2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)最新文献_第2页

Assessments of High-Power Solid State Transformers based on Grain-Oriented magnetic cores 基于晶粒取向磁芯的大功率固态变压器性能评估

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864806

H. Ichou, D. Roger, M. Rossi, T. Belgrand

引用次数: 2

Demagentization Issues in Low Cost Synchronous Machine 低成本同步电机的消磁问题

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864886

C. Bianchini, A. Torreggiani, M. Davoli, Danilo David, A. Bellini, A. Formentini

引用次数: 0

Frequency Extraction of Current Signal Spectral Components: A New Tool for the Detection of Rotor Electrical Faults in Induction Motors 电流信号频谱分量的频率提取:一种检测异步电动机转子电气故障的新工具

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864893

P. Panagiotou, I. Arvanitakis, N. Lophitis, K. Gyftakis

引用次数: 1

Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework 基于物理辅助深度学习框架的短时间窗逆变感应电机诊断

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864889

S. Kandukuri, H. Van Khang, K. Robbersmyr

引用次数: 0

A Survey of Multi-Sensor Systems for Online Fault Detection of Electric Machines 电机故障在线检测多传感器系统研究进展

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864829

Genyi Luo, Jed Hurwitz, T. Habetler

引用次数: 7

Deep Learning Algorithms for Bearing Fault Diagnostics - A Review 轴承故障诊断的深度学习算法综述

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864915

S. Zhang, Shibo Zhang, B. Wang, T. Habetler

{"title":"Deep Learning Algorithms for Bearing Fault Diagnostics - A Review","authors":"S. Zhang, Shibo Zhang, B. Wang, T. Habetler","doi":"10.1109/DEMPED.2019.8864915","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864915","url":null,"abstract":"This paper presents a comprehensive review on applying various deep learning algorithms to bearing fault diagnostics. Over the last ten years, the emergence and revolution of deep learning (DL) methods have sparked great interests in both industry and academia. Some of the most noticeable advantages of DL based models over conventional physics based models or heuristic based methods are the automatic fault feature extraction and the improved classifier performance. In addition, a thorough and intuitive comparison study is presented summarizing the specific DL algorithm structure and its corresponding classifier accuracy for a number of papers utilizing the same Case Western Reserve University (CWRU) bearing data set. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on healthy monitoring are also presented.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"19 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":"117293804","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: 44

Multiplexing FBG Thermal Sensing for Uniform/Uneven Thermal Variation Monitoring in In-service Electric Machines 用于在役电机均匀/不均匀热变化监测的多路FBG热感测

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864832

A. Mohammed, S. Djurović

{"title":"Multiplexing FBG Thermal Sensing for Uniform/Uneven Thermal Variation Monitoring in In-service Electric Machines","authors":"A. Mohammed, S. Djurović","doi":"10.1109/DEMPED.2019.8864832","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864832","url":null,"abstract":"This paper reports a distributed thermal sensing system for stator winding internal thermal conditions monitoring in operating low voltage electric machines (LVEMs). To achieve multiple thermal sensing points distributed circumferentially in the interior of the stator windings structure, the proposed sensing system utilises the multiplexing feature of fibre Bragg grating sensing (FBG) technology coupled with flexible and non-conductive sensing fibre packaging. The proposed technique enables distributed temperature monitoring with much reduced sensing volume, weight and wiring, including a key advantage of ease of in-situ sensing points repositioning post-installation. System performance was evaluated in tests on a purpose built inverter driven totally enclosed fan cooled induction machine (TFEC-IM). In addition, its potential use for thermal capacity monitoring and evaluation of the examined TEFC-IM design under deteriorated cooling capability is evaluated. The results demonstrate that the proposed sensing system is effective in providing circumferential peak temperatures distribution of the stator windings in operating machines under normal and abnormal operating conditions.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"16 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":"122054294","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

Vibration, Acoustic Noise Generation and Power Quality in Inverter-fed Induction Motors 变频感应电动机的振动、噪声产生和电能质量

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864844

P. A. Delgado-Arredondo, R. Romero-Troncoso, Ó. Duque-Pérez, D. Morinigo-Sotelo, R. Osornio-Ríos

引用次数: 1

The Role of the Mechanical Speed Frequency on the Induction Motor Fault Detection via the Stray Flux 机械调速频率对通过杂散磁通量检测感应电机故障的作用

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864863

K. Gyftakis, P. Panagiotou, Sang Bin Lee

{"title":"The Role of the Mechanical Speed Frequency on the Induction Motor Fault Detection via the Stray Flux","authors":"K. Gyftakis, P. Panagiotou, Sang Bin Lee","doi":"10.1109/DEMPED.2019.8864863","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864863","url":null,"abstract":"Lately, the monitoring and analysis of the induction motor stray flux has been a modern trend and significant research work has been accomplished. Most papers have focused on the monitoring of rotor electrical faults around the fundamental stray flux signature, imitating in this way the traditional Motor Current Signature Analysis (MCSA). However, more significant fault related harmonics exist at other frequencies and most significantly around the mechanical frequency. The existence of the mechanical frequency in the stator current is still the best signature for detection of the mixed rotor eccentricity fault. Even healthy motors present this harmonic due to some low level inherent eccentricity. Despite that, it will be shown for the first time in this paper, with extensive Finite Element Analysis (FEA) and experimental testing, that the mechanical frequency associated harmonics in the stray flux can be purely rotor electrical fault related and completely independent from any rotor eccentricity and rotor imbalance. This makes this specific harmonic unreliable for any rotor fault diagnosis although can be a good indicator of rotor electrical faults at low slip operation. Finally, the sidebands of the mechanical frequency harmonics appear to be very sensitive to the broken rotor bar fault while quite immune to the number of the rotor bars.","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":"129465044","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: 15

Evaluation of Multiclass Novelty Detection Algorithms for Electric Machine Monitoring 电机监测中多类新颖性检测算法的评价

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864874

M. Ramírez Chávez, L. Ruiz Soto, F. Arellano Espitia, J. J. Saucedo, M. Delgado Prieto, L. Romeral

{"title":"Evaluation of Multiclass Novelty Detection Algorithms for Electric Machine Monitoring","authors":"M. Ramírez Chávez, L. Ruiz Soto, F. Arellano Espitia, J. J. Saucedo, M. Delgado Prieto, L. Romeral","doi":"10.1109/DEMPED.2019.8864874","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864874","url":null,"abstract":"The detection of unexpected events represents, currently, one of the most critical challenges dealing with electromechanical system diagnosis. In this regard, machine learning based algorithms widely applied in other fields of application are being considered now to face the novelty detection during the electric machine monitoring. In this study, an electrical monitoring scheme is considered for novelty detection performance evaluation, where vibration signals under different bearing fault conditions are acquired. Thus, the common electric machine monitoring framework, that is, a set of features estimated from a limited number of measurements, is considered in front of the three main novelty detection approaches: probability, domain and distance based. Performance of the corresponding approaches are studied and discussed experimentally. It is revealed that, although novelty detection provides enhanced diagnosis results in all cases, the response of some approaches fit better with the patterns resulting from the electric machine faults and the characteristics of the available measurements.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"4 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":"130535118","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: 2