Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864857
Unai Galfarsoro, A. McCloskey, Xabier Hernández, G. Almandoz, S. Zarate, X. Arrasate
There are several fault types that may arise in electric motors decreasing both reliability and comfort. Eccentricity is one of the main faults, and therefore, tools are needed to detect the motors that do not fulfil the quality requirements. This paper proposes a methodology based on a novel experimental test bench to assess both static and dynamic eccentricities, since it generates continuous and controlled values of both types of eccentricities. A permanent magnet synchronous motor is analysed in the test bench. The unbalanced magnetic pull is measured by a force transducer located below the stator and the electromagnetic field in the air gap is determined by search coils embedded around the teeth of the stator. The experimental results show the capability of these two variables to detect the presence of eccentricities, and determine its type (static or dynamic) and level.
{"title":"Eccentricity detection procedure in electric motors by force transducer and search coils in a novel experimental test bench","authors":"Unai Galfarsoro, A. McCloskey, Xabier Hernández, G. Almandoz, S. Zarate, X. Arrasate","doi":"10.1109/DEMPED.2019.8864857","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864857","url":null,"abstract":"There are several fault types that may arise in electric motors decreasing both reliability and comfort. Eccentricity is one of the main faults, and therefore, tools are needed to detect the motors that do not fulfil the quality requirements. This paper proposes a methodology based on a novel experimental test bench to assess both static and dynamic eccentricities, since it generates continuous and controlled values of both types of eccentricities. A permanent magnet synchronous motor is analysed in the test bench. The unbalanced magnetic pull is measured by a force transducer located below the stator and the electromagnetic field in the air gap is determined by search coils embedded around the teeth of the stator. The experimental results show the capability of these two variables to detect the presence of eccentricities, and determine its type (static or dynamic) and level.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125936818","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864835
J. Saucedo-Dorantes, R. Osornio-Ríos, R. Romero-Troncoso, M. Delgado-Prieto, Francisco Arellano-Espitia
New challenges involve the development of new condition monitoring approaches to avoid unexpected downtimes and to ensure the availability of machines during operating working conditions. The feature calculation from vibrations and stator currents is one of the most common an important signal processing included in condition monitoring strategies; however, the calculation of features from only one signal alone can only detect some specific faults. Thus, disadvantages are presented if multiple faults are addressed. Aiming to avoid this issue, in this work is proposed a novel condition monitoring approach based on a hybrid feature calculation of statistical features from the available vibrations and stator current signals. Thus, the characterization of the available signals is performed by estimating a hybrid set of features, then, through the Linear Discriminant Analysis, such hybrid set of features is subjected to a dimensionality reduction procedure resulting into a 2-dimensional space. Finally, the assessment and identification of multiple faulty conditions are carried out through a Neural Network. The effectiveness of the proposed approach is validated by its application to two different experimental test benches, which makes the proposed approach feasible to be applied in industrial processes.
{"title":"Novel condition monitoring approach based on hybrid feature extraction and neural network for assessing multiple faults in electromechanical systems","authors":"J. Saucedo-Dorantes, R. Osornio-Ríos, R. Romero-Troncoso, M. Delgado-Prieto, Francisco Arellano-Espitia","doi":"10.1109/DEMPED.2019.8864835","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864835","url":null,"abstract":"New challenges involve the development of new condition monitoring approaches to avoid unexpected downtimes and to ensure the availability of machines during operating working conditions. The feature calculation from vibrations and stator currents is one of the most common an important signal processing included in condition monitoring strategies; however, the calculation of features from only one signal alone can only detect some specific faults. Thus, disadvantages are presented if multiple faults are addressed. Aiming to avoid this issue, in this work is proposed a novel condition monitoring approach based on a hybrid feature calculation of statistical features from the available vibrations and stator current signals. Thus, the characterization of the available signals is performed by estimating a hybrid set of features, then, through the Linear Discriminant Analysis, such hybrid set of features is subjected to a dimensionality reduction procedure resulting into a 2-dimensional space. Finally, the assessment and identification of multiple faulty conditions are carried out through a Neural Network. The effectiveness of the proposed approach is validated by its application to two different experimental test benches, which makes the proposed approach feasible to be applied in industrial processes.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127228279","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864877
A. Soualhi, H. Razik, G. Clerc
The reliability and safety operation of an industrial system are the main objectives of industrial companies to remain competitive in a constantly growing market. Unexpected shutdowns can often lead to physical hazards as well as economic consequences in key sectors. Hence, fault prediction emerges as an important focus of the industry. Thus, this paper aims to detail the prognostic aspect and provides a state of the art of existing data-driven prognostic methods used in the literature. This paper shows the diversity of possible prognostic methods and the choice of one among them that will define a framework for industrials.
{"title":"Data Driven Methods for the Prediction of Failures","authors":"A. Soualhi, H. Razik, G. Clerc","doi":"10.1109/DEMPED.2019.8864877","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864877","url":null,"abstract":"The reliability and safety operation of an industrial system are the main objectives of industrial companies to remain competitive in a constantly growing market. Unexpected shutdowns can often lead to physical hazards as well as economic consequences in key sectors. Hence, fault prediction emerges as an important focus of the industry. Thus, this paper aims to detail the prognostic aspect and provides a state of the art of existing data-driven prognostic methods used in the literature. This paper shows the diversity of possible prognostic methods and the choice of one among them that will define a framework for industrials.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127305605","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864913
A. Giedymin, Théodore Cherriére, F. Avcilar, U. Schäfer, F. Mazaleyrat
This paper presents a new iron wire concept for the design of transverse flux machines with reduced losses and good magnetic properties. Previous approaches have pursued the construction of the stator from two-dimensional magnetically conductive components or alternatively SMC was used, which has poor magnetic properties. Soft magnetic materials were presented and evaluated with regard to their use as suitable material for wires. Furthermore, the paper shows the analytical calculation of eddy currents in iron wires and describes a further approach for a more precise evaluation of the losses. Eventually, measurements of iron wire samples for different diameters and heat treatment are presented.
{"title":"Optimization of magnetic flux paths in transverse flux machines through the use of iron wire wound materials","authors":"A. Giedymin, Théodore Cherriére, F. Avcilar, U. Schäfer, F. Mazaleyrat","doi":"10.1109/DEMPED.2019.8864913","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864913","url":null,"abstract":"This paper presents a new iron wire concept for the design of transverse flux machines with reduced losses and good magnetic properties. Previous approaches have pursued the construction of the stator from two-dimensional magnetically conductive components or alternatively SMC was used, which has poor magnetic properties. Soft magnetic materials were presented and evaluated with regard to their use as suitable material for wires. Furthermore, the paper shows the analytical calculation of eddy currents in iron wires and describes a further approach for a more precise evaluation of the losses. Eventually, measurements of iron wire samples for different diameters and heat treatment are presented.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"51 29","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113934197","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864922
Z. Xue, M. S. Li, K. Xiahou, T. Ji, Q. Wu
Wind energy conversion system technology has attracted worldwide attention, and the condition monitoring and fault diagnosis for the system become significant issues. A data-driven fault diagnosis method is presented to detect and locate open-circuit switch faults of the back-to-back converter in permanent magnet synchronous generator (PMSG)-based wind generation system. Convolutional neural network (CNN)-based neural network is applied as a fault diagnosis method, and the dropout process is employed to deal with the over-fitting problem. Twelve sensor signals of current and voltage in the back-to-back converter in various conditions are measured. A grid-connected PMSG-based wind generation model has been built in MATLAB/Simulink to estimate the proposed algorithm. Least squares support vector machine (LSSVM) and back-propagation artificial neural network (BPANN) are applied as comparison methods. Simulation results reveals that the proposed theory has a decent performance regarding the detection and location of different faulty switches in an assembly of various operating conditions.
{"title":"A Data-Driven Diagnosis Method of Open-Circuit Switch Faults for PMSG-Based Wind Generation System","authors":"Z. Xue, M. S. Li, K. Xiahou, T. Ji, Q. Wu","doi":"10.1109/DEMPED.2019.8864922","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864922","url":null,"abstract":"Wind energy conversion system technology has attracted worldwide attention, and the condition monitoring and fault diagnosis for the system become significant issues. A data-driven fault diagnosis method is presented to detect and locate open-circuit switch faults of the back-to-back converter in permanent magnet synchronous generator (PMSG)-based wind generation system. Convolutional neural network (CNN)-based neural network is applied as a fault diagnosis method, and the dropout process is employed to deal with the over-fitting problem. Twelve sensor signals of current and voltage in the back-to-back converter in various conditions are measured. A grid-connected PMSG-based wind generation model has been built in MATLAB/Simulink to estimate the proposed algorithm. Least squares support vector machine (LSSVM) and back-propagation artificial neural network (BPANN) are applied as comparison methods. Simulation results reveals that the proposed theory has a decent performance regarding the detection and location of different faulty switches in an assembly of various operating conditions.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121462185","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864885
L. Frosini, Marcello Minervini, L. Ciceri, A. Albini
This paper presents a novel procedure to detect the most frequent faults in inverter-fed induction motors, i.e. stator short circuits and bearing defects, even in case of simultaneous presence. The procedure is based only on the analysis in the frequency domain of electromagnetic signals (one-phase stator current and stray flux around the motor), by evaluating in the experimental measurements the amplitude of the harmonic components at characteristic fault frequencies. A methodology based on high sampling frequency and filtering process allowed to distinguish not only the presence of single and multiple faults, but also the progression of these faults, from an early stage to a more serious condition.
{"title":"Multiple faults detection in low voltage inverter-fed induction motors","authors":"L. Frosini, Marcello Minervini, L. Ciceri, A. Albini","doi":"10.1109/DEMPED.2019.8864885","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864885","url":null,"abstract":"This paper presents a novel procedure to detect the most frequent faults in inverter-fed induction motors, i.e. stator short circuits and bearing defects, even in case of simultaneous presence. The procedure is based only on the analysis in the frequency domain of electromagnetic signals (one-phase stator current and stray flux around the motor), by evaluating in the experimental measurements the amplitude of the harmonic components at characteristic fault frequencies. A methodology based on high sampling frequency and filtering process allowed to distinguish not only the presence of single and multiple faults, but also the progression of these faults, from an early stage to a more serious condition.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002548","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864862
T. Garcia-Calva, D. Morinigo-Sotelo, A. García-Perez, R. Romero-Troncoso
Fault detection of rotatory machinery under nonstationary conditions is a topic increasing importance in industrial applications. Induction motors are not the exception and monitoring electric motor current has become a standard option to determine the health of several motor parts. However, in the inverter-fed motor case, the health condition of a motor is difficult to diagnose from electric motor current in certain cases. This paper explores the analysis of instantaneous speed during startup transient operation as a way to enhance the reliability for rotor fault detection. An experimental study of a rotor fault time-frequency evolution is presented. Results are promising and show high potential to overcome some important drawbacks of the classical current signature analysis to track fault-related signatures.
{"title":"Rotor Fault Detection in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transient","authors":"T. Garcia-Calva, D. Morinigo-Sotelo, A. García-Perez, R. Romero-Troncoso","doi":"10.1109/DEMPED.2019.8864862","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864862","url":null,"abstract":"Fault detection of rotatory machinery under nonstationary conditions is a topic increasing importance in industrial applications. Induction motors are not the exception and monitoring electric motor current has become a standard option to determine the health of several motor parts. However, in the inverter-fed motor case, the health condition of a motor is difficult to diagnose from electric motor current in certain cases. This paper explores the analysis of instantaneous speed during startup transient operation as a way to enhance the reliability for rotor fault detection. An experimental study of a rotor fault time-frequency evolution is presented. Results are promising and show high potential to overcome some important drawbacks of the classical current signature analysis to track fault-related signatures.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129935438","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 : 2019-08-01DOI: 10.1109/demped.2019.8864894
{"title":"Welcoome to SDEMPED 2019","authors":"","doi":"10.1109/demped.2019.8864894","DOIUrl":"https://doi.org/10.1109/demped.2019.8864894","url":null,"abstract":"","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127771832","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864879
D. A. Elvira-Ortiz, D. Morinigo-Sotelo, Á. Zorita-Lamadrid, R. Osornio-Ríos, R. Romero-Troncoso
Broken rotor bar (BRB) detection in induction motors (1M) is a challenging task because the associated failure frequencies appear near the fundamental frequency component (FFC). This identification becomes harder when the IM operates at a low frequency or with low load conditions. Therefore, techniques like motor current signature analysis may suffer on properly detecting the existence and the severity of the fault. In this sense, suppressing the FFC results helpful to improve results in the condition monitoring of IM operating at low load. This work proposes the use of a genetic algorithm for estimating and suppressing the FFC in the current signals from an IM with a BRB. Experimental results prove that the use of this technique results in better and easier identification of BRB even when the motor works at low frequency or with a low load.
{"title":"Genetic Algorithm Methodology for Broken Bar Detection in Induction Motor at Low Frequency and Load Operation","authors":"D. A. Elvira-Ortiz, D. Morinigo-Sotelo, Á. Zorita-Lamadrid, R. Osornio-Ríos, R. Romero-Troncoso","doi":"10.1109/DEMPED.2019.8864879","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864879","url":null,"abstract":"Broken rotor bar (BRB) detection in induction motors (1M) is a challenging task because the associated failure frequencies appear near the fundamental frequency component (FFC). This identification becomes harder when the IM operates at a low frequency or with low load conditions. Therefore, techniques like motor current signature analysis may suffer on properly detecting the existence and the severity of the fault. In this sense, suppressing the FFC results helpful to improve results in the condition monitoring of IM operating at low load. This work proposes the use of a genetic algorithm for estimating and suppressing the FFC in the current signals from an IM with a BRB. Experimental results prove that the use of this technique results in better and easier identification of BRB even when the motor works at low frequency or with a low load.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117085028","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 : 2019-08-01DOI: 10.1109/DEMPED.2019.8864895
Á. Sapena-Bañó, M. Riera-Guasp, J. Martínez-Román, M. Pineda-Sánchez, R. Puche-Panadero, J. Pérez-Cruz
The detection of abnormal eccentricity levels is a key issue for induction machines reliability, as it is related to the development of mechanical faults that produce most of induction motor (IM) breakdowns. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents to test the new techniques and devices under a wide variety of scenarios. Models running in real time in hardware in the loop (HIL) simulators could have a major impact in the development of the fault diagnosis techniques as they are free of the main drawbacks of test benches (high material and time costs, limited fault conditions that can be tested). These models must be accurate enough and they must run in real time. In this paper, a model of IM considering the static eccentricity (SE) fault is presented. The model takes advantage of the accuracy of the finite element method (FEM) to compute the coupling parameters which are used in an analytical model which can run in real-time in a HIL simulator. The model has been used to track the evolution of the SE related components in the stator current of an IM for several fault severity degrees.
{"title":"FEM-Analytical Hybrid Model for Real Time Simulation of IMs Under Static Eccentricity Fault","authors":"Á. Sapena-Bañó, M. Riera-Guasp, J. Martínez-Román, M. Pineda-Sánchez, R. Puche-Panadero, J. Pérez-Cruz","doi":"10.1109/DEMPED.2019.8864895","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864895","url":null,"abstract":"The detection of abnormal eccentricity levels is a key issue for induction machines reliability, as it is related to the development of mechanical faults that produce most of induction motor (IM) breakdowns. To favour the development of on-line fault diagnosis techniques, it is necessary to have real-time currents to test the new techniques and devices under a wide variety of scenarios. Models running in real time in hardware in the loop (HIL) simulators could have a major impact in the development of the fault diagnosis techniques as they are free of the main drawbacks of test benches (high material and time costs, limited fault conditions that can be tested). These models must be accurate enough and they must run in real time. In this paper, a model of IM considering the static eccentricity (SE) fault is presented. The model takes advantage of the accuracy of the finite element method (FEM) to compute the coupling parameters which are used in an analytical model which can run in real-time in a HIL simulator. The model has been used to track the evolution of the SE related components in the stator current of an IM for several fault severity degrees.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713294","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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