Real-time Parameters Research Articles

A parameter-interactive digital twin model of bearings under variable speeds

The digital twin model plays a crucial role in the Prognostics and Health Management of mechanical systems. However, current digital twin models for bearings lack comprehensive research on the interaction between the virtual entity and the physical entity under variable speeds. Accordingly, a parameter-interactive digital twin model has been proposed to interact the information of bearings between virtual entity and physical entity in real-time under variable speeds. Firstly, a bearing dynamics model considering speed, stiffness and fault size was established under variable speeds. Secondly, speed parameter was updated in accordance with the ridgeline of the power spectrum. Stiffness and fault size parameters were updated through an iterative extended Kalman filtering process. Condition of the bearing was monitored through various parameters. The accuracy of the dynamic model and the feasibility of parameter interaction were validated through experimental studies. These findings demonstrate that the proposed method can effectively ensure real-time parameter interaction and condition monitoring under variable speeds within the digital twin model.

Integrating Python and MATLAB for Digital Twin Applications in Aeroservoelasticity

The development of Digital Twin (DT) applications in intricate engineering areas has been greatly accelerated by the incorporation of sophisticated computational tools like Python and MATLAB. The goal of this research is to model, simulate, and analyze aeroservoelastic systems for Digital Twin implementations by utilizing the combined powers of Python and MATLAB. In order to achieve the real-time predictive skills needed for Digital Twins, aeroservoelasticity—which involves the coupled interplay of aerodynamic, structural, and control forces—presents special obstacles. This work successfully tackles these issues by fusing MATLAB's strong numerical and control system capabilities with Python's adaptability and library ecosystem. Using real-time sensor data streaming, and feedback control system implementation is used in this work. High-fidelity state-space modeling, sophisticated control design (such as PID and state feedback controllers), and system dynamics visualization are all done concurrently with MATLAB. In order to simulate and stabilize a highly flexible aeroelastic system, a continuous feedback loop was modeled in both environments, highlighting the platforms' complimentary advantages. Through the MATLAB Engine API for Python, the integrated method facilitates smooth data transmission between Python and MATLAB, guaranteeing real-time communication and model parameter synchronization. The efficiency of this dual-platform system is demonstrated by case studies on wing flutter suppression, limit cycle oscillation (LCO) mitigation, and control input optimization. The method is well suited for the dynamic needs of aeroservoelastic Digital Twins because of the results, which demonstrate a shorter error convergence time, improved stability, and computational economy. By utilizing MATLAB and Python's interoperability, this work establishes the groundwork for scalable, real-time Digital Twin solutions in aeronautical engineering. It highlights how crucial hybrid computational ecosystems are for bridging the gap between high- fidelity simulations and real-time predictive capabilities, opening the door for improvements in fault prediction, control optimization, and aeronautical system monitoring.

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis.

Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

Research on a New Multifunctional Cell Sample Automatic Culture Device for Use in the Chinese Space Station

In order to meet the needs of scientific research in space medicine and biology, a new multifunctional automated cell sample culture device for a Chinese space station has been designed. The temperature and carbon dioxide concentration are adjustable, making it convenient for cell culture in microgravity environments of the space station. A centrifuge is used to simulate the microgravity environment, allowing for synchronous gravity and microgravity comparison during cell culture. An automated focusing visible light microscope has been designed, capable of real-time photography of cultured cells, which can receive ground commands to complete automatic focusing and image transmission. The thermal design of the cell sample culture device uses an air heating method, and the rationality of the thermal control measures has been verified through thermal simulation analysis. The designed cell sample preparation device can monitor and display the cell growth environment parameters and device performance parameters in real time on orbit. It can also control the internal temperature within the temperature range required for cell culture. Thus, it can meet the urgent needs of various cell cultures, experiments, and scientific research on a Chinese space station.

Prediction of Tuber Damage from Harvesting and Processing Machine Working Units Based on the Recording of Impact Parameters

The paper presents the development of an empirical mathematical model of the potato tuber damage index that considers the relationship between impact parameters, such as peak acceleration, velocity change, and the experimental coefficient. This coefficient was developed using statistical analysis methods for four isolated surfaces and two potato varieties, Hermes and Saturna. The IRD 400 device was used to measure impacts, which recorded peak accelerations and changes in impact velocity at initial velocities in the 2.43–4.43 m·s−1 range. The study’s results indicate that the variety, type of surface, and initial impact velocity had a statistically significant effect on the tuber damage index; the type of surface and initial impact velocity had a statistically significant effect on the peak acceleration values (p < 0.05). The increase in peak acceleration with increases in the impact velocity confirms the hypothesis that the maximum force and the resulting internal stresses of the tuber are key elements causing damage due to the impact. The highest values of the tuber damage index occurred during impacts with steel surfaces and conveyor bars. The developed model allows for the faster prediction of potential damage in harvesting and post-harvest processing conditions than traditional measurement methods. To fully use the proposed model in Agriculture 4.0, further research should be conducted to improve recording devices for the measurement of impact parameters in real time.

Particle Swarm Optimization–Long Short-Term Memory-Based Dynamic Prediction Model of Single-Crystal Furnace Temperature and Heating Power

Precise temperature and heating power control are crucial for crystal quality and production efficiency in the Czochralski single-crystal growth process. Existing sensor technologies can only monitor these parameters in real time, lacking the ability to predict future trends, which limits the ability to implement preventive control before issues arise. To address this, a temperature and heating power prediction model based on Long Short-Term Memory (LSTM) is proposed and developed using extensive production data. Spearman’s rank correlation coefficient is applied to identify the key parameters related to temperature and heating power. Hyperparameter optimization uses Particle Swarm Optimization (PSO) to improve prediction accuracy. The performance of the PSO-LSTM model is compared with two other widely used prediction models, demonstrating its superior predictive capability. The results show that the PSO-LSTM model achieves highly accurate temperature and heating power predictions in the crystal growth process, with a Mean Absolute Error (MAE) of 0.0295 for temperature and 0.0392 for heating power, further validating its effectiveness for real-time predictive control.

Real-Time Analysis of Industrial Data Using the Unsupervised Hierarchical Density-Based Spatial Clustering of Applications with Noise Method in Monitoring the Welding Process in a Robotic Cell

The application of modern machine learning methods in industrial settings is a relatively new challenge and remains in the early stages of development. Current computational power enables the processing of vast numbers of production parameters in real time. This article presents a practical analysis of the welding process in a robotic cell using the unsupervised HDBSCAN machine learning algorithm, highlighting its advantages over the classical k-means algorithm. This paper also addresses the problem of predicting and monitoring undesirable situations and proposes the use of the real-time graphical representation of noisy data as a particularly effective solution for managing such issues.

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications

Optimization and sensitivity analysis for developing a real-time non-contact physiological parameters measurement and monitoring system using IPPG signal for biomedical applications

Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network.

Aeromagnetic surveying technology detects minute variations in Earth's magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (UAVs) offer increased flexibility in complex terrains. However, measurement accuracy and reliability are adversely affected by environmental and sensor noise, including Barkhausen noise. Therefore, we proposed a novel denoising method that integrates Particle Swarm Optimization (PSO) with Wavelet Neural Networks, enhanced by a dynamic compression factor and an adaptive adjustment strategy. This approach leverages PSO to fine-tune the Wavelet Neural Network parameters in real time, significantly improving denoising performance and computational efficiency. Experimental results indicate that, compared to conventional wavelet transform methods, this approach reduces time difference fluctuation by 23.26%, enhances the signal-to-noise ratio (SNR) by 0.46%, and improves sensor precision and stability. This novel approach to processing RTD fluxgate sensor signals not only strengthens noise suppression and measurement accuracy but also holds significant potential for improving UAV-based geological surveying and environmental monitoring in challenging terrains.

Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency

Whether a processor can meet the real-time requirements of a system is a crucial factor affecting the security of real-time systems. Currently, the methods for evaluating hardware real-time performance and the quality of real-time performance are not comprehensive. To address these issues, a “Hardware Real-Time Parameter (Hrtp)” is proposed, which integrates the concepts of “whether it meets real-time system requirements”, “execution speed”, and “operational stability”. This parameter is used to evaluate the real-time performance of RISC-V processors before and after optimization. The processor is optimized for real-time performance using a “simplified local history branch predictor” and a “division data dependency module”. Experimental results show that the processor’s branch prediction accuracy and division calculation speed have both improved. When running the CoreMark benchmark program and the division test program, the test results improved, indicating an enhanced “real-time” performance of the hardware. The changes in the “Standalone Hardware Real-Time Performance Parameter (Hrtp)” data are consistent with theoretical analysis, and it can meet the evaluation needs for “hardware real-time performance”.

Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks

The promising outcomes of dynamical system identification techniques, such as SINDy (Brunton et al. in Proc Natl Acad Sci 113(15):3932–3937, 2016), highlight their advantages in providing qualitative interpretability and extrapolation compared to non-interpretable deep neural networks (Rudin in Nat Mach Intell 1(5):206–215, 2019). These techniques suffer from parameter updating in real-time use cases, especially when the system parameters are likely to change during or between processes. Recently, the OASIS (Bhadriraju et al. in AIChE J 66(11):16980, 2020) framework introduced a data-driven technique to address the limitations of real-time dynamical system parameters updating, yielding interesting results. Nevertheless, we show in this work that superior performance can be achieved using more advanced model architectures. We present an innovative encoding approach, based mainly on the use of Set Encoding methods of sequence data, which give accurate adaptive model identification for complex dynamic systems, with variable input time series length. Two Set Encoding methods are used: the first is Deep Set (Zaheer et al. in Adv Neural Inf Process Syst 30, 2017), and the second is Set Transformer (Lee et al. in: International conference on machine learning, PMLR, pp 3744–3753 2019). Comparing Set Transformer to OASIS framework on Lotka–Volterra for real-time local dynamical system identification and time series forecasting, we find that the Set Transformer architecture is well adapted to learning relationships within data sets. We then compare the two Set Encoding methods based on the Lorenz system for online global dynamical system identification. Finally, we trained a Deep Set model to perform identification and characterization of abnormalities for 1D heat-transfer problem.

An Internet of Things—Supervisory Control and Data Acquisition (IoT-SCADA) Architecture for Photovoltaic System Monitoring, Control, and Inspection in Real Time

The Internet of Things (IoT) serves as a key component to enhance operational efficiency and decision-making in the context of supervisory control and data acquisition (SCADA) systems. Featuring the improved system robustness and real-time parameters, including images of the load, a new design of SCADA system monitoring for a photovoltaic (PV) system based on dual IoT platforms is proposed in this paper. Two voltage sensors collect the voltages of the PV module and the battery, while three current sensors accumulate the current data from the PV module, the battery, and the load. ESP32-E assembles the data and then transmits them to the Arduino Cloud via MQTT for real-time display and ESP32-S3 via HTTP. The relay and the load are controlled by ESP32-E to turn ON/OFF based on the battery voltage as well. In addition, ESP32-S3 forwards the received data to ThingSpeak for advanced analysis, data storage, and real-time display via HTTP. The load images are also displayed on a camera web server built by ESP32-S3. Successfully monitoring for over 20 days, the proposed system demonstrated its robustness and versatility even during the downtime of the Arduino Cloud, with a one-day voltage measurement ranging to a maximum of 13 V and current ranging from zero amperes to 4.42 amperes. To add to this system, it incorporates visual load monitoring features, which are unseen in traditional systems.

ESTIMASI TEGANGAN OPEN CIRCUIT DAN RESISTANSI INTERNAL BATERAI MENGGUNAKAN ALGORITMA RECURSIVE LEAST SQUARES

This study employs the Recursive Least Squares (RLS) algorithm to estimate the Open Circuit Voltage (OCV) and internal resistance (Ro) of a battery in real-time, based on measured current and voltage data. The results demonstrate that RLS can produce accurate estimates of OCV and Ro, with a Mean Squared Error (MSE) of 0.031, indicating a very small difference between the predicted and measured terminal voltage. The estimated OCV is stable, while Ro remains consistently within the range of 0.075 to 0.175 ohms, despite fluctuations in voltage due to changes in current. These findings show that the RLS algorithm can be effectively applied in battery management systems to dynamically and in real-time estimate parameters, which is crucial for applications such as electric vehicle battery monitoring and energy storage systems.

Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm

With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT.

USE OF GEO-INFORMATION SYSTEMS FOR GROUNDWATER MONITORING

Abstract . The article is devoted to the study of the possibilities of using geographic information systems (GIS) for monitoring the state of groundwater, which is important for ensuring environmental safety and sustainable management of water resources. Groundwater is an important source of fresh water for the population, industry and agriculture, but it is vulnerable to various pollutions caused by both natural and anthropogenic factors. Therefore, the implementation of reliable monitoring systems that allow timely detection, assessment and forecasting of changes in water quality is necessary to prevent the deterioration of the ecological situation and prevent the spread of pollution. Geographic information systems (GIS ) offer significant opportunities for the collection and integration of large volumes of spatial data, including satellite images, remote sensing results, automated sensor data, and field observations. GIS allow creating multi-layered maps reflecting the ecological state of underground aquifers, modeling pollution processes and predicting their possible impact on nearby ecosystems and water sources. Thanks to the use of GIS, it is possible to combine data from different sources in a single interactive system, which simplifies analysis and provides the ability to quickly respond to any detected deviations. The article examines in detail the key methods used within GIS for groundwater monitoring, including satellite sounding to analyze changes in the earth's surface, spectral indices to assess the state of vegetation and soil salinity, and radar methods to determine soil electrical conductivity. Particular attention is paid to the application of three-dimensional modeling, which allows visualization of the spread of pollution in underground aquifers, which facilitates decision-making regarding the optimal location of monitoring stations and the design of protective structures. In combination with automated sensors recording water parameters in real time, GIS provides continuous monitoring of the physico-chemical indicators of groundwater and allows for prompt assessment of their condition. The article also analyzes the possibilities of using predictive modeling in GIS to assess the further spread of pollution and develop environmental risk management measures. It is described how mathematical models in GIS can help predict the impact of anthropogenic and natural factors on groundwater, which ensures the development of scientifically based solutions to reduce pollution and maintain water quality at a safe level. The implementation of GIS in groundwater monitoring is an important step towards increasing the efficiency of water resources management, ensuring their protection from pollution and preserving the ecological stability of the regions.

AI‐Driven Approach for Sustainable Extraction of Earth's Subsurface Renewable Energy While Minimizing Seismic Activity

ABSTRACTDeep geothermal energy, carbon capture and storage, and hydrogen storage hold considerable promise for meeting the energy sector's large‐scale requirements and reducing emissions. However, the injection of fluids into the Earth's crust, essential for these activities, can induce or trigger earthquakes. In this paper, we highlight a new approach based on reinforcement learning (RL) for the control of human‐induced seismicity in the highly complex environment of an underground reservoir. This complex system poses significant challenges in the control design due to parameter uncertainties and unmodeled dynamics. We show that the RL algorithm can interact efficiently with a robust controller, by choosing the controller parameters in real time, reducing human‐induced seismicity, and allowing the consideration of further production objectives, for example, minimal control power. Simulations are presented for a simplified underground reservoir under various energy demand scenarios, demonstrating the reliability and effectiveness of the proposed control–RL approach.

Adaptive compensation for multi-axial real-time hybrid simulation via nonlinear parameter estimation

For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering.

Application-Level Evaluation of IEEE 802.1AS Synchronized Time and Linux for Distributed Real-Time Systems

The use of Ethernet and Linux is becoming common in industrial applications, even for those with real-time requirements, although neither of them were originally designed for this purpose. The emergence of Industry 4.0 (also known as Industrial Internet of Things, IIoT) has encouraged the evolution of these technologies to better handle real-time issues. On the one hand, Linux now supports mechanisms to configure certain real-time parameters, as well as core isolation and interrupt allocation facilities in multicore processors. On the other hand, the set of Ethernet standards IEEE 802.1 Time-Sensitive Networking (TSN) includes a high precision clock synchronization protocol (IEEE 802.1AS). The purpose of this work is to outline an execution framework for distributed systems based on TSN and Linux, which allows the execution of time-aware applications. We have studied and evaluated different configurations available for the proposed execution framework. In particular, a detailed characterization of the clock synchronization mechanism, from the application point of view, has been performed. Some conclusions about the current real-time capabilities of these technologies are also presented.

Leveraging Jasper Reports for Dynamic Financial Reporting in Modern Tolling Systems: A Technical Implementation Guide

This article presents a comprehensive technical implementation guide for leveraging Jasper Reports in modern tolling systems to enhance dynamic financial reporting capabilities. The article explores the evolution of electronic toll collection infrastructures and their integration with advanced reporting systems. It examines the core components of dynamic reporting architecture, including data flow pipelines, real-time parameter handling, and system integration points. The article delves into report customization and format optimization techniques, highlighting the importance of stakeholder-specific templates and dynamic parameter implementation. The article also investigates financial analysis capabilities through AI-driven smart information systems and big data technologies, focusing on daily ledger management and revenue tracking mechanisms. Advanced analytics features, including ranking functions and hierarchical data visualization, are analyzed for their impact on operational efficiency. The implementation of automated report scheduling and distribution frameworks is discussed, along with their security features and reliability metrics. The article concludes by examining the operational benefits achieved through these implementations, including improved decision support, stakeholder communication, and cost reduction through automation.

An On-Machine Measuring Apparatus for Dimension and Form Errors of Deep-Hole Parts.

The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth.