Pub Date : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613092
Pang Xin-yu, Tong Yu, Zhang Bo-wen, Wei Ji-gui
The vibration signal of rolling bearing has non-stationary and nonlinear characteristics. In order to apply the advantages of deep learning recognition of 2-D images to the fault diagnosis of rolling bearings, a multi-layer nested scatter plot-convolutional neural network (NSP-CNN) rolling bearing fault diagnosis model is proposed. The model uses fast Fourier transform to obtain the frequency spectrum of the vibration signal in different directions, and divides the frequency bands. After that, the signals of different bandwidths are given different colors to highlight the fault information of the rolling bearing. Finally, the combined NSP The optimized CNN model is input to the feature map to realize fault diagnosis. The results show that the model can achieve high diagnostic accuracy when diagnosing rolling bearing faults.
{"title":"NSP-CNN Rolling Bearing Fault Diagnosis Method","authors":"Pang Xin-yu, Tong Yu, Zhang Bo-wen, Wei Ji-gui","doi":"10.1109/PHM-Nanjing52125.2021.9613092","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613092","url":null,"abstract":"The vibration signal of rolling bearing has non-stationary and nonlinear characteristics. In order to apply the advantages of deep learning recognition of 2-D images to the fault diagnosis of rolling bearings, a multi-layer nested scatter plot-convolutional neural network (NSP-CNN) rolling bearing fault diagnosis model is proposed. The model uses fast Fourier transform to obtain the frequency spectrum of the vibration signal in different directions, and divides the frequency bands. After that, the signals of different bandwidths are given different colors to highlight the fault information of the rolling bearing. Finally, the combined NSP The optimized CNN model is input to the feature map to realize fault diagnosis. The results show that the model can achieve high diagnostic accuracy when diagnosing rolling bearing faults.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128098091","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9613116
Zhang Jian, Qian Jia-jia, Zha Hai-yan
In recent years, due to the growth in the number of vehicles and traffic facilities construction lag, traffic safety problems and congestion problems become increasingly prominent. Traffic intelligentization, alleviates the problem of congestion, travel more convenient become the focus of attention. In order to solve the above problems, a multi-objective path planning system based on ZigBee technology is designed. In the intercepted road network planning area, the similarity measure conditions between the path paths are determined, and then the congestion coefficient is calibrated by combining the multi-objective optimization idea. Based on this, an electronic map document is established, and a suitable database host model is selected by using the MapX development tool. The path planning system based on ZigBee is designed. Simulation experiments are set up to highlight the practical application value of multi-objective optimization path planning system by comparing with the shortest path and the shortest traditional time.
{"title":"Design of Multiobjective Path Planning System for Intelligent Transportation Based on ZigBee Technology","authors":"Zhang Jian, Qian Jia-jia, Zha Hai-yan","doi":"10.1109/PHM-Nanjing52125.2021.9613116","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9613116","url":null,"abstract":"In recent years, due to the growth in the number of vehicles and traffic facilities construction lag, traffic safety problems and congestion problems become increasingly prominent. Traffic intelligentization, alleviates the problem of congestion, travel more convenient become the focus of attention. In order to solve the above problems, a multi-objective path planning system based on ZigBee technology is designed. In the intercepted road network planning area, the similarity measure conditions between the path paths are determined, and then the congestion coefficient is calibrated by combining the multi-objective optimization idea. Based on this, an electronic map document is established, and a suitable database host model is selected by using the MapX development tool. The path planning system based on ZigBee is designed. Simulation experiments are set up to highlight the practical application value of multi-objective optimization path planning system by comparing with the shortest path and the shortest traditional time.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125442471","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 : 2021-10-15DOI: 10.1109/phm-nanjing52125.2021.9612763
{"title":"PHM-Nanjing 2021 Cover Page","authors":"","doi":"10.1109/phm-nanjing52125.2021.9612763","DOIUrl":"https://doi.org/10.1109/phm-nanjing52125.2021.9612763","url":null,"abstract":"","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122223567","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612906
Jian-Fei Zheng, Qing Dong, Changjin Hu, Xin Zhang, H. Mu
Aiming at the problem of on-demand maintenance and spare parts ordering under the condition of periodic state detection, a joint decision model of on-demand maintenance and spare parts ordering of single-component system with nonlinear adaptive Wiener process considering the influence of state detection is proposed. Firstly, nonlinear adaptive Wiener process and normal distribution are used to describe the effects of natural degradation process and periodic state detection on equipment degradation, respectively, and the probability distribution of remaining useful life is derived under the first arrival time. Based on the prediction results of remaining useful life, a joint decision-making model of condition-based maintenance and spare parts ordering is established, which aims at minimizing the long-term average cost of components. Finally, an example is given to verify the effectiveness of the proposed method.
{"title":"Condition-based Maintenance and Spare Parts Ordering Strategy Considering the Influence of Condition Detection","authors":"Jian-Fei Zheng, Qing Dong, Changjin Hu, Xin Zhang, H. Mu","doi":"10.1109/PHM-Nanjing52125.2021.9612906","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612906","url":null,"abstract":"Aiming at the problem of on-demand maintenance and spare parts ordering under the condition of periodic state detection, a joint decision model of on-demand maintenance and spare parts ordering of single-component system with nonlinear adaptive Wiener process considering the influence of state detection is proposed. Firstly, nonlinear adaptive Wiener process and normal distribution are used to describe the effects of natural degradation process and periodic state detection on equipment degradation, respectively, and the probability distribution of remaining useful life is derived under the first arrival time. Based on the prediction results of remaining useful life, a joint decision-making model of condition-based maintenance and spare parts ordering is established, which aims at minimizing the long-term average cost of components. Finally, an example is given to verify the effectiveness of the proposed method.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435484","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}
In view of the high development cost of the Long March series of launch vehicle and the difficulty of implementing reliability tests, a small sample reliability sampling method based on composite equivalency Bayesian fusion is proposed. First, in order to avoid large amounts of prior data submerging the field data with small sample, a comprehensive use of physical equivalency credibility and data compatibility test are used to fully integrate multi-source test data. Then, according to Bayesian theory, the reliability of the fusion data of the launch vehicle that obeys the normal distribution is statistically verified under the complex assumptions. Finally, considering the experimental cost and the constraints of the two types of risks, a nonlinear constraint programming model for solving the minimum sample size is established.
{"title":"Test Design of Small Sample Launch Vehicle Based on Composite Equivalency Bayesian Fusion","authors":"Q. Huangpeng, Xiaojun Duan, Wenwei Huang, Yinhui Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9612896","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612896","url":null,"abstract":"In view of the high development cost of the Long March series of launch vehicle and the difficulty of implementing reliability tests, a small sample reliability sampling method based on composite equivalency Bayesian fusion is proposed. First, in order to avoid large amounts of prior data submerging the field data with small sample, a comprehensive use of physical equivalency credibility and data compatibility test are used to fully integrate multi-source test data. Then, according to Bayesian theory, the reliability of the fusion data of the launch vehicle that obeys the normal distribution is statistically verified under the complex assumptions. Finally, considering the experimental cost and the constraints of the two types of risks, a nonlinear constraint programming model for solving the minimum sample size is established.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132497473","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612745
Hong-Ci Wu, R. Liu, Youchao Sun
Aiming at evaluating parameters with small sample numbers considering unobvious development trends and irregular fluctuations in civil aircraft system prognosties and health management, this paper proposes a grey wave prediction optimization model based on the improved grey effect amount and whitening equation. A K-value clustering method is first applied to determine main data contours to determine the intersection of the main contours and the original data waves. The grey effect amount and whitening equations in GM(1,1) prediction model are then optimized, as well as modeling and fitting the contour time sequence. The verification is performed on the A320 air conditioning system, and the model performance is analyzed. The verification and comparison analysis shows that the our improved model has a prediction accuracy of 9.33% with irregular waves, which outperforms the conventional model with accuracy of 77.8%. The proposed model presents a good fitting and can predict irregular waves which is a characteristic in the health management showing significant impacts on the civil aircraft system.
{"title":"Improvement and Application of Grey Wave Prediction Model Based on PHM of Civil Aircraft System","authors":"Hong-Ci Wu, R. Liu, Youchao Sun","doi":"10.1109/PHM-Nanjing52125.2021.9612745","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612745","url":null,"abstract":"Aiming at evaluating parameters with small sample numbers considering unobvious development trends and irregular fluctuations in civil aircraft system prognosties and health management, this paper proposes a grey wave prediction optimization model based on the improved grey effect amount and whitening equation. A K-value clustering method is first applied to determine main data contours to determine the intersection of the main contours and the original data waves. The grey effect amount and whitening equations in GM(1,1) prediction model are then optimized, as well as modeling and fitting the contour time sequence. The verification is performed on the A320 air conditioning system, and the model performance is analyzed. The verification and comparison analysis shows that the our improved model has a prediction accuracy of 9.33% with irregular waves, which outperforms the conventional model with accuracy of 77.8%. The proposed model presents a good fitting and can predict irregular waves which is a characteristic in the health management showing significant impacts on the civil aircraft system.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130068664","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612935
Hongyan Yin
Because the bridge structure health monitoring system involves multi-disciplinary knowledge, the original system has some defects such as chaotic information storage and poor monitoring precision, which seriously hinders the development of bridge structure health monitoring technology. In order to solve the above problems, the design and research of large bridge structure intelligent health monitoring hybrid information system are proposed. The hardware of the system includes the selection unit of prestressing tensioning measuring device, the selection unit of industrial control machine, the selection unit of data exchange machine and the selection unit of sensor. Through the design of the hardware unit and its software module, the intelligent health monitoring hybrid information system of large bridge structure is realized. Compared with the existing system, the experimental data show that the accuracy of bridge structure health monitoring is higher, which fully proves the effectiveness and feasibility of the design system.
{"title":"Design of Intelligent Health Monitoring Hybrid Information System for Large Bridge Structures","authors":"Hongyan Yin","doi":"10.1109/PHM-Nanjing52125.2021.9612935","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612935","url":null,"abstract":"Because the bridge structure health monitoring system involves multi-disciplinary knowledge, the original system has some defects such as chaotic information storage and poor monitoring precision, which seriously hinders the development of bridge structure health monitoring technology. In order to solve the above problems, the design and research of large bridge structure intelligent health monitoring hybrid information system are proposed. The hardware of the system includes the selection unit of prestressing tensioning measuring device, the selection unit of industrial control machine, the selection unit of data exchange machine and the selection unit of sensor. Through the design of the hardware unit and its software module, the intelligent health monitoring hybrid information system of large bridge structure is realized. Compared with the existing system, the experimental data show that the accuracy of bridge structure health monitoring is higher, which fully proves the effectiveness and feasibility of the design system.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134015205","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612904
Xinglong Zhang, Lingwei Li, Tianhong Zhang
At present, the main data source for the verification of surge detection devices still relies on the surge test of the compressor or the whole engine which makes it urgent to study the simulation methods of the whole engine surge process to replace the high-cost and high-risk surge test. To solve this problem, a turboshaft engine component level model (CLM) is established firstly and then the compressor characteristic lines are expanded in the classic Moore-Greitzer (MG) model to establish an extended MG model. Finally, a novel real-time surge model based on the surge mechanism for simulating the turboshaft engine dynamic process of surge is proposed with considering the coupling relationship between compressor’s rotor speed, mass flow and pressure of CLM and extended MG model. The simulation results show that the model can realize the whole-process simulation of the whole process of steady—surge—steady under multiple operating states of the engine. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means this model can be further applied to the simulation test research of surge detection and anti-surge control.
{"title":"Research on Real-Time Model of Turboshaft Engine with Surge Process","authors":"Xinglong Zhang, Lingwei Li, Tianhong Zhang","doi":"10.1109/PHM-Nanjing52125.2021.9612904","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612904","url":null,"abstract":"At present, the main data source for the verification of surge detection devices still relies on the surge test of the compressor or the whole engine which makes it urgent to study the simulation methods of the whole engine surge process to replace the high-cost and high-risk surge test. To solve this problem, a turboshaft engine component level model (CLM) is established firstly and then the compressor characteristic lines are expanded in the classic Moore-Greitzer (MG) model to establish an extended MG model. Finally, a novel real-time surge model based on the surge mechanism for simulating the turboshaft engine dynamic process of surge is proposed with considering the coupling relationship between compressor’s rotor speed, mass flow and pressure of CLM and extended MG model. The simulation results show that the model can realize the whole-process simulation of the whole process of steady—surge—steady under multiple operating states of the engine. The change characteristics of the rotor speed, compressor outlet pressure, mass flow, exhaust gas temperature and other parameters are consistent with the test data, which means this model can be further applied to the simulation test research of surge detection and anti-surge control.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134434155","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 : 2021-10-15DOI: 10.1109/PHM-Nanjing52125.2021.9612966
D. Zhou, Yang Liu, Junwang He, Xiaosong Yao, Uongfei Tian, Denghui Hu, Yan Cao
With the development and maturity of artificial intelligence technology, more and more space remote sensing satellites begin to use artificial intelligence technology to solve the problem of remote sensing image real-time processing. Payload image processing of remote sensing satellite is a complex system, involving process control, data acquisition, data transmission, data storage, analysis and processing. At present, the image processing process of most remote sensing satellites is roughly the same, but the requirements for image processing are different, resulting in different image processing algorithms, and then the hardware computing power requirements are different. This paper presents a design of universal high-performance space-borne intelligent processor, which has the characteristics of high performance, high reliability and redundancy, supports configurable computing power, and has strong versatility.
{"title":"A Universal High Performance Intelligent Processor Of Satellite","authors":"D. Zhou, Yang Liu, Junwang He, Xiaosong Yao, Uongfei Tian, Denghui Hu, Yan Cao","doi":"10.1109/PHM-Nanjing52125.2021.9612966","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612966","url":null,"abstract":"With the development and maturity of artificial intelligence technology, more and more space remote sensing satellites begin to use artificial intelligence technology to solve the problem of remote sensing image real-time processing. Payload image processing of remote sensing satellite is a complex system, involving process control, data acquisition, data transmission, data storage, analysis and processing. At present, the image processing process of most remote sensing satellites is roughly the same, but the requirements for image processing are different, resulting in different image processing algorithms, and then the hardware computing power requirements are different. This paper presents a design of universal high-performance space-borne intelligent processor, which has the characteristics of high performance, high reliability and redundancy, supports configurable computing power, and has strong versatility.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134554301","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}
Data-driven methods have gained great success in motor fault diagnosis. Most researches only use signals from a single sensor, which limits the diagnosis accuracy. Multi-sensor fusion methods have been studied in the past few years to enhance model performance. However, in real applications, high noise usually exists in the collected signals and sometimes some sensors may encounter unexpected failure, which will greatly influence the diagnosis accuracy. In this paper, an innovative fault diagnosis model based on multi-sensor fusion is proposed to solve the problems. The proposed model is divided into two parts: parallel physical signal denoising network and memorized credibility evidence theory. The parallel physical signal denoising network is composed of one-dimensional convolutional neural network and residual building block. The memorized credibility evidence theory is proposed based on Dempster-Shafer evidence theory, and the concept of memory credibility is introduced. Experiment on a real induction motor Multi-sensor fault dataset illustrates the superiority of proposed model compared with traditional data fusion algorithm, feature fusion algorithm and proposed model without memory credibility.
{"title":"Induction Motor Fault Diagnosis Based on Multi-Sensor Fusion Under High Noise and Sensor Failure Condition","authors":"Zhiyu Tao, Pengcheng Xia, Yixiang Huang, Dengyu Xiao, Yuxiang Wuang, Zhiwei Zhong, Chengliang Liu","doi":"10.1109/PHM-Nanjing52125.2021.9612787","DOIUrl":"https://doi.org/10.1109/PHM-Nanjing52125.2021.9612787","url":null,"abstract":"Data-driven methods have gained great success in motor fault diagnosis. Most researches only use signals from a single sensor, which limits the diagnosis accuracy. Multi-sensor fusion methods have been studied in the past few years to enhance model performance. However, in real applications, high noise usually exists in the collected signals and sometimes some sensors may encounter unexpected failure, which will greatly influence the diagnosis accuracy. In this paper, an innovative fault diagnosis model based on multi-sensor fusion is proposed to solve the problems. The proposed model is divided into two parts: parallel physical signal denoising network and memorized credibility evidence theory. The parallel physical signal denoising network is composed of one-dimensional convolutional neural network and residual building block. The memorized credibility evidence theory is proposed based on Dempster-Shafer evidence theory, and the concept of memory credibility is introduced. Experiment on a real induction motor Multi-sensor fault dataset illustrates the superiority of proposed model compared with traditional data fusion algorithm, feature fusion algorithm and proposed model without memory credibility.","PeriodicalId":436428,"journal":{"name":"2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131522075","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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