As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior with...This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.展开更多
The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at ...The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer （DSSAT） model and the Agro- ecological Zone （AEZ） model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations （1962-1999） are employed to estimate average crop productivity. Comparison of the two models＇ estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.展开更多
Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operat...Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.展开更多
This paper proposes a novel intelligent fault detection method,i.e.,combining the subspace identification technique(SIT)with the data-model interactive compensation mechanism for fault detection.More precisely,the des...This paper proposes a novel intelligent fault detection method,i.e.,combining the subspace identification technique(SIT)with the data-model interactive compensation mechanism for fault detection.More precisely,the design process is divided into two primary stages:offline learning process and real-time fault monitoring.In the offline learning phase,the fault isolation algorithm is employed,along with parameter identification using the subspace identification technique.Then the online data undergo normalization processing and are subjected to online fault detection using a trained offline model.In addition,considering the accuracy of fault detection,this paper designs the dynamic optimal threshold based on the fault detection rate(FDR)and the false alarm rate(FAR).In the end,the case studies on PT700 and Tennessee Eastman(TE)process data are provided to confirm that the proposed method achieves the fault detection objectives.展开更多
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
基金supported by the Fundamental Research Fund for the Central Universities(Grant No.BLX202226)。
文摘This paper presents a method for measuring stress fields within the framework of coupled data models,aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region.This approach contributes to advancing the field of intrinsic equation-free mechanics.The methodology combines measured strain fields with data-model coupling driven algorithms.The gradient and Canny operators are utilized to process the strain field data,enabling the determination of the deterioration region's location.Meanwhile,an adaptive model building method is proposed for constructing coupling driven models.To address the issue of unknown datasets during computation,a dataset updating strategy based on a differential evolutionary algorithm is introduced.The resulting optimal dataset is then used to generate stress field results.Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.
文摘The response of the agro-ecological system to the environment includes the response of individual crop＇s physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer （DSSAT） model and the Agro- ecological Zone （AEZ） model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations （1962-1999） are employed to estimate average crop productivity. Comparison of the two models＇ estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.
文摘Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.
基金supported by the National Natural Science Foundation of China(Nos.62273058 and U22A2045)the Youth Science and Technology Innovation and Entrepreneurship Outstanding Talents Project of Jilin Province(No.20230508043RC)the Industrial Technology and Development Special Project of Jilin Provincial Development and Reform Commission(No.2024C008-2).
文摘This paper proposes a novel intelligent fault detection method,i.e.,combining the subspace identification technique(SIT)with the data-model interactive compensation mechanism for fault detection.More precisely,the design process is divided into two primary stages:offline learning process and real-time fault monitoring.In the offline learning phase,the fault isolation algorithm is employed,along with parameter identification using the subspace identification technique.Then the online data undergo normalization processing and are subjected to online fault detection using a trained offline model.In addition,considering the accuracy of fault detection,this paper designs the dynamic optimal threshold based on the fault detection rate(FDR)and the false alarm rate(FAR).In the end,the case studies on PT700 and Tennessee Eastman(TE)process data are provided to confirm that the proposed method achieves the fault detection objectives.
