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Dynamic Non-Gaussian and Nonlinear Industrial process Monitoring Using Deep analysis of Hybrid Characteristics

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Yudong Bai, Haijun Li, Shuai Zhou, Xiaofeng Shenyang Univ Chem Technol Coll Informat Engn Shenyang 110142 Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

data-driven monitoring using principal component analysis (PCA) and independent component analysis (ICA) has been studied extensively for modern industrial processes. However, due to the hybrid dynamic, nonlinear, and non-Gaussian characteristics of variables, complex industrial processes are difficult to model accurately. In view of the above issue, we present a novel method named deep analysis of hybrid characteristics (DAHCs) for industrial process monitoring. Considering that different monitoring methods can extract different characteristics, DAHC builds a process monitoring model on the basis of dynamic inner PCA (DiPCA), kernel PCA (KPCA), and ICA using a deep serial structure. First, process data are decomposed using DiPCA to analyze the dynamic characteristics. Then, the residual of DiPCA is further decomposed by multilayer KPCA-ICA for simultaneously analyzing nonlinear and non-Gaussian characteristics. Finally, the merged Bayesian inference comprehensive statistic (MBICS) for the dynamic, nonlinear, and non-Gaussian subspaces is used for integrated monitoring of hybrid characteristics. Typically, existing researches on hybrid characteristics indicate that single-layer feature extraction is insufficient for analyzing complex information in industrial processes. Current hierarchical feature extraction methods have yet to simultaneously account for dynamic, nonlinear, and non-Gaussian characteristics. DAHC adopts a deep serial structure to analyze the hybrid dynamic, nonlinear, and non-Gaussian characteristics of industrial data. Its feasibility and effectiveness are validated via the Tennessee Eastman (TE) chemical process and a practical mineral processing of lean iron ore. The results show that the TE and mineral processes have the highest average fault detection rates (FDRs) of 84.47% and 99.42% compared with other related methods.

关键词： Feature extraction Principal component analysis Kernel data models data mining Analytical models Computational modeling Nonhomogeneous media Accuracy Deep analysis deep serial structure hybrid characteristics industrial processes process monitoring process monitoring

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Hybrid self-learning model for the prediction and control of sintering furnace temperature

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control ENGINEERING PRACTICE 2025年 154卷

作者： Dai, Yuanshen Chen, Ning Shao, Zhijiang Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China BASF China Co Ltd Shanghai 200137 Peoples R China Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China

Ternary cathode materials are important components of lithium-ion batteries. However, the sintering process during manufacturing is challenging to control due to the inaccessibility of key dynamic variables and the frequent fluctuations in operating conditions. These lead to high energy consumption and inconsistent product quality. In this paper, we propose a hybrid self-learning prediction model and control method for sintering furnace temperature based on both first-principle and process data. Firstly, a mechanism model with temperature time delay is established based on energy flow analysis in the furnace. To capture the tail gas temperature dynamic in the mechanism model, a Ventingformer-based prediction data-driven model is proposed. In this model, a memory updating technique and an autoregressive module based on the Transformer framework are developed to identify long-time dependencies and respond to variations in input sequences. Then, a hybrid self-learning modeling framework is designed. Based on the established hybrid model, a multiscale objective function-based nonlinear model predictive control (MSCF-NMPC) method is proposed to achieve precise tracking control of the internal temperature in the furnace. A multiscale objective function with short-term cost in terms of energy consumption and tracking accuracy as well as long-term cost in terms of energy loss is constructing in the control optimization problem. Finally, the proposed hybrid self-learning model and MSCF-NMPC method are verified using the actual process data from a sintering furnace, demonstrating the effectiveness of the proposed method. The results offer practical guidance for industrial applications.

关键词： Ternary cathode materials Sintering furnace Self-learning hybrid model Multi-scale objective function Nonlinear model predictive control

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Performance analysis and Verification of Elastic Circuits under process Variations

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第10期38卷 2273-2287页

作者： Zaeemi, M. Mohammadi, S. Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

The diversity of designs and optimization methods for elastic systems makes evaluating their performance challenging. This paper proposes a method based on xMAS and high-level modeling to analyze the performance, functional and timing verification of regular and early evaluation synchronous elastic circuits while considering process variations. The xMAS framework provides modularity, precise semantics, and executable models, enhancing formal verification and high-level analysis capabilities over existing approaches. The proposed platform calculates the throughput value, which is the most critical performance factor in elastic circuits. The power, delay, and PDP of all early evaluation elastic components are evaluated under process variations and compared to those of regular elastic circuits. The results indicate that early evaluation properties increase the sensitivity of circuit components to process variations, making their performance less predictable. modeling results of the Elastic DLX microprocessor highlight these findings by demonstrating that process variations can cause a 26% reduction in throughput and lead to a 0.2% chance of synchronization errors between data and control signals. These findings underscore the critical need to account for process variations when designing and verifying early evaluation elastic circuits to maintain performance reliability.

关键词： Elastic Circuits Early Evaluation High-level modeling Performance analysis process Variation

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Improving data-Driven Inferential Sensor modeling by Industrial Knowledge: A Bayesian Perspective

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 1043-1055页

作者： Chen, Zhichao Wang, Hao Song, Zhihuan Ge, Zhiqiang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming 525000 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China

Accurate quality variable inference by process variables is the core of industrial inferential sensor modeling, where recent advancements have seen deep learning (DL) models achieving remarkable success. However, integrating knowledge of unit operations is critical for improving inferential sensor performance, yet it has received little attention. The main challenge lies in the incompleteness and correctness of industrial knowledge due to its semi-empirical nature and inevitable engineering errors. Addressing this, this article introduces the gradient knowledge network based on the graph neural network's message-passing mechanism within the variational Bayesian inference framework, which naturally copes with the abovementioned issues by fusing observational data. Initially, the prior knowledge about the process variables, which mirrors the graph in graph neural network, is parameterized as Dirichlet distribution based on the analysis of message-passing mechanism. However, the divergence computation and normalization constraints are challenging for model implementation. To navigate these challenges, the Bayesian inference problem is transformed into an optimization problem, subsequently recast as a simulation problem induced by the gradient field, ensuring compatibility with DL backends. Furthermore, a theoretical iteration equation is derived to maintain the normalization constraint. The architecture of the proposed model and its learning algorithm are then detailed. Finally, various experiments are conducted on two real industrial processes to demonstrate the model's efficacy from the perspective of prediction accuracy, sensitivity analysis, and ablation study.

关键词： Mathematical models Feature extraction Predictive models Knowledge engineering Bayes methods data models Computational modeling Accuracy Analytical models Mirrors Deep learning (DL) graph neural network inferential sensor knowledge automation variational inference

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modeling the working pressure of a cement vertical roller mill using SHAP-XGBoost: A "conscious lab of grinding principle" approach

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POWDER TECHNOLOGY 2025年 457卷

作者： Fatahi, Rasoul Abdollahi, Hadi Noaparast, Mohammad Hadizadeh, Mehdi Univ Tehran Coll Engn Sch Min Engn Tehran Iran

The optimization and control of Vertical Roller Mill (VRM) circuits are critical for industrial processes, yet limited modeling has slowed progress in operator training, error minimization, and laboratory cost savings. To overcome limitations, the innovative "Conscious Lab" (CL) was introduced, utilizing industrial datasets and Explainable Artificial Intelligence (XAI) techniques. For the first time, CL combines Shapley Additive Explanations (SHAP) with machine learning models, such as XGBoost and Random Forest, to optimize VRM operations. Differential pressure and feed rate were identified as the most influential parameters of working pressure, essential for maintaining stable operations. Robust linear correlations (coefficients: 0.94 for feed rate, 0.84 for main drive power, and 0.83 for differential pressure) and nonlinear marginal plots (0.95, 0.81, and 0.81) highlighted how increases in these parameters significantly raise working pressure. The XGBoost model achieved remarkable prediction accuracy (0.99 for training and 0.98 for testing/validation) with a low RMSE (0.01), confirmed by 5-fold cross-validation. SHAP analysis further verified the relationship between working pressure and key parameters, aligning with VRM grinding principles. The CL approach introduces a data-driven control system enabling real-time decision-making, process optimization, and improved production efficiency, showcasing the transformative potential of advanced data analytics for industrial applications.

关键词： control and optimization VRM Explainable artificial intelligence Working pressure

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Physics-informed generative regression for industrial process modeling in steel strip rolling

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern Univ State Key Lab Rolling & Automat Shenyang Peoples R China Aalto Univ Dept Elect Engn & Automat Espoo Finland Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea Sweden

Machine learning (ML) provides powerful tools for industrial process modeling by identifying patterns and optimizing performance. However, conventional ML models often struggle with accuracy and reliability because they rely solely on data and fail to incorporate process-specific knowledge. To address this limitation, we propose a physics-informed ML approach that integrates engineering insights into an ML model. Specifically, we develop a variational autoencoder-based generative regression model with a probabilistic regression layer, enabling uncertainty-aware predictions. Additionally, we derive process knowledge through finite element analysis and incorporate it into the model via a custom physics-informed loss function, ensuring consistency with real-world process dynamics. The effectiveness of this approach is demonstrated in the steel strip rolling process, a critical manufacturing operation where precise flatness control directly impacts product quality. By integrating data-driven learning with physics-based constraints, the proposed method enables more accurate flatness predictions, reducing defects and improving process stability. This research provides a practical and scalable solution for industrial applications, offering manufacturers a more reliable tool for optimizing production processes and ensuring product quality.

关键词： Physics-informed machine learning process manufacturing process knowledge Generative regression Steel strip rolling

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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 3249-3249页

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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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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