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A data-driven modeling Method for Stochastic Nonlinear Degradation Process With Application to RUL Estimation

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第6期52卷 3847-3858页

作者： Zhang, Yuhan Yang, Ying Li, He Xiu, Xianchao Liu, Wanquan Peking Univ Coll Engn Dept Mech & Engn Sci State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Guangdong Peoples R China

This article proposes a novel modeling method for the stochastic nonlinear degradation process by using the relevance vector machine (RVM), which can describe the nonlinearity of degradation process more flexibly and accurately. Compared with the existing methods, where degradation processes are modeled as the Wiener process with a nonlinear drift function formulized as the power law or exponential law, this kind of modeling method can characterize degradation processes with more nonlinear behavior. Instead of modeling the drift coefficient of the Wiener process directly, the weighted combination of basis functions is utilized to express the increment of the Wiener process and the parameters are calculated by a sparse Bayesian learning algorithm. Based on the proposed model, a numerical approximation formula for the probability density function (PDF) of the remaining useful life (RUL) is derived. Finally, comparison studies, including a numerical simulation and a practical case, are provided to demonstrate the effectiveness and the accuracy of the proposed methods for RUL estimation.

关键词： Degradation Stochastic processes Probability density function modeling Bayes methods data-driven modeling Support vector machines data driven degradation process remaining useful life (RUL)

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data-driven modeling and Working Condition Prediction in Process Industry Production

Data-Driven Modeling and Working Condition Prediction in Pro...

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IEEE International Conference on Networking, Sensing and Control

作者： Meifang Zhang Ziqi Wang Jing Bi Haitao Yuan College of Computer Science Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

In the production process of the process industry, precise adjustment of working conditions presents a challenge due to the complexity of processes and unknown disturbances. Central control operators need to adjust setpoints based on deviations in process parameters and monitor target values to maintain system stability. However, many operating procedures excessively rely on human experience, increasing the uncertainty of the production process. In addition, the expert knowledge is not fully embedded in accumulated operations, limiting its potential in decision support. Therefore, data-driven modeling of production processes is essential for developing industrial expert systems to realize intelligent manufacturing. This work proposes a work condition prediction framework based on an Operation Mode Library (OML) to realize Working Condition Prediction (WCP), called for OML-WCP short. Taking the cement rotary kiln adjustment process as an example, a stable OML is constructed using Gaussian mixture clustering technology. Experimental results with real-life operation data of a cement plant reveal that the prediction accuracy of OML-WCP outperforms the existing methods. Moreover, the continuous accumulation of operating mode libraries can improve prediction accuracy in practical annlications.

关键词： Industries Employee welfare data-driven modeling Accuracy Process control Production Predictive models Feature extraction Libraries Complexity theory

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data-driven modeling to predict adsorption of hydrogen on shale kerogen: Implication for underground hydrogen storage

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INTERNATIONAL JOURNAL OF COAL GEOLOGY 2023年 280卷

作者： Kalam, Shams Arif, Muhammad Raza, Arshad Lashari, Najeebullah Mahmoud, Mohamed Khalifa Univ Dept Chem & Petr Engn Abu Dhabi U Arab Emirates King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dept Petr Engn Dhahran 31261 Saudi Arabia Univ Teknol PETRONAS Dept Petr Engn Seri Iskandar Perak Darul Rid Malaysia

The interaction of hydrogen in shale gas formations holds significant interest for long-term subsurface hydrogen storage. Accurately and rapidly predicting hydrogen adsorption in these formations is crucial for assessing un-derground hydrogen storage potential. Many laboratory experiments and molecular simulations have been conducted to determine hydrogen adsorption. However, laboratory experiments and molecular simulations require complex setups and extensive calculations, which can be time-consuming. Consequently, end-users may prefer quick and accurate prediction of hydrogen adsorption to reduce the experimental and computational burden. This study introduces a novel model for predicting hydrogen adsorption using gradient boosting regression and available molecular simulation data from the literature. The data-driven model predicts hydrogen adsorption on kerogen structures based on pressure, temperature, adsorbed methane, hydrogen-to-carbon ratio, oxygen-to-carbon ratio, and kerogen density. We compared gradient-boosting regression with other machine learning tools, including artificial neural networks, symbolic regression assisted with genetic programming, decision trees, and random forests in terms of their capability to predict H2 adsorption on shale kerogen. A simple mathematical equation based on symbolic regression via genetic programming has also been provided, with training and testing coefficients of determination of 88.4% and 85.8%, respectively. However, the digital model created using gradient boosting regression outperformed all other machine learning tools, achieving a coefficient of determination of 99.6% for training data and 94.6% for testing data. A sensitivity analysis was also conducted that demonstrates the robustness of the developed model. In the case of kerogen type A, the order of increasing hydrogen adsorption is KIA < KIIA

关键词： Hydrogen adsorption Kerogen Shale gas reservoirs Machine learning data-driven modeling Underground hydrogen storage

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data-driven modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin

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ENERGIES 2023年第20期16卷 7032页

作者： Park, Hyang-A Byeon, Gilsung Son, Wanbin Kim, Jongyul Kim, Sungshin Korea Electrotechnol Res Inst Energy Platform Res Ctr Gwangju 61751 South Korea Pusan Natl Univ Sch Elect Engn Pusan 46241 South Korea

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building's heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model's goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system.

关键词： virtual power plant (VPP) heating, ventilation, and air conditioning (HVAC) digital twin data-driven modeling artificial neural networks (ANNs)

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data-driven modeling and Simulation Approach for Energy Demand Prediction of Off-Road Electric Truck: *Note: Sub-Titles are not Captured in Xplore and Should not be Used

Data-Driven Modeling and Simulation Approach for Energy Dema...

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Annual IEEE Systems Conference

作者： Hussein A. Taha David Holt Abdelhamid Mammeri Wei Huang Intelligent Transportation System National Research Council Canada Ottawa ON Canada Vehicle Structural Dynamics National Research Council Canada Ottawa ON Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

As the adoption of electrical transportation continues to rise, understanding and addressing performance degradation becomes increasingly critical to ensuring reliability and efficiency. This paper proposes a data-driven modeling and simulation approach for off-road electric trucks (e-truck). It employs a comprehensive simulation framework incorporating vehicle dynamics, terrain variability, and operational conditions to predict energy consumption with high precision. The model includes representations of the control system, electrical motor, regenerative energy mechanisms, and vehicle dynamics. This approach links the e-truck physical modeling to the realistic input data such as road conditions, truck loading, and driving behavior to provide valuable insights into the realistic operational performance of the e-truck. The developed approach addresses two main simulation models. The first one is the Terra-mechanics model that is developed in the Vortex environment to estimate the e-truck rolling resistance for different soil types. The second is the energy model, which is designed in Maplesim to estimate the instantaneous power demand and the energy required to perform a mining cycle. In the specific mining case study, rolling resistance and e-truck loading are the top sensitive variables in the energy prediction model. The fully loaded e-truck consumes 52.5% energy more than the empty truck, on average with 5% rolling resistance. The estimated energy demand increases by 55.9% for each percentage increase of rolling resistance, on average with the maximum e-truck loading. Detailed route simulation and sensitivity analysis were reported.

关键词： Resistance data-driven modeling Energy consumption Roads Loading Predictive models Soil Reliability Vehicle dynamics Load modeling

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data-driven modeling and analysis of nonlinear isolated mechanical

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 204卷

作者： Gupta, Sunit Kumar Bukhari, Mohammad A. Barry, Oumar R. Okwudire, Chinedum Virginia Tech Blacksburg Dept Mech Engn Blacksburg VA 24061 USA Wayne State Univ Dept Mech Engn Detroit MI 48202 USA Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

This study aims to identify nonlinearities in the stiffness and damping characteristics of a passive pneumatic isolator connected to an ultra-precision manufacturing prototype in the uncoupled transverse and horizontal directions. Experimental data are collected using sine sweep tests and is used to plot nonlinear frequency response functions. The resulting analysis reveals a quadratic polynomial relationship between the equivalent stiffness and the system's dynamic response, indicating a combination of quadratic and cubic nonlinearity. Similarly, the nonlinear damping characteristic is determined using the half-power bandwidth method, which exhibits a quadratic polynomial relationship between the equivalent damping and dynamic response. The linear stability analysis of the system for the identified nonlinear stiffness and damping characteristics reveals that the system remains linearly stable for the given range of frequency and excitation amplitudes. Furthermore, bifurcation analysis reveals the existence of complex solutions, such as period-2 and period-4 solutions, for higher excitation amplitudes.

关键词： System identification Stability analysis data-driven modeling Bifurcation analysis

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data-driven modeling Methods and Techniques for Pharmaceutical Processes

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PROCESSES 2023年第7期11卷

作者： Dong, Yachao Yang, Ting Xing, Yafeng Du, Jian Meng, Qingwei Dalian Univ Technol Inst Chem Proc Syst Engn Sch Chem Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Pharmaceut Sci & Technol State Key Lab Fine Chem Dalian 116024 Peoples R China

As one of the most influential industries in public health and the global economy, the pharmaceutical industry is facing multiple challenges in drug research, development and manufacturing. With recent developments in artificial intelligence and machine learning, data-driven modeling methods and techniques have enabled fast and accurate modeling for drug molecular design, retrosynthetic analysis, chemical reaction outcome prediction, manufacturing process optimization, and many other aspects in the pharmaceutical industry. This article provides a review of data-driven methods applied in pharmaceutical processes, based on the mathematical and algorithmic principles behind the modeling methods. Different statistical tools, such as multivariate tools, Bayesian inferences, and machine learning approaches, i.e., unsupervised learning, supervised learning (including deep learning) and reinforcement learning, are presented. Various applications in the pharmaceutical processes, as well as the connections from statistics and machine learning methods, are discussed in the narrative procedures of introducing different types of data-driven models. Afterwards, two case studies, including dynamic reaction data modeling and catalyst-kinetics prediction of cross-coupling reactions, are presented to illustrate the power and advantages of different data-driven models. We also discussed current challenges and future perspectives of data-driven modeling methods, emphasizing the integration of data-driven and mechanistic models, as well as multi-scale modeling.

关键词： data-driven modeling machine learning multivariate tools pharmaceutical processes process systems engineering modeling and optimization

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Beyond Persistent Excitation: Online Experiment Design for data-driven modeling and Control

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 319-324页

作者： van Waarde, Henk J. Univ Cambridge Dept Engn Control Grp Cambridge CB2 1PZ England

This letter presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.

关键词： Trajectory Design methodology Linear systems data-driven modeling Noise measurement Adaptive systems Tuning Identification linear systems

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data-driven modeling and Control of Semicontinuous Distillation Process

Data-Driven Modeling and Control of Semicontinuous Distillat...

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American Control Conference (ACC)

作者： Sakthi Prasanth Aenugula Aswin Chandrasekar Prashant Mhaskar Thomas A. Adams Department of Chemical Engineering McMaster University Hamilton Ontario Canada Department of Energy and Process Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A semicontinuous distillation process is effectively used in the separation of a multi-component mixture with low to medium production rates. This work focuses on building a data-driven model predictive control (MPC) framework to optimize the performance of a semicontinuous process by reducing total annualized cost (TAC) per tonne of feed processed while meeting the specified product quality. A data-driven modeling technique is considered in this work because of the unavailability of a highly complex and accurate first-principle model. An Aspen Plus Dynamics simulation is used as a test bed to collect the data from the process. A multi-model framework developed by modifying the traditional subspace algorithm is adapted in the shrinking horizon MPC (SHMPC) scheme to minimize TAC per tonne of feed processed. Visual Basic for Application (VBA) is used as a third tool to communicate the inputs from MPC developed in MATLAB to the process in Aspen Plus Dynamics. The simulation results illustrate that the MPC reduced the TAC/tonne of feed by 11.4% compared to the existing PI control configuration.

关键词： data-driven modeling Visual BASIC Simulation Production Product design Mathematical models Quality assessment

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data-driven modeling of Comprehensive Academic Level and Physical/Moral Education for Students in Control Discipline

Data-driven Modeling of Comprehensive Academic Level and Phy...

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Chinese Control and Decision Conference, CCDC

作者： Ziyan Zhao Shuangru Li Shipeng Li College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Physical education and moral education, as two important components, have a profound impact on the overall development of students. This work deeply studies the relationship between physical/moral education and students’ comprehensive academic level through data-driven modeling. Based on students’ grades in physical/moral-related courses, three machine learning models, logistic regression, support vector machine and random forest, are constructed to realize the high-accuracy classification of the students having different comprehensive academic level. Experimental results show that the constructed data-driven models can achieve significant accuracy and stability in predicting students’ comprehensive academic level. It proves that physical education and moral education have obvious positive correlation with students’ comprehensive academic level, and thus highlights the importance of physical/moral education in students’ all-round development.

关键词： data-driven modeling Support vector machines Ethics Logistic regression Accuracy Education Predictive models

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