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Micro-Scale Particle Tracking: From Conventional to data-driven methods

MICROMACHINES 2024年第5期15卷 629-629页

作者： Wang, Haoyu Hong, Liu Chamorro, Leonardo P. Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA Univ Illinois Dept Geol Urbana IL 61801 USA

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

关键词： micro-scale positioning particle tracking velocimetry fluid mechanics data-driven method deep learning neural networks

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From Static and Dynamic Perspectives:A Survey on Historical data Benchmarks of Control Performance Monitoring

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 300-316页

作者： Pengyu Song Jie Wang Chunhui Zhao Biao Huang the State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University IEEE the Department of Chemical and Materials Engineering University of Alberta

In recent decades, control performance monitoring(CPM) has experienced remarkable progress in research and industrial applications. While CPM research has been investigated using various benchmarks, the historical data benchmark(HIS) has garnered the most attention due to its practicality and effectiveness. However, existing CPM reviews usually focus on the theoretical benchmark, and there is a lack of an in-depth review that thoroughly explores HIS-based methods. In this article, a comprehensive overview of HIS-based CPM is provided. First, we provide a novel static-dynamic perspective on data-level manifestations of control performance underlying typical controller capacities including regulation and servo: static and dynamic properties. The static property portrays time-independent variability in system output, and the dynamic property describes temporal behavior driven by closed-loop feedback. Accordingly,existing HIS-based CPM approaches and their intrinsic motivations are classified and analyzed from these two ***, two mainstream solutions for CPM methods are summarized, including static analysis and dynamic analysis,which match data-driven techniques with actual controlling behavior. Furthermore, this paper also points out various opportunities and challenges faced in CPM for modern industry and provides promising directions in the context of artificial intelligence for inspiring future research.

关键词： Control performance monitoring (CPM) data-driven method historical data benchmark (HIS) industrial process performance index static and dynamic analysis

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Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

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ADVANCES IN STRUCTURAL ENGINEERING 2021年第16期24卷 3609-3626页

作者： Fan, Xingyu Li, Jun Hao, Hong Guangzhou Univ Guangzhou Univ Curtin Univ Joint Res Ctr Struct M Sch Civil Engn Guangzhou Peoples R China Xian Univ Technol Sch Mech & Precis Instrument Engn Xian Peoples R China Curtin Univ Sch Civil & Mech Engn Ctr Infrastruct Monitoring & Protect Bentley WA Australia

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

关键词： piezoelectric materials electromechanical impedance technique non-destructive testing data-driven method physics-based method damage quantification

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data-driven methods to Predict the Burst Strength of Corroded Line Pipelines Subjected to Internal Pressure

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Journal of Marine Science and Application 2022年第2期21卷 115-132页

作者： Jie Cai Xiaoli Jiang Yazhou Yang Gabriel Lodewijks Minchang Wang Department of Technology and Innovation University of Southern DenmarkCampusvej 55-5230 OdenseDenmark Department of Maritime and Transport Technology Delft University of Technology2628 CD Delftthe Netherlands College of Advanced Interdisciplinary Studies National University of Defence TechnologyChangsha410073P.R.China School of Engineering University of NewcastleNSW2308Australia Hangzhou Silan Microelectronics Co.Ltd. HangzhouZhejiang310008P.R.China.

A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long ***-element method and empirical formulas are thereby used for the strength prediction of such pipes with ***,it is time-consuming for finite-element method and there is a limited application range by using empirical *** order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven *** supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental *** analysis is first conducted to determine proper pipe features for *** tuning to control the learning process is then performed to fit the best strength models for corroded *** all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest *** SVM model provides both high training accuracy and high validation *** LR model has the best performance in terms of generalization *** models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.

关键词： Pipelines Corrosion Burst strength Internal pressure data-driven method Machine learning

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data-driven casting defect prediction model for sand casting based on random forest classification algorithm

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China Foundry 2024年第2期21卷 137-146页

作者： Bang Guan Dong-hong Wang Da Shu Shou-qin Zhu Xiao-yuan Ji Bao-de Sun Shanghai Key Lab of Advanced High-Temperature Materials and Precision Forming School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Lab of Metal Matrix Composites Shanghai Jiao Tong UniversityShanghai 200240China Hefei Casting and Forging Factory of Anhui Heli Co. LtdHefei 230000China State Key Laboratory of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhan 430074China

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo *** collected data includes four types of defects and corresponding process parameters were used to construct the RF *** results show a recall rate above 90% for all *** Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF ***,the classification model was applied to different production conditions for quality *** the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature *** prediction model,when applied to the factory,greatly improved the efficiency of defect *** show that the scrap rate decreased from 10.16% to 6.68%.

关键词： sand casting process data-driven method classification model quality prediction feature importance

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data-driven control synthesis for discrete-time semi-Markov jump systems

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第6期362卷

作者： Niu, Huawei Yang, Liu Li, Zhicheng Wang, Yang Harbin Univ Sci & Technol Dept Automat 52 Xuefu St Harbin 150080 Heilongjiang Peoples R China Shenzhen Polytech Univ IoT Res Inst 7098 Liuxian St Shenzhen 518055 Guangdong Peoples R China

In discrete-time semi-Markov jump systems, the system matrix for each subsystem is usually determined by the system identification method. For such systems, a large amount of data and computation are generally required to obtain system parameters close to the real ones. This method has a lot of difficulties in practical applications, especially when there are too many subsystems. In contrast, the data-driven method can skip the system identification step and directly design the controller based on the input and output data. Therefore, the data- driven method is investigated to solve the control problem of discrete-time semi-Markov jump systems. Firstly, a data model for discrete-time semi-Markov hopping systems is proposed. Secondly, the controller design method for the semi-Markov jump system with known and partly unknown transition probabilities is carried out using Lyapunov functions. Thirdly, for the practical situation, the time when the jump occurs is unknown;a new algorithm for mode estimation and the application conditions of this algorithm are given. Finally, a discrete-time semi-Markov jump system is constructed with the car model as an example to verify the effectiveness of the proposed method.

关键词： data-driven method Discrete-time semi-Markov jump system Semi-Markov chain Lyapunov function

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Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

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ENGINEERING STRUCTURES 2025年 322卷

作者： Astorga, A. Gueguen, P. Beth, M. Bessoule, N. Univ Gustave Eiffel Univ Grenoble Alpes Univ Savoie Mont Blanc ISTerreCNRSIRD Grenoble France Sixense Paris France

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build'HealthTM framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build'HealthTM c

关键词： Population-based structural health monitoring Transfer of knowledge data-driven method Building damage detection Existing buildings

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State of health estimation for lithium-ion batteries based on recurrence plot analysis using charging voltage curves

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JOURNAL OF ENERGY STORAGE 2025年 123卷

作者： Zou, Wenhao Hu, Zhiyong Yu, Kun Zhang, Heng Lu, Hongdian Mao, Lei Univ Sci & Technol China Sch Engn Sci Hefei Peoples R China AnHui Univ Sch Elect Engn & Automat Hefei Peoples R China Hefei Univ Sch Energy Mat & Chem Engn Hefei Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei Peoples R China

Accurate state of health (SOH) estimation is crucial for ensuring the reliability and safety of lithium-ion batteries (LIBs) in various applications. Traditional SOH estimators often require complex models, strict test profiles (e.g., low cycling rates), or extensive training data, limiting their applications. Hence, this study introduces a novel data-driven method that integrates recurrence plot (RP) analysis for direct SOH estimation using a simple regression model trained on a single battery. The method begins by transforming voltage data from charging cycles into RP images, which serve as a basis for extracting significant features associated with LIB degradation. These features, termed features-of-interest (FOIs), are then thoroughly analyzed to establish their correlation with the aging status of LIBs. Experimental results demonstrate that the proposed RP-derived FOIs align closely with those obtained from differential voltage analysis (DVA) using open circuit voltage (OCV) data, even under high charging rates. This alignment underscores the effectiveness of the FOIs as reliable indicators of SOH. Moreover, SOH estimations conducted on battery aging datasets reveals that RP-derived FOIs from a single LIB can accurately estimate the SOH of all remaining LIBs through a simple regression model, reducing the need for extensive training data and computational resources.

关键词： State of health estimation Lithium-ion batteries data-driven method Recurrence plot analysis Feature extraction

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A comprehensive review on uncertainty modeling methods in modern power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Wang, Zhaoyuan Bu, Siqi Wen, Jiaxin Huang, Can Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Guangdong Peoples R China Hong Kong Polytech Univ Res Ctr Grid Modernisat Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Int Ctr Urban Energy Nexus Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy Kowloon Hong Kong Peoples R China China Southern Power Grid Energy Dev Res Inst Guangzhou Guangdong Peoples R China Pacific Gas & Elect Co Oakland CA USA

In recent years, modern power systems integrated with renewable energy sources and communication technologies have been rapidly developed. However, the randomness and intermittency of renewable energy sources, together with the unexpected disturbances in the vulnerable cyber layer, introduce uncertainties into power systems, which pose huge threats to the stable operation of modern power systems and should be carefully considered. Moreover, there is a lack of a comprehensive review of uncertainty modeling in modern power systems, which is one of the significant tasks in quantifying uncertainties. In this review, the methods and respective characteristics of uncertainty modeling in modern power systems are analyzed. According to the outputs of methods, they are categorized as model-driven and data-driven methods. The existing uncertainties in modern power systems and the respective modeling methods are investigated from the aspects of the physical layer, cyber layer, and economic and social layer. The applications of uncertainty modeling methods in modern power system operation are summarized from the aspects of steady state, dynamic, and risk analysis of modern power systems. Also, the prospective future research topics are recommended.

关键词： Uncertainty modeling Model-driven method data-driven method Renewable energy Modern power system

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Sparse identification-assisted exploration of the atomic-scale deformation mechanism in multiphase CoCrFeNi high-entropy alloys

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Science China(Technological Sciences) 2024年第4期67卷 1124-1132页

作者： XIAO Lu GUO XiaoXiang SUN YuTong WANG Gang LONG WeiMin LIAW Peter K. REN JingLi School of Mathematics and Statistics Zhengzhou UniversityZhengzhou 450001China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Institute of Materials Shanghai UniversityShanghai 200444China SKL of Advanced Brazing Metals&Technology Zhengzhou Research Institute of Mechanical EngineeringZhengzhou 450001China Department of Materials Science and Engineering The University of TennesseeKnoxvilleTN 37996USA

This study investigated the atomic-scale deformation mechanism of multiphase CoCrFeNi high-entropy alloys(HEAs)at liquid helium,liquid nitrogen,and room temperatures.A million-atom multiphase HEA was prepared using molecular dynamics simulation involving melt and quench *** HEA exhibited high-density dislocations and some twins,consistent with experimental *** analysis revealed an inconsistent evolution of the microstructure under tensile *** particular,the elastic and initial plastic stages exhibited an increase in the disordered structure at the expense of the face-centered cubic and hexagonal close-packed structures,followed by a subsequent transformation involving multiple structural ***,through sparse identification,a model depicting microstructural evolution during tension was extracted for the CoCrFeNi HEA at three typical temperatures and three tensile *** model highlighted the importance of the body-centered cubic structure in the evolutionary process.

关键词： high-entropy alloy data-driven method microstructure evolution multiphase structure molecular dynamics simulation

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