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Active Fault-Tolerant control to Prevent Hanger Bending During Configuration Transformation of 3D Cable System in Suspension Bridges

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APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： Bai, Yunteng Wang, Xiaoming Zhao, Zhiyan Wang, Huan Changan Univ Sch Highway Xian 710018 Peoples R China State Grid Xixian New Area Elect Power Supply Co Xian 712000 Peoples R China Sichuan Highway Planning Survey Design & Res Inst Chengdu 610041 Peoples R China

Effectively preventing hanger bending damage during the configuration transformation of the spatial main cables in suspension bridges is a critical challenge, particularly under the influence of construction errors. This study proposes an active fault-tolerant control method that integrates real-time data feedback, tolerance interval inversion technology, and a suspended lateral bracing (SLB) system to mitigate the risk of hanger bending damage in real time. The method establishes a dynamic inversion mechanism, utilizing data feedback, constraint function reconstruction, and secondary optimization to compensate for construction errors. This ensures that hangers remain undamaged throughout the transformation process. Construction errors are quantified as intervals, with the lower bound of the reliability interval used to account for extreme disturbances. This transforms the inversion process into a multi-objective optimization problem constrained by the worst reliability conditions. By integrating finite element analysis (FEA), reliability analysis, surrogate modeling, and interval analysis, the proposed approach establishes a direct relationship between design variables and the lower bound of the reliability interval. Case study results demonstrate that the proposed method not only ensures structural performance and hanger safety, but also significantly enhances the constructability of the configuration transformation. Additionally, it provides a larger fault tolerance margin, thereby improving the overall efficiency and safety of the construction process.

关键词： self-anchored suspension bridge configuration transformation hanger bending damage active fault-tolerant control suspended lateral bracing system multi-objective optimization

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Physics-based model as a versatile tool towards advanced process control of the naphtha distillation unit

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2025年 218卷 53-65页

作者： Stabrov, Sergey Shevlyagina, Svetlana Russian Acad Sci Inst Automat & Control Proc Far Eastern Branch Vladivostok 690041 Russia Far Eastern Fed Univ Vladivostok 690922 Russia

Following the recent successes with low input data variability and soft sensor design under feed composition changes, this study proposes, among other things, the use of a physics-based approach to improve multivariable model predictive control (MPC) of naphtha distillation. Unlike the industrial settings, where the influence of other manipulated variables is difficult to exclude due to the actions of the human operator, a physically based modeling provides close to an ideal step-by-step and one-by-one testing of the chemical process, resulting in improved accuracy of the transfer function matrix used for MPC design. The proposed approach has been tested on canonical and alternative control schemes used in stabilized naphtha production. Importantly, the physicsbased model resolved all the issues associated with unavailability to reach the set points in controlling the quality of end products when compared with MPC built on the industrial data only irrespective of the control scheme considered. As a result, the steady-state controllability analysis and the closed-loop process behavior highlight that an alternative control structure with transfer function matrix obtained on a physics-based model is a better choice for the industrial case study. Thus, the developed strategy for MPC design was approved as relevant for the cases when a preliminary control scheme requires an update or optimized control scheme without affecting production is of great demand.

关键词： Model predictive control Physics-based model Naphtha distillation Dynamic simulation controllability analysis

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EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Khan, Zahid Saghir, Aamir Katona, Attila Kosztyan, Zsolt T. Univ Pannonia Dept Quantitat Methods Egyet Str 10 Veszprem Hungary Mirpur Univ Sci & Technol Dept Stat Mirpur Pakistan

The Maxwell distribution is extensively employed in statistical modeling to analyze data, notably in the fields of chemistry, astrophysics, demography, actuarial sciences, economics, industry, and engineering. Recently, the application of the Maxwell data generating process (DGP) has focused extensively on the domain of statistical control charts. The V chart and V SQ chart are often used to identify unexpected changes in the distributional shift of the Maxwell process. The V SQ chart offers considerably better performance than the V chart for detecting moderate to large shifts in the scale parameter. However, the V SQ chart adopts the fundamental structure of the Shewhart monitoring scheme and is insensitive to small alterations in the target parameter. We propose anew control chart, namely, the Maxwell exponentially weighted moving average (MXEWMA) chart, for improved monitoring of quality attributes that are assumed to conform to Maxwell data generation. The factors used to design the parameters of the proposed chart are computed at different false alarm probabilities and across various sample sizes. The effectiveness of the suggested scheme is considered in terms of the different features of the run length (RL) distribution, including the average, median and standard deviation. A comparative study of the MXEWMA chart with the existing V SQ chart was performed across various sample sizes. The comparative analysis showed that the MXEWMA chart is an effective alternative and performs well in detecting reasonably small shifts in the parameter. Simulated data are employed to describe the computational procedure of the MXEWMA scheme. The simulation analysis demonstrated that the MXEWMA chart outperforms the existing method in identifying slight changes in the studied parameters. A real dataset is also considered to support the theoretical part of the work.

关键词： Maxwell quality characteristic Skewed data Estimation control chart Run length Simulation

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Development and validation of a human lumbar spine finite element model based on an automated process: Application to disc degeneration

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BIOMEDICAL SIGNAL processING AND control 2025年 105卷

作者： Liang, Chang Yang, Lei Wang, Yuming Zhang, Jinyang Zhang, Bing Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China China Japan Friendship Hosp Dept Spine Surg Beijing 100029 Peoples R China

Background: Patient-specific lumbar spine models are crucial for enhancing diagnostic accuracy, preoperative planning, intraoperative navigation, and biomechanical analysis of the lumbar spine. However, the methods currently used for creating and analyzing these models primarily involve manual operations, which require significant anatomical expertise and often result in inefficiencies. To overcome these challenges, this study introduces a novel method for automating the creation and analysis of subject-specific lumbar spine models. Methods: This study utilizes deep learning algorithms and smoothing algorithms to accurately segment CT images and generate patient-specific three-dimensional (3D) lumbar masks. To ensure accuracy and continuity, vertebral surface models are then constructed and optimized, based on these 3D masks. Following that, model accuracy metrics are calculated accordingly. An automated modeling program is employed to construct structures such as intervertebral discs (IVD) and generate input files necessary for Finite Element (FE) analysis to simulate biomechanical behavior. The validity of the entire lumbar spine model produced using this method is verified by comparing the model within vitro experimental data. Finally, the proposed method is applied to a patient-specific model of the degenerated lumbar spine to simulate its biomechanical response and changes. Results: In the test set, the neural network achieves an average Dice coefficient (DC) of 97.8%, demonstrating high segmentation accuracy. Moreover, the application of the smoothing algorithm reduces model noise substantially. The smoothed model exhibits an average Hausdorff distance (HD) of 3.53 mm and an average surface distance (ASD) of 0.51 mm, demonstrating high accuracy. The FE analysis results agree closely within vitro experimental data, while the simulation results of the degradation lumbar model correspond with trends observed in existing literature.

关键词： Medical image segmentation Three-dimensional modeling Finite element analysis Biomechanics Intervertebral disc degeneration

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An Improved Model Predictive Torque control for Switched Reluctance Motors Based on Variable Hyperbolic Tangent Function modeling

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 5029-5041页

作者： Xu, Shuai Chen, Hanyang Wang, Peixin Nie, Rui Si, Jikai Zhengzhou Univ Elect & Informat Engn Dept Zhengzhou 450001 Peoples R China

In this article, an improved model predictive torque control (MPTC) method is proposed for switched reluctance motors (SRMs) based on variable hyperbolic tangent function modeling. First, the complete modeling of flux-linkage characteristics is developed based on finite measurement data using a variable hyperbolic tangent function. It could be found that only two parameters are necessary to obtain the corresponding flux-linkage at a given rotor position, significantly reducing the storage space required during the implementation process. Additionally, based on the inductance characteristics, an improved switching table is proposed with three switching states. Meanwhile, the operation principles of MPTC are presented with the improved switching table. Finally, the simulation analysis and experimental results are conducted on a four-phase 8/6 SRM to validate the effectiveness of the proposed improved MPTC. The verification results demonstrate that the proposed method can effectively reduce storage space, lower switching frequency, and exhibit minimal torque ripple.

关键词： Torque Switches Reluctance motors Torque control Torque measurement Inductance Analytical models Adaptation models Rotors Predictive models Hyperbolic tangent function improved switching table model predictive torque control (MPTC) switched reluctance motor (SRM) torque ripple reduction

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Intelligent Monitoring System for Deep Foundation Pit Based on Digital Twin

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BUILDINGS 2025年第3期15卷

作者： Pan, Peng Sun, Shuo-Hui Feng, Jie-Xun Wen, Jiang-Tao Lin, Jia-Rui Wang, Hai-Shen China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat Harbin 150080 Peoples R China Minist Emergency Management Key Lab Earthquake Disaster Mitigat Harbin 150080 Peoples R China Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Minist Housing & Urban Rural Dev Key Lab Digital Construct & Digital Twin Beijing 100084 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Underground space development has significantly increased the depth, scale, and complexity of foundation pit engineering. However, monitoring systems lack mechanical analysis models and fail to predict and control construction risks. Additionally, the foundation pit model could not be updated based on on-site observed data, leading to inaccurate predictions. This study proposes a DT modeling framework for foundation pits, which is used to simulate, predict, and control the risks associated with the entire excavation process. Consequently, based on the DT modeling framework, a DT foundation pit model (DTFPM) was established using modeling and updating algorithms. This study summarizes and identifies the key modeling parameters of foundation pits. A parametric modeling algorithm based on ABAQUS (v2020) was developed to drive the excavation pit modeling process within seconds. Furthermore, an inverse analysis optimization algorithm based on genetic algorithms (GA) and real-time observed deformation was employed to update the elastic modulus of the soil. The algorithm supports parallel computing and can converge within 10 generations. The prediction error of the model after inverse analysis can be reduced to within 10%. Finally, the authors applied DTFPM to establish an intelligent monitoring system. The focus is on real-time and predictive warnings based on the monitoring deformation of the current construction step and the updated model. This study analyzes a Beijing project case to verify the effectiveness of the system, demonstrating the practical application of the proposed method. The results showed that the DTFPM could accurately simulate the deformation behavior of the foundation pit. The system could provide more timely and accurate safety warnings. The proposed method can potentially contribute to the intelligent construction of foundation pits in the future, both theoretically and practically.

关键词： digital twin deep foundation pit intelligent monitoring system inverse analysis

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Prediction of Wafer Performance: Use of Functional Outlier Detection and Regression

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IEEE ACCESS 2025年 13卷 35298-35308页

作者： Kim, Kyusoon Oh, Seunghee Bae, Kiwook Oh, Hee-Seok Seoul Natl Univ Dept Stat Seoul 08826 South Korea Samsung Elect Innovat Ctr Hwaseong 18448 South Korea

Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also supports improved decision-making, particularly in process control and quality assurance. To exploit the consecutive nature of OES data, we propose a prediction method based on a functional approach using multivariate functional partial least squares regression, coupled with dimension reduction and a novel outlier detection technique via functional independent component analysis. The proposed approach improves prediction performance by capturing the continuous nature of OES data and effectively extracting the components that describe the data structure. Numerical experiments, including simulation studies and real-world applications of OES data, demonstrate the effectiveness of the proposed method through superior prediction performance, as evidenced by low RMSE and MAE values, particularly in the presence of outliers.

关键词： Metrology Anomaly detection Semiconductor device modeling Independent component analysis Dimensionality reduction data models Wavelength measurement Semiconductor device measurement Random variables Principal component analysis Functional data analysis functional independent component analysis functional partial least squares optical emission spectroscopy virtual metrology

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SENTINEL: A Shiny app for processing and analysis of fenceline sensor data

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ENVIRONMENTAL MODELLING & SOFTWARE 2025年 189卷

作者： MacDonald, M. K. Champion, W. M. Thoma, E. D. US Environm Protect Agcy Ctr Environm Measurement & Modeling Off Res & Dev Res Triangle Pk NC 27709 USA

SENTINEL (SEnsor NeTwork INtelligent Emissions Locator) is an application developed in R Shiny to support emerging user groups of lower cost fenceline sensors, such as those monitoring volatile organic compound or methane concentrations inside and near industrial facilities or for emergency response applications. During deployment, sensors collect a large quantity of high-frequency pollutant concentration data, time-aligned meteorological information, and sensor performance indicators. These sensors can collect a quantity of data that is overwhelming for users to process and understand without designated software. The SENTINEL application provides users with a consistent framework for processing, analyzing, and visualizing fenceline sensor data. SENTINEL temporally aggregates data for synthesized analysis and interpretation. Quality assurance screening automatically removes anomalous datapoints and a baseline correction algorithm reduces background drift in pollutant concentration data. SENTINEL offers streamlined sensor data analysis through a user-friendly graphical user interface that supports interpretation of source emission data and sensor-triggered field samples.

关键词： Air pollution Air quality Sensors Fenceline SPod Shiny app

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Monitoring mean of INAR(1) process with discrete mixture exponential innovations

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COMPUTATIONAL STATISTICS 2025年第2期40卷 821-862页

作者： Irshad, M. R. Ahammed, Muhammed Maya, R. Cochin Univ Sci & Technol Dept Stat Cochin 682022 Kerala India Univ Coll Dept Stat Thiruvananthapuram 695034 Kerala India

This paper presents a discrete counterpart of the mixture exponential distribution, namely discrete mixture exponential distribution, by utilizing the survival discretization method. The moment generating function and associated moment measures are discussed. The distribution's hazard rate function can assume increasing or decreasing forms, making it adaptable for diverse fields requiring count data modeling. The paper delves into two parameter estimation methods and evaluates their performance through a Monte Carlo simulation study. The applicability of this distribution extends to time series analysis, particularly within the framework of the first-order integer-valued autoregressive process. Consequently, an INAR(1) process with discrete mixture exponential innovations is proposed, outlining its fundamental properties, and the performance of conditional maximum likelihood and conditional least squares estimation methods is evaluated through a simulation study. Real data analysis showcases the proposed model's superior performance compared to alternative models. Additionally, the paper explores quality control applications, addressing serial dependence challenges in count data encountered in production and market management. As a result, the INAR(1)DME process is employed to explore control charts for monitoring autocorrelated count data. The performance of two distinct control charts, the cumulative sum chart and the exponentially weighted moving average chart, are evaluated for their effectiveness in detecting shifts in the process mean across various designs. A bivariate Markov chain approach is used to estimate the average run length and their deviations for these charts, providing valuable insights for practical implementation. The nature of design parameters to improve the robustness of process monitoring under the considered charts is examined through a simulation study. The practical superiority of the proposed charts is demonstrated through effective mode

关键词： Mixture exponential INAR(1) process CUSUM chart EWMA chart

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Digital twin system for manufacturing processes based on a multi-layer knowledge graph model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Su, Chang Tang, Xin Jiang, Qi Han, Yong Wang, Tao Jiang, Dongsheng Ocean Univ China Dept Informat Sci & Engn Qingdao 266100 Peoples R China Qingdao Marine Sci & Technol Ctr Lab Reg Oceanog & Numercial Modeling Qingdao Peoples R China North China Elect Power Univ Control & Comp Engn Beijing 102206 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China AECC South Ind Co Ltd Zhuzhou 410000 Hunan Peoples R China

Digital twin technology in the manufacturing process faces challenges like integrating diverse data sources and managing real-time data flow. To address this, we propose a novel three-layer knowledge graph architecture to enhance digital twin modeling for manufacturing processes. This architecture consists of a concept layer that structures key information into a knowledge network, a model layer that aligns digital and physical parameters, and a decision layer that leverages model and real-time data for decision support. Validated in aero-engine blade production, this system integrates multi-source data, enhances predictive analysis and anomaly detection, and supports process control and quality management. Over a 5-month validation period, the maximum contour error precision of the blades improved from 0.073 mm to 0.062 mm, and the product qualification rate increased from 81.3% to 85.2%. This demonstrates the system's robust capability for advancing digital twin utilization in manufacturing, highlighting its potential for future improvements.

关键词： Digital twin Knowledge graph Intelligent manufacturing data integration Manufacturing process management Decision support

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