Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00070
Huixian Zhang, Xiukun Wei, Xin Li
The reliability of multi-state systems has been investigated extensively in the last decade. In this paper, the reliability analysis of multi-state system considering periodic maintenance based on PH distribution is studied. The transitions of the system between different states are analyzed. An infinitesimal generator matrix is constructed for calculating steady-state availability. Finally, a numerical example is presented to demonstrate the method. The proposed method can provide a basis for determining the optimal number of periodic maintenance for the multi-state repairable system. The model proposed in this paper can be an alternative for practical application.
{"title":"Multi-state system reliability analysis based on PH distribution for periodic maintenance","authors":"Huixian Zhang, Xiukun Wei, Xin Li","doi":"10.1109/PHM58589.2023.00070","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00070","url":null,"abstract":"The reliability of multi-state systems has been investigated extensively in the last decade. In this paper, the reliability analysis of multi-state system considering periodic maintenance based on PH distribution is studied. The transitions of the system between different states are analyzed. An infinitesimal generator matrix is constructed for calculating steady-state availability. Finally, a numerical example is presented to demonstrate the method. The proposed method can provide a basis for determining the optimal number of periodic maintenance for the multi-state repairable system. The model proposed in this paper can be an alternative for practical application.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122451695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00037
Yingshun Li, A. Yu, Shiming Liu, Si Zhang
Fault prediction and health management (PHM) is a system engineering discipline extracted from the engineering field, and constantly systematized, focusing on the monitoring, prediction and management of complex engineering health status. It plays an important role in reducing the maintenance cost of fire control equipment, improving the integrity of the fire control system and improving the management efficiency of the fire control system. This paper introduces the key technologies in fault prediction and health management, and studies and probes into the structure of fault prediction and health management system.
{"title":"Fire Control System Fault Prediction and Health Management Related Technology","authors":"Yingshun Li, A. Yu, Shiming Liu, Si Zhang","doi":"10.1109/PHM58589.2023.00037","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00037","url":null,"abstract":"Fault prediction and health management (PHM) is a system engineering discipline extracted from the engineering field, and constantly systematized, focusing on the monitoring, prediction and management of complex engineering health status. It plays an important role in reducing the maintenance cost of fire control equipment, improving the integrity of the fire control system and improving the management efficiency of the fire control system. This paper introduces the key technologies in fault prediction and health management, and studies and probes into the structure of fault prediction and health management system.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123036238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00059
Yingshun Li, Tao Qiu, Huanhuan Sui, De-biao Wang
The emergence of deep learning and transfer learning techniques has provided new ideas and methods for the detection and prediction of faults in complex systems. In practical engineering, the integrated transmission device plays a crucial role as an important transmission component, and its fault detection is essential to ensure the normal operation of tracked vehicles. This article will introduce the application of deep transfer learning in the fault detection of integrated transmission devices, as well as the concepts of deep learning and transfer learning. Then, we will discuss the background and challenges of fault detection in integrated transmission devices. Based on a simple model experiment, we will summarize this article and look forward to future research directions.
{"title":"Application of Deep Transfer Learning in Fault Diagnosis of Integrated Transmission","authors":"Yingshun Li, Tao Qiu, Huanhuan Sui, De-biao Wang","doi":"10.1109/PHM58589.2023.00059","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00059","url":null,"abstract":"The emergence of deep learning and transfer learning techniques has provided new ideas and methods for the detection and prediction of faults in complex systems. In practical engineering, the integrated transmission device plays a crucial role as an important transmission component, and its fault detection is essential to ensure the normal operation of tracked vehicles. This article will introduce the application of deep transfer learning in the fault detection of integrated transmission devices, as well as the concepts of deep learning and transfer learning. Then, we will discuss the background and challenges of fault detection in integrated transmission devices. Based on a simple model experiment, we will summarize this article and look forward to future research directions.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131996551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00025
Quan Qian, Yi Qin, Zhengyi Wang, Tumsa Tola Bekele
Lots of fault transfer diagnosis methods have been presented to bring the gap between source domain and target domain. Nevertheless, most of them only pay attention to the marginal domain adaptation (MDA), while ignoring the conditional domain adaptation (CDA) of class levels. Additionally, the universal CDA mechanisms greatly rely on the quality of pseudo label of target-domain samples. To deal with above issues, an enhanced deep joint distribution alignment (DJDA) mechanism is proposed to comprehensively achieve the MDA and CDA. In DJDA, a new MDA distribution discrepancy metric, including the mean and covariance information of two domains, is constructed. Meanwhile, a new CDA mechanism based on unsupervised clustering and Wasserstein distance is built to align the class-wise distribution of two domains, in which the pseudo label is needless. Experimental results evaluate the efficacy and advantage of proposed DJDA.
{"title":"An enhanced deep joint distribution alignment mechanism for planetary gearbox fault transfer diagnosis","authors":"Quan Qian, Yi Qin, Zhengyi Wang, Tumsa Tola Bekele","doi":"10.1109/PHM58589.2023.00025","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00025","url":null,"abstract":"Lots of fault transfer diagnosis methods have been presented to bring the gap between source domain and target domain. Nevertheless, most of them only pay attention to the marginal domain adaptation (MDA), while ignoring the conditional domain adaptation (CDA) of class levels. Additionally, the universal CDA mechanisms greatly rely on the quality of pseudo label of target-domain samples. To deal with above issues, an enhanced deep joint distribution alignment (DJDA) mechanism is proposed to comprehensively achieve the MDA and CDA. In DJDA, a new MDA distribution discrepancy metric, including the mean and covariance information of two domains, is constructed. Meanwhile, a new CDA mechanism based on unsupervised clustering and Wasserstein distance is built to align the class-wise distribution of two domains, in which the pseudo label is needless. Experimental results evaluate the efficacy and advantage of proposed DJDA.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131038663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00065
Xuan Zhou, M. Dziendzikowski, K. Dragan, Leiting Dong, M. Giglio, C. Sbarufatti
The structural digital twin is a virtual representation of physical entities that accurately predicts the evolution of structural damage through multidisciplinary and multi-level probabilistic simulations. It provides crucial support for prognostic and health management. Flight parameters are important input data for airframe digital twin to support aerodynamic and structural simulations. However, many small aircraft or UAVs often suffer from insufficient sampling rates of flight parameters due to cost limitation or premature service. In this study, we propose a deep learning-based flight data upsampling method that effvbectively enhances the resolution of flight data. The method constructs an upsampling model using a one-dimensional super-resolution convolutional residual network, defines multiple loss functions associated with the flight data, and uses a highly sampled test aircraft dataset for training. The proposed method is validated using real UAV flight test data and several criteria, achieving good results with different upsampling factors. This approach is expected to facilitate the construction of structural digital twins in the future.
{"title":"Generating High-Resolution Flight Parameters in Structural Digital Twins Using Deep Learning-based Upsampling","authors":"Xuan Zhou, M. Dziendzikowski, K. Dragan, Leiting Dong, M. Giglio, C. Sbarufatti","doi":"10.1109/PHM58589.2023.00065","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00065","url":null,"abstract":"The structural digital twin is a virtual representation of physical entities that accurately predicts the evolution of structural damage through multidisciplinary and multi-level probabilistic simulations. It provides crucial support for prognostic and health management. Flight parameters are important input data for airframe digital twin to support aerodynamic and structural simulations. However, many small aircraft or UAVs often suffer from insufficient sampling rates of flight parameters due to cost limitation or premature service. In this study, we propose a deep learning-based flight data upsampling method that effvbectively enhances the resolution of flight data. The method constructs an upsampling model using a one-dimensional super-resolution convolutional residual network, defines multiple loss functions associated with the flight data, and uses a highly sampled test aircraft dataset for training. The proposed method is validated using real UAV flight test data and several criteria, achieving good results with different upsampling factors. This approach is expected to facilitate the construction of structural digital twins in the future.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00033
Hongwei Wang, Liu Tang, You Li
Aiming at the simulation acceleration requirements of a kind of FPGA design with external memory chips, this paper studies on the FPGA software and hardware combined simulation acceleration platform, puts forward a general memory chip interface conversion idea, takes SRAM as a specific example to illustrate the conversion method, and verifies the correctness of the method through the physical simulation of the software and hardware combined simulation acceleration platform, The applicability of this method is given by influence domain analysis. This method has strong adaptability, so that more FPGA design under test (DUT) can run on the simulation acceleration platform, to improve the efficiency of simulation verification.
{"title":"Research on simulation acceleration method of FPGA design with external memory chip","authors":"Hongwei Wang, Liu Tang, You Li","doi":"10.1109/PHM58589.2023.00033","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00033","url":null,"abstract":"Aiming at the simulation acceleration requirements of a kind of FPGA design with external memory chips, this paper studies on the FPGA software and hardware combined simulation acceleration platform, puts forward a general memory chip interface conversion idea, takes SRAM as a specific example to illustrate the conversion method, and verifies the correctness of the method through the physical simulation of the software and hardware combined simulation acceleration platform, The applicability of this method is given by influence domain analysis. This method has strong adaptability, so that more FPGA design under test (DUT) can run on the simulation acceleration platform, to improve the efficiency of simulation verification.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122414846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00069
F. Harrou, Benamar Bouyeddou, Ying Sun
This study proposes a machine learning-based approach for detecting sensor faults in wind turbines. The approach combines the Gaussian process regression (GPR) model and the Exponentially Weighted Moving Average (EWMA) monitoring chart, which provides sensitivity in detecting small shifts in the process mean. The detection threshold is computed using Kernel Density Estimation, which adds flexibility to the EWMA chart. We adopted Bayesian optimization to optimize the hyperparameters of the GPR model based on anomaly-free data. The proposed approach is tested on different sensor faults and compared with support Vector regression-based methods. The results show that the proposed approach effectively detects various types of sensor faults, including sensor faults in pitch angle measurement and generator speed measurement, and outperforms the support Vector regression-based approach.
{"title":"Sensor Fault Detection in Wind Turbines Using Machine Learning and Statistical Monitoring Chart","authors":"F. Harrou, Benamar Bouyeddou, Ying Sun","doi":"10.1109/PHM58589.2023.00069","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00069","url":null,"abstract":"This study proposes a machine learning-based approach for detecting sensor faults in wind turbines. The approach combines the Gaussian process regression (GPR) model and the Exponentially Weighted Moving Average (EWMA) monitoring chart, which provides sensitivity in detecting small shifts in the process mean. The detection threshold is computed using Kernel Density Estimation, which adds flexibility to the EWMA chart. We adopted Bayesian optimization to optimize the hyperparameters of the GPR model based on anomaly-free data. The proposed approach is tested on different sensor faults and compared with support Vector regression-based methods. The results show that the proposed approach effectively detects various types of sensor faults, including sensor faults in pitch angle measurement and generator speed measurement, and outperforms the support Vector regression-based approach.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00026
Yingshun Li, Na Li, Zhannan Guo, Haiyang Liu
With the development of science and technology, the technology of tank fire control system is also being iteratively updated. At this stage, the fire control system shows the characteristics of higher technical content, more complex structure, more advanced control system, and more difficult fault judgment. Aiming at the problems of small amount of signal data and complex composition collected by artillery control system, a model prediction method based on chaotic mapping improved aquila algorithm optimization support vector machine is proposed. The gray correlation degree analysis is carried out through the collected signal data, the original data parameters are screened, and the attributes with higher gray correlation degree are selected to construct the dataset. The improved aquila algorithm of chaos mapping is used to perform parameter optimization on the penalty factor c and kernel function g of the support vector machine, and after the model training is completed, the failure prediction is performed on the test set. The test shows that the improved prediction model has high prediction accuracy, stable performance, low dependence on the number of sample training sets, and strong advantages.
{"title":"Fire Control System Fault Prediction Method Based on CAO-SVM","authors":"Yingshun Li, Na Li, Zhannan Guo, Haiyang Liu","doi":"10.1109/PHM58589.2023.00026","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00026","url":null,"abstract":"With the development of science and technology, the technology of tank fire control system is also being iteratively updated. At this stage, the fire control system shows the characteristics of higher technical content, more complex structure, more advanced control system, and more difficult fault judgment. Aiming at the problems of small amount of signal data and complex composition collected by artillery control system, a model prediction method based on chaotic mapping improved aquila algorithm optimization support vector machine is proposed. The gray correlation degree analysis is carried out through the collected signal data, the original data parameters are screened, and the attributes with higher gray correlation degree are selected to construct the dataset. The improved aquila algorithm of chaos mapping is used to perform parameter optimization on the penalty factor c and kernel function g of the support vector machine, and after the model training is completed, the failure prediction is performed on the test set. The test shows that the improved prediction model has high prediction accuracy, stable performance, low dependence on the number of sample training sets, and strong advantages.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129187108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00014
Zhikai Xing, Yigang He
In recent years, researchers have presented numerous deep learning (DL) approaches to provide reliable prediction of remaining useful life (RUL) in prognostic and health management (PHM) applications. Although supervised DL approaches, such as gated recurrent unite, long-short term memory have overcome RUL prediction technology, these methods are still dependent on certainty data. Concerning real-life PHM applications, the machine learning-based RUL prediction of the power transformer methods is still in the initial phase. To solve this issue, this paper presented improved neural controlled differential equations for RUL prediction of power transformers. First, the multi-scale entropy and K-means are used to calculate the health confidence of the power transformer based on the vibration signal. Then, the cross-attention mechanism improves the feature extraction ability of neural controlled differential equation to overcome the influence of uncertain phenomena. Finally, the RUL of the power transformers is obtained by the health index formula. The advantages of the presented approach have been verified on the vibration data from 13 real power transformers. The presented approach compares with the different RUL prediction methods and obtains a stronger performance than the comparison algorithms. Contrastive results demonstrate that the presented approach obtains an accurate RUL of the power transformer online. Moreover, the accuracy of RUL prediction achieves 0.0523.
{"title":"Improved Neural Controlled Differential Equation for Remaining Useful Life Prediction of Power Transformers","authors":"Zhikai Xing, Yigang He","doi":"10.1109/PHM58589.2023.00014","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00014","url":null,"abstract":"In recent years, researchers have presented numerous deep learning (DL) approaches to provide reliable prediction of remaining useful life (RUL) in prognostic and health management (PHM) applications. Although supervised DL approaches, such as gated recurrent unite, long-short term memory have overcome RUL prediction technology, these methods are still dependent on certainty data. Concerning real-life PHM applications, the machine learning-based RUL prediction of the power transformer methods is still in the initial phase. To solve this issue, this paper presented improved neural controlled differential equations for RUL prediction of power transformers. First, the multi-scale entropy and K-means are used to calculate the health confidence of the power transformer based on the vibration signal. Then, the cross-attention mechanism improves the feature extraction ability of neural controlled differential equation to overcome the influence of uncertain phenomena. Finally, the RUL of the power transformers is obtained by the health index formula. The advantages of the presented approach have been verified on the vibration data from 13 real power transformers. The presented approach compares with the different RUL prediction methods and obtains a stronger performance than the comparison algorithms. Contrastive results demonstrate that the presented approach obtains an accurate RUL of the power transformer online. Moreover, the accuracy of RUL prediction achieves 0.0523.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125109104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/PHM58589.2023.00020
Qingluan Guan, Xiukun Wei
Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.
{"title":"The Statistical Data-driven Remaining Useful Life Prediction—A Review on the Wiener Process-based Method","authors":"Qingluan Guan, Xiukun Wei","doi":"10.1109/PHM58589.2023.00020","DOIUrl":"https://doi.org/10.1109/PHM58589.2023.00020","url":null,"abstract":"Prognostics and health management (PHM) is a core technology in the domain of reliability, and it has got extensive acclamation and application. The statistical data-driven method prediction method has become a popular hotspot of research in recent years since it only considers the condition monitoring data and relevant degradation information. As one of the data-driven remaining useful life (RUL) prediction methods, the Wiener process-based method is commonly used. Considering the uncertainty existing in the degradation process for the equipment or device, this paper summarizes the statistical data-driven method and focuses on the Wiener process-based method. Finally, some urgent issues to be addressed in the future are discussed.","PeriodicalId":196601,"journal":{"name":"2023 Prognostics and Health Management Conference (PHM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126103571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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