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data-driven modeling of Wireless Power Transfer Systems With Slowly Time-Varying Parameters

IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第11期35卷 12442-12456页

作者： Chen, Fengwei Padilla, Arturo Young, Peter C. Garnier, Hugues Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Univ La Frontera Dept Mech Engn Temuco 4811230 Chile Univ Lancaster Syst & Control Grp Lancaster Environm Ctr Lancaster LA1 4YQ England Univ Lorraine Ctr Rech Automat Nancy CNRS F-54000 Nancy France

This article considers the data-driven modeling of a class of phase-controlled wireless power transfer (WPT) systems, where the load may vary slowly with respect to time. The dominant mode analysis suggests that a model of the Hammerstein type, which consists of a static nonlinearity function, followed by a linear time-varying model with a pure time delay, is the best structure to describe the input-output relationship of the system. On this basis, we derive a small-signal model that is linear in the variables in order to aid control design and allow the associated model parameters to be estimated from sampled input-output data using the standard refined instrumental variable (RIV) method. In the presence of a time-varying load, however, the plant model parameters may not be correctly estimated if the load response is not removed. In order to address this problem, a new recursive RIV method is proposed, in which an effective technique is introduced to track the load response, so allowing the parameters and time delay of the time-varying model to be accurately estimated. The effectiveness of the proposed method is verified by applying it to both a simulation model and a laboratory system.

关键词： Load modeling Integrated circuit modeling Inverters Delay effects data models Time-varying systems Analytical models data-driven modeling recursive estimation system identification time delay time-varying parameter wireless power transfer (WPT)

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data-driven modeling of Wireless Power Transfer Systems With Multiple Transmitters

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第11期35卷 11363-11379页

作者： Chen, Fengwei Young, Peter C. Garnier, Hugues Deng, Qijun Kazimierczuk, Marian K. Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Univ Lancaster Lancaster Environm Ctr Syst & Control Grp Lancaster LA1 4YQ England Univ Lorraine CNRS Ctr Rech Automat Nancy F-54000 Nancy France Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Wright State Univ Dept Elect Engn Dayton OH 45435 USA

This article develops a new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems. A continuous-time multiple-input single-output (MISO) model with pure time delays is used to characterize the input-output behavior of the system, where the transfer functions associated with each input channel are not constrained to have the same denominator. Moreover, the time delays are allowed to be a fraction of the sampling interval in order to account for the delay effects that stem from circuit components and wireless communication, which are, by nature, often a fraction of the sampling interval. An optimal refined instrumental variable method is proposed to estimate the parameters and time delays of the MISO model based on sampled input-output data. In contrast to the conventional circuit-theory-based modeling methods that rely on circuit parameters and result in models which are often complex, the proposed data-based method yields parsimonious models, whose parameters are directly estimated from input-output data. Due to the easy availability of sampled data in control engineering applications, the proposed method is clearly more user-friendly, having a broad prospect for efficient operation of WPT systems, such as prediction, optimization, and control. Numerical and experimental results are presented to validate the effectiveness and merit of the proposed method.

关键词： Integrated circuit modeling Transmitters MISO communication Inverters Coils Receivers Delay effects data-driven modeling instrumental variable (IV) method multiple-input single-output (MISO) system time-delay estimation wireless power transfer (WPT)

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data-driven modeling of Carbon Dioxide Corrosion for Integrity Management Application

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SPE PRODUCTION & OPERATIONS 2019年第4期34卷 667-677页

作者： Mnasri, Hamdi Wassar, Taoufik Franchek, Matthew A. Marotta, Egidio (Ed) Univ Houston Dept Mech Engn Houston TX 77004 USA Univ Houston Subsea Engn Program Houston TX 77004 USA Halliburton Landmark Digital Twin & Optimizat Houston TX USA

In this work we expand on the carbon dioxide (CO2) corrosion-rate model presented by DeWaard et al. (1995) using a parameter-varying (PV) approach to improve prediction accuracy. Specifically, the constant coefficients of the reaction-kinetics-dependent term are replaced with varying parameters presented as a function of mean flow velocity and pH. On the basis of experimental data provided by Nesic et al. (1996) and Dugstad et al. (1994), we compare the corrosion-rate predictions between the original model and the proposed model. A significant improvement of the correlation coefficient (R-2) from 0.54 to 0.90 is achieved when the PV coefficients replace the constant coefficients. Accurate predictions of the CO2 corrosion rate affect useful life forecasting of oil and gas systems during front-end engineering design studies, facilitate condition-based maintenance, and serve as the basis for the creation of a digital twin for this process.

关键词： carbon dioxide corrosion data-driven modeling flow assurance system identification integrity management

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data-driven modeling of Microgrid Transient Dynamics Through Modularized Sparse Identification

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第1期15卷 109-122页

作者： Nandakumar, Apoorva Li, Yan Zheng, Honghao Zhao, Junhui Zhao, Dongbo Zhang, Yichen Hong, Tianqi Chen, Bo Penn State Univ Dept Elect Engn University Pk PA 16802 USA Commonwealth Edison Co Chicago IL 60197 USA Eversource Energy Adv Engn Analyt Berlin CT 06037 USA Eaton Corp Global Technol Golden CO 80401 USA Univ Texas Arlington Dept Elect Engn Arlington TX 76010 USA Argonne Natl Lab Lemont IL 60439 USA

Modularized sparse identification (M-SINDy) is developed in this paper for effective data-driven modeling of the nonlinear transient dynamics of microgrid systems. The high penetration of power-electronic interfaces makes microgrids highly susceptible to disturbances, causing severe transients, especially in the islanded mode. The M-SINDy method realizes distributed discovery of nonlinear dynamics by partitioning a higher-order microgrid system into multiple subsystems and introducing pseudo-states to represent the impact of neighboring subsystems. This specific property of the proposed algorithm is found to be very useful while working with re-configurable and scalable microgrids. The governing equations of the subsystems are identified through regression by mapping the nonlinear system's data to a linear system in a large functional space. The effectiveness of the M-SINDy method is tested and validated through numerical examples and comparisons with other existing identification models on a typical islanded microgrid. The method can simultaneously compute the governing equations of different subsystems and is examined to be robust to measurement noises and partial observations. This paper highlights the advances of data science in providing a potent tool for modeling and analyzing higher-order nonlinear microgrid systems. Dynamic discovery of system transients from measurements can be beneficial for designing control strategies that improve the overall microgrid stability and reliability.

关键词： data-driven modeling distributed energy resources (DERs) microgrids modularized design psedo-states sparse identification transient dynamics

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data-driven modeling and analysis based on complex network for multimode recognition of industrial processes

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JOURNAL OF MANUFACTURING SYSTEMS 2022年 62卷 915-924页

作者： Sun, Yan-Ning Zhuang, Zi-Long Xu, Hong-Wei Qin, Wei Feng, Meng-Jiao Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Aerosp Equipments Manufacturer Co Ltd Shanghai 200245 Peoples R China

An industrial process usually has multiple operating conditions or periods due to various factors such as the fluctuation of raw material quality, differences of worker levels, and changes of product specifications. Industrial data from different operating conditions or periods have different statistical characteristics, namely multimode behavior. In fact, multimode recognition of industrial processes is the first problem to develop effective intelligent manufacturing strategies for engineers. The clustering-based methods can help them characterize multi mode behavior in historical databases and group them together with little detailed knowledge of the process. However, these methods are facing the challenges of massive high-dimensional industrial big data and unknown mode number. This study proposes a construction method of complex network to describe the potential relationship in industrial big data. The topological structure of complex network can be acquired by taking the correlation matrix of multi-variables as nodes and the similarity between correlation matrices as edges. On this basis, community detection technology is employed to reveal the aggregation behavior of nodes in network. It takes the maximum modularity as the optimization objective, and partitions each node into one community through greedy search algorithm, where a community denotes a mode of industrial process. Finally, case studies have demonstrated the proposed method has good performance and application prospects.

关键词： data mining data-driven modeling Multimode recognition Complex network Industrial process

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data-driven modeling and prediction on hysteresis behavior of flexure RC columns using deep learning networks

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2023年第11-12期32卷 e2039-e2039页

作者： Guo, Jiangmeng Wang, Luji Shan, Jiazeng Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China Stanford Univ Dept Civil & Environm Engn Stanford CA USA Shanghai Engn Res Ctr Resilient Cities & Intellige Shanghai Peoples R China

Hysteresis behavior of structural components has been one of the research focus for the structural engineering community for decades, comprehensively determines the structural performance and safety, and plays an important role in structural disaster mitigation. It is of great significance to continuously monitor structural responses and accurately characterize structural hysteresis. Currently, the nonlinear properties of real-world structural components cannot be obtained before its failure. Thus, a historical database is collected firstly. Then, a data-driven analysis method is proposed for predicting hysteresis behaviors of reinforced concrete (RC) columns. A bidirectional LSTM (BLSTM) network is developed to model and predict hysteresis curves. The data with unfixed lengths are specially processed to simultaneously guarantee a uniform size and avoid data loss, and the clipping layers are inserted in the model to clip off inferior predictions and improve the accuracy. This methodology is systematically studied and validated by employing a sythetic database generated by numerical simulation and the full-scale experiment database named PEER database. Result shows that proposed BLSTM can predict the hysteresis curves of the RC components with acceptable accuracy and robustness. Moreover, the interpretability analysis is performed on identifying the learning and prediction principle of the BLSTM model on hysteresis prediction and its accuracy variation under different model architectures.

关键词： data-driven modeling hysteresis behavior LSTM model prediction

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data-driven modeling of pharmacological systems using endpoint information fusion

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COMPUTERS IN BIOLOGY AND MEDICINE 2015年 61卷 36-47页

作者： Kim, Chang-Sei Fazeli, Nima Hahn, Jin-Oh Univ Maryland Dept Mech Engn College Pk MD 20742 USA

This study investigated the feasibility of deriving data-driven model of a class of pharmacological systems using the information fusion of endpoint responses. For a class of pharmacological systems subsuming conventional steady-state dose-response models, compartmental pharmacokinetic-pharmacodynamic models and indirect response models, a relation between multiple endpoint responses was formalized and analyzed to elucidate if this class of systems is identifiable, i.e., if the data-driven model of this class of systems can be derived from the endpoint responses alone. It was shown that this class of systems is fully identifiable in case all the responses involve effect compartments. However, it was also observed that persistently exciting dose profiles may be required in accurately deriving reliable data-driven model with low variance. The findings from the identifiability analysis were demonstrated using benchmark pharmacological system examples. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： data-driven modeling Pharmacological system Endpoint information fusion Identifiability Parametric variance

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data-driven modeling of General Fluid Density Under Subcritical and Supercritical Conditions

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AIAA JOURNAL 2023年第4期61卷 1519-1531页

作者： Zhou, Mingshuo Chen, Wei Su, Xingyu Sung, Chih-Li Wang, Xingjian Ren, Zhuyin Tsinghua Univ Dept Energy & Power Engn Beijing 100084 Peoples R China Florida Inst Technol Dept Mech & Civil Engn Melbourne FL 32901 USA Tsinghua Univ Ctr Combust Energy Beijing 100084 Peoples R China Michigan State Univ Dept Stat & Probabil E Lansing MI 48824 USA Tsinghua Univ Inst Aero Engine Beijing 100084 Peoples R China

Advanced propulsion and power-generation systems often operate under extreme conditions, where thermophysical properties of the working fluids undergo complex variations in a wide range of fluid states, where empirical cubic equations of state could yield substantial errors in density prediction. The present work develops data-driven models for accurate density estimation of general fluids across all thermodynamic regimes. The model starts with the cubic equation of state, whose alpha function is calibrated in a data-driven manner with statistical correction accounting for inherent correlations among training data samples. The developed models are examined for the representative pure substances in aerospace propulsion systems, including oxygen, nitrogen, carbon dioxide, and hydrocarbon fuels. Results show that the model with pressure and temperature as input variables provides consistently superior accuracy over wide ranges of temperatures and pressures, especially in the compressed-liquid region, where the Peng-Robinson equation of state significantly underperforms. The corresponding absolute average relative deviation for the studied substances is below 0.65% at different pressures, compared to 7.16% by the Peng-Robinson equation of state. The model is also extended to examine the density calculations of the selected binary and ternary mixtures, and the consistent result is obtained. The data-driven approach can be adopted to evaluate other thermodynamic properties of fluids and fluid mixtures and characteristics of vapor-liquid equilibrium, and further incorporated into large-scale multiphysics simulations where nonideal gas behavior occurs in the future.

关键词： Thermodynamic Properties Kernel Functions supercritical fluid transcritical fluid data-driven modeling Peng-Robinson equation of state Guassian process regression liquid propellants Near-critical behavior high-pressure properties

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data-driven modeling and fast adjustment for digital coded metasurfaces database: Application in adaptive electromagnetic energy harvesting

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APPLIED ENERGY 2024年 365卷

作者： Liu, Cheng Wang, Wei Wang, Zhixia Ding, Bei Wu, Zhiqiang Feng, Jingjing Tianjin Univ Sch Mech Engn Tianjin 300350 Peoples R China Tianjin Key Lab Nonlinear Dynam & Control Tianjin 300350 Peoples R China Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China

Metasurfaces (MSs) show great promise in efficient electromagnetic energy harvesting (EMEH) due to their compactness, high efficiency, and long -distance transmission capabilities. Nonetheless, the conventional iterative and time-consuming solving process of MSs significantly escalates computational demands. Furthermore, once processed, the MS shape remains fixed and cannot be adapted to changing requirements. Accordingly, a critical challenge is the development of a new efficient solver for MS real -time tuning. Here, we introduce a class of digital coded MS databases including multiple pre-defined resonant frequency MS. The combination of multiple MS base functions from the database enables swift resonance frequency adjustments to adapt to changing environmental conditions. A topology optimization method based on data-driven modeling is employed to rapidly acquire the optimal digital coding for the corresponding MS at various operating frequencies, facilitating the construction of a database. This approach integrates a convolutional neural network and genetic algorithm (CNNGA). It not only enables more accurate and expedited forward prediction of MSs' electromagnetic (EM) response but also facilitates inverse design based on specified requirements. We employ this method to design a MS that achieves perfect energy harvesting (EH) over a broad range of incident angles and polarization directions. In addition, a data -driven modeling is used to establish an EH efficiency predictive model corresponding to MS combination. This model serves as a guide for real-time MS adjustments as per changing requirements. Compared to previously designed MSs, this model achieves rapid design and adaptive adjustment capabilities. Through the incorporation of various functional MS base functions into the database, this method can be universally applied to MS combinations tailored to specific functions, including EM cloaking, ultra -thin flat lenses, and computational MSs.

关键词： Digital coded metasurface database data-driven modeling Rapid design Adaptive adjustment

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data-driven modeling for Gait Phase Recognition in a Wearable Exoskeleton Using Estimated Forces

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IEEE TRANSACTIONS ON ROBOTICS 2023年第4期39卷 3072-3086页

作者： Park, Kyeong-Won Choi, Jungsu Kong, Kyoungchul Republ Korea AF Acad Dept Mech Engn Cheongju 28187 South Korea Yeungnam Univ Dept Robot Engn Gyongsan 712749 South Korea Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea

Accurate identification of gait phases is critical in effectively assessing the assistance provided by lower limb exoskeletons. In this study, we propose a novel gait phase recognition system called ObsNet to analyze the gait of individuals with spinal cord injuries (SCI). To ensure the reliable use of exoskeletons, it is essential to maintain practicality and avoid exposing the system to unnecessary risks of fatigue, inaccuracy, or incompatibility with human-centered devices. Therefore, we propose a new approach to characterize exoskeletal-assisted gait by estimating forces on exoskeletal joints during walking. Although these estimated forces are potentially useful for detecting gait phases, their nonlinearities make it challenging for existing algorithms to generalize accurately. To address this challenge, we introduce a data-driven model that simultaneously captures both feature extraction and order dependencies, and enhance its performance through a threshold-based compensational method to filter out momentary errors. We evaluated the effectiveness of ObsNet through robotic walking experiments with two practical users with complete paraplegia. Our results indicate that ObsNet outperformed state-of-the-art methods that use joint information and other recurrent networks in identifying the gait phases of individuals with SCI (p< 0.05). We also observed reliable imitation of ground truth after compensation. Overall, our research highlights the potential of wearable technology to improve the daily lives of individuals with disabilities through accurate and stable state assessment.

关键词： Robots Legged locomotion Exoskeletons Robot sensing systems Trajectory Force Dynamics data-driven modeling gait phase recognition sensorless force estimation wearable robotics

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