2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)最新文献_第4页

Eccentricity detection procedure in electric motors by force transducer and search coils in a novel experimental test bench 基于力传感器和搜索线圈的电机偏心检测方法

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864857

Unai Galfarsoro, A. McCloskey, Xabier Hernández, G. Almandoz, S. Zarate, X. Arrasate

引用次数: 5

Novel condition monitoring approach based on hybrid feature extraction and neural network for assessing multiple faults in electromechanical systems 基于混合特征提取和神经网络的机电系统多故障状态监测新方法

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864835

J. Saucedo-Dorantes, R. Osornio-Ríos, R. Romero-Troncoso, M. Delgado-Prieto, Francisco Arellano-Espitia

{"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}

引用次数: 0

Data Driven Methods for the Prediction of Failures 故障预测的数据驱动方法

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864877

A. Soualhi, H. Razik, G. Clerc

引用次数: 6

Optimization of magnetic flux paths in transverse flux machines through the use of iron wire wound materials 利用铁丝缠绕材料优化横向磁通机的磁通路径

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864913

A. Giedymin, Théodore Cherriére, F. Avcilar, U. Schäfer, F. Mazaleyrat

引用次数: 2

A Data-Driven Diagnosis Method of Open-Circuit Switch Faults for PMSG-Based Wind Generation System 基于pmsg的风力发电系统开路开关故障数据驱动诊断方法

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864922

Z. Xue, M. S. Li, K. Xiahou, T. Ji, Q. Wu

{"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}

引用次数: 6

Multiple faults detection in low voltage inverter-fed induction motors 低压变频感应电动机的多重故障检测

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864885

L. Frosini, Marcello Minervini, L. Ciceri, A. Albini

引用次数: 6

Rotor Fault Detection in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transient 基于启动暂态速度分析的变频异步电动机转子故障检测

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864862

T. Garcia-Calva, D. Morinigo-Sotelo, A. García-Perez, R. Romero-Troncoso

引用次数: 4

Welcoome to SDEMPED 2019 欢迎参加SDEMPED 2019

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/demped.2019.8864894

引用次数: 0

Genetic Algorithm Methodology for Broken Bar Detection in Induction Motor at Low Frequency and Load Operation 低频负载下感应电动机断条检测的遗传算法

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864879

D. A. Elvira-Ortiz, D. Morinigo-Sotelo, Á. Zorita-Lamadrid, R. Osornio-Ríos, R. Romero-Troncoso

引用次数: 4

FEM-Analytical Hybrid Model for Real Time Simulation of IMs Under Static Eccentricity Fault 静态偏心故障下IMs实时仿真的有限元-解析混合模型

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864895

Á. Sapena-Bañó, M. Riera-Guasp, J. Martínez-Román, M. Pineda-Sánchez, R. Puche-Panadero, J. Pérez-Cruz

{"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}

引用次数: 7