the signal fluctuation patterns of visible radiation from different height of metal vapor varied when meeting severe instability like melt ejections. Signal features were extracted based on the understanding of process mechanism and signal behaviors. A cascade model combining Artificial Neural Network (ANN) and Support Vector Machine (SVM) was introduced to predict weld seam quality, where the ANN model focused on short-time stability status perception and the SVM model was used to decide macroscopic seam formation defects based on combining outputs of the ANN model in a long-term sampling window. Application results showed that the recognition accuracy of pit was 100 % and the accuracy of uneven toe reached 86.3 %. The multi-source signals of unstable molten pool recognized by the cascade model were summarized. The evolution process of copper molten pool ejection was revealed.

Addressing multidimensional highly correlated data for forecasting in precision beekeeping

In recent years, there have been relevant advances in precision beekeeping. These advances are mainly focused on proposing sensor systems that collect crucial information for bee welfare, creating integrated architectures that allow beekeepers to monitor the current state of their hive through real-time data. However, there is a lack of predictive models that would allow beekeepers to anticipate specific events that endanger bee welfare and lead to a decline in productivity. Specifically, predictive approaches accounting for the high correlation among internal variables of beehives have not been developed to date. To address this research gap, multivariate predictive models, including auto-regressive state-space and time series models, have been implemented and applied to four different hives from the we4bee project. These models aim to predict the internal variables of beehives (four different temperatures, humidity, and weight) by utilizing the meteorological conditions to which the hives are exposed. A cross-validation adapted to time series data was employed for model generalization assessment. Prediction models based on vector time series exhibited superior performance in forecasting internal hive variables compared to multivariate auto-regressive state-space models. Overall, the approach based on the vector error correction model yielded the best balance between fit, prediction, and computational cost. The VEC-based approach produces predictions with maximum mean absolute errors of 177 (312)g in weight, 3.366 (3.802)% in humidity, and 1.122 (1.685)°C in temperature at 1 (3)-days ahead when dealing with beehives exhibiting a high degree of correlation in their internal variables. Moreover, the VEC-based approach requires less than a second to perform the time series fitting process, which makes it particularly interesting for application in big data environments. The integration of such models into a decision support system would meet the need of beekeepers to anticipate potential threats to the welfare of their bee colonies, streamlining their monitoring processes while eliminating the need for continuous inspections.

Enhanced forest fire evacuation planning using real-time sensor and GPS algorithm

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng’s Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input.

Substrate-Switched Dual-Signal Self-Powered Sensing System Based on Dual-Nanozyme Activity of Bimetal-Doped CeO2 Nanospheres for Electrochemical Assay of Aflatoxin B1.

The long-term operation feature of enzymatic biofuel cell-based self-powered biosensor (EBFC-SPB) endows them with the potential to execute dual-signal biosensing without having to integrate an extra signal acquisition device. Herein, cobalt and manganese codoped CeO2 nanospheres (CoMn-CeO2 NSs) with glucose-oxidase-like and peroxidase-like activities have been developed as substrate-switched dual-channel signal transduction components in EBFC-SPB for a dual-signal assay of aflatoxin B1 (AFB1). The CoMn-CeO2 NSs modified with aptamer are anchored to a complementary DNA-attached bioanode of EBFC-SPB by base complementary pairing, which catalyze the glucose oxidation together with the glucose oxidase (GOx) on the bioanode. Once the AFB1 appears, CoMn-CeO2 NSs will be released from the bioanode due to the binding specificity of the aptamer, resulting in a decreased catalytic efficiency and the first declining stage of EBFC-SPB. Accompanied by the introduction of H2O2, the residual CoMn-CeO2 NSs on the bioanode switch to peroxidase-like activity and mediate the production of benzo-4-chlorohexadienone (4-CD) precipitate, which increases the steric hindrance and yields another declining stage of EBFC-SPB. By assessing the variation amplitudes during these two declining stages, the dual-signal assay of AFB1 has been realized with satisfying results. This work not only breaks ground in dual-signal bioassays but also deepens the application of nanozymes in EBFC-SPB.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

PCR virtual temperature sensor design based on system modeling and identification

The reaction results of Polymerase chain reaction (PCR) are highly sensitive to temperature accuracy, but the reaction liquid temperature is hard to be measured directly. In this paper, a virtual temperature sensor is creatively designed to directly obtain the reaction liquid temperature using a model-switching approach. This study first establishes the mathematical models of the temperature control system and obtains precise system models for the heating and cooling stages. Based on this, a virtual sensor is constructed, and a closed-loop fuzzy PID control system is established. Furthermore, the accuracy of the designed virtual temperature sensor is verified through simulation and experimentation. Simulation results show that the fuzzy PID control method has higher temperature accuracy, with a deviation of 0.23 °C, making it more suitable for Peltier systems, as Peltier systems suffer from inconsistent temperature rise and fall models. The experimental results show that the control method using the virtual sensor achieves higher precision in liquid temperature control, with an accuracy of ±0.21 °C. Finally, standard PCR procedures are performed for designed chip, and the results indicate that the temperature sensor and control system designed in this study exhibit significantly higher amplification efficiency, approximately 16 times greater than traditional control methods.

FiberFlex: Real-time FPGA-based Intelligent &amp; Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This paper proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing FPGA high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing, and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on 5 test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 watts of power, 33 watts in the optical DAS system, and 22 watts in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let's use fiber optic touch instead.

Establishment of DC Electric Field Measurements by Linear Dipole Type Sensor System

Nowadays, electric fields above the ground are measured mainly by so‐called ‘field mill type’ sensor systems. However, their measurements are limited to vertical field component only due to its technical constraint. Out of necessity of measurements of electric field components in arbitrary directions in any spaces, a linear dipole type DC electric field sensor system, which can be immersed within a fully uniform electric field, was developed. However, it had been remained unknown whether its sensor output is parameter‐dependent or not, and unknown whether it can provide reliable intensity value of measured electric fields or not. In order to clarify these points, test observations of natural electric fields have been conducted by means of plural sensor systems having different values of parameter. Obtained electric field intensity derived from the observed data has shown a parameter‐dependence despite that it should be constant for any parameter values. After a detailed analysis of the effect of the parameter along the equivalent electric circuit of the sensor system, a field‐calibrating formula, which can make the sensor system to be parameter‐independent, has been found. Finally, the linear dipole type DC electric field sensor system for obtaining reliable electric field intensities has been established. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multi-Sensory Tool Holder for Process Force Monitoring and Chatter Detection in Milling.

Sensor-based monitoring of process and tool condition in milling is a key technology for improving productivity and workpiece quality, as well as enabling automation of machine tools. However, industrial implementation of such monitoring systems remains a difficult task, since they require high sensitivity and minimal impact on CNC machines and cutting conditions. This paper presents a novel multi-sensory tool holder for measurement of process forces and vibrations in direct proximity to the cutting tool. In particular, the sensor system has an integrated temperature sensor, a triaxial accelerometer and strain gauges for measurement of axial force and bending moment. It is equipped with a self-sufficient electric generator and wireless data transmission, allowing for a tool holder design without interfering contours. Milling and drilling experiments with varying cutting parameters are conducted. The measurement data are analyzed, pre-processed and verified with reference signals. Furthermore, the suitability of all integrated sensors for detection of dynamic instabilities (chatter) is investigated, showing that bending moment and tangential acceleration signals are the most sensitive regarding this monitoring task.

AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples.