Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864806
H. Ichou, D. Roger, M. Rossi, T. Belgrand
All fields of activity using electric energy are more than ever challenged for efficiency and versatility of energy flow at reasonable costs. The emergence of electronic components with high voltage and current capabilities enables to cope with those challenges. The paper deals with assessments on Medium Frequency (MF) high-power Solid State Transformer (SST). A good technical-economic balance can be achieved by assembling suitable high power SST cells made with mature technologies for power electronics, magnetic cores and simple and reliable control strategies. In this framework, the study and design of a SST based on elementary cells involving a Grain Oriented Electrical Steel (GOES) core is addressed.
{"title":"Assessments of High-Power Solid State Transformers based on Grain-Oriented magnetic cores","authors":"H. Ichou, D. Roger, M. Rossi, T. Belgrand","doi":"10.1109/DEMPED.2019.8864806","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864806","url":null,"abstract":"All fields of activity using electric energy are more than ever challenged for efficiency and versatility of energy flow at reasonable costs. The emergence of electronic components with high voltage and current capabilities enables to cope with those challenges. The paper deals with assessments on Medium Frequency (MF) high-power Solid State Transformer (SST). A good technical-economic balance can be achieved by assembling suitable high power SST cells made with mature technologies for power electronics, magnetic cores and simple and reliable control strategies. In this framework, the study and design of a SST based on elementary cells involving a Grain Oriented Electrical Steel (GOES) core is addressed.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"21 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":"121708422","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.2019.8864886
C. Bianchini, A. Torreggiani, M. Davoli, Danilo David, A. Bellini, A. Formentini
The request for high efficiency motor opens the possibility of substituting induction motors with more efficient permanent magnet synchronous motors. For medium and high power, the current ripple causes significant additional losses in the magnet and lamination and correlated demagnetization issues of the rotor permanents magnets due to high temperature. In this paper a new rotor topology is proposed and compared to a traditional surface permanent magnet rotor to reduce the magnet losses and protect them from demagnetization. A reference surface permanent magnet machine is compared with the proposed one in terms of performance and magnet losses. Both analytical and experimental analysis are carried out and shown in the following
{"title":"Demagentization Issues in Low Cost Synchronous Machine","authors":"C. Bianchini, A. Torreggiani, M. Davoli, Danilo David, A. Bellini, A. Formentini","doi":"10.1109/DEMPED.2019.8864886","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864886","url":null,"abstract":"The request for high efficiency motor opens the possibility of substituting induction motors with more efficient permanent magnet synchronous motors. For medium and high power, the current ripple causes significant additional losses in the magnet and lamination and correlated demagnetization issues of the rotor permanents magnets due to high temperature. In this paper a new rotor topology is proposed and compared to a traditional surface permanent magnet rotor to reduce the magnet losses and protect them from demagnetization. A reference surface permanent magnet machine is compared with the proposed one in terms of performance and magnet losses. Both analytical and experimental analysis are carried out and shown in the following","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"237 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":"132747469","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.2019.8864893
P. Panagiotou, I. Arvanitakis, N. Lophitis, K. Gyftakis
This work expands the classical current signature analysis in induction machines in a two-stage spectral decomposition manner. The proposed methodology can be summarized in two main steps: initially, the current signals are analyzed using a time frequency representation, with the analysis focusing on the steady-state regime; thereafter, frequency extraction is applied to the spectral signatures of interest, aiming to identify specific fault related harmonic subcomponents induced by the fault related speed ripple effect. The proposed approach is verified experimentally on a 4 kW induction motor.
{"title":"Frequency Extraction of Current Signal Spectral Components: A New Tool for the Detection of Rotor Electrical Faults in Induction Motors","authors":"P. Panagiotou, I. Arvanitakis, N. Lophitis, K. Gyftakis","doi":"10.1109/DEMPED.2019.8864893","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864893","url":null,"abstract":"This work expands the classical current signature analysis in induction machines in a two-stage spectral decomposition manner. The proposed methodology can be summarized in two main steps: initially, the current signals are analyzed using a time frequency representation, with the analysis focusing on the steady-state regime; thereafter, frequency extraction is applied to the spectral signatures of interest, aiming to identify specific fault related harmonic subcomponents induced by the fault related speed ripple effect. The proposed approach is verified experimentally on a 4 kW induction motor.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131829542","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.2019.8864889
S. Kandukuri, H. Van Khang, K. Robbersmyr
This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 seconds.
{"title":"Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework","authors":"S. Kandukuri, H. Van Khang, K. Robbersmyr","doi":"10.1109/DEMPED.2019.8864889","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864889","url":null,"abstract":"This article presents a framework for accurate fault diagnostics in inverter-fed induction machinery operating under variable speed and load conditions within very short time windows. Condition indicators based on fault characteristic frequencies observed over the extended Park's vector modulus are fused with deep features extracted using stacked autoencoders to generate a multidimensional feature space for fault classification using support vector machine. The proposed approach is demonstrated in a laboratory setup to detect the most commonly occurring faults, namely, the stator turns fault, broken rotor bars fault and bearing fault with an accuracy > 98% within a short time window of 2–3 seconds.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"59 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":"133016549","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.2019.8864843
P. Talitha, I. Hafiz, R. Vincentius, P. Ardyono, L. Vita, H. Mauridhi
Electrical machines such as generator can lose its synchronization due to the oscillation when a large disturbance happens. It becomes the primary concern in power stability, especially in transient stability because it leads to blackout condition. This paper proposed the addition of Super Capacitor Energy Storage (SCES) by absorbing the excess power when a disturbance happens. Equal Area Criterion (EAC) is used to obtain the value of Critical Clearing Time (CCT). The simulation is conducted in Single Machine Infinite Bus (SMIB). The value of CCT before adding SCES is 0.272s, while after adding SCES it becomes 0.485s. In order to optimize the CCT, a Differential Evolution (DE) Algorithm is used. In this paper, SCES strengthening components (KSCES) used as the optimized parameter. As a result, the value of SCES becomes 0.574s, which is higher than before adding SCES and before optimizing the parameter of SCES.
{"title":"Optimizing the Generator Critical Clearing Time using Super Capacitor Energy Storage in the Grid Power System with Differential Evolution Algorithm","authors":"P. Talitha, I. Hafiz, R. Vincentius, P. Ardyono, L. Vita, H. Mauridhi","doi":"10.1109/DEMPED.2019.8864843","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864843","url":null,"abstract":"Electrical machines such as generator can lose its synchronization due to the oscillation when a large disturbance happens. It becomes the primary concern in power stability, especially in transient stability because it leads to blackout condition. This paper proposed the addition of Super Capacitor Energy Storage (SCES) by absorbing the excess power when a disturbance happens. Equal Area Criterion (EAC) is used to obtain the value of Critical Clearing Time (CCT). The simulation is conducted in Single Machine Infinite Bus (SMIB). The value of CCT before adding SCES is 0.272s, while after adding SCES it becomes 0.485s. In order to optimize the CCT, a Differential Evolution (DE) Algorithm is used. In this paper, SCES strengthening components (KSCES) used as the optimized parameter. As a result, the value of SCES becomes 0.574s, which is higher than before adding SCES and before optimizing the parameter of SCES.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"98 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":"132981369","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.2019.8864915
S. Zhang, Shibo Zhang, B. Wang, T. Habetler
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.
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864832
A. Mohammed, S. Djurović
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.
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864844
P. A. Delgado-Arredondo, R. Romero-Troncoso, Ó. Duque-Pérez, D. Morinigo-Sotelo, R. Osornio-Ríos
The use of inverter-fed electric motors in the industry is currently of great importance. These inverters generate harmonics and noise in the current and voltage signals, which affect the generation of vibration and acoustic noise. The power supply should be considered for establishing fault thresholds in a diagnostic system or meeting certain specifications for acoustic noise and vibrations. This paper presents an experimental analysis of the energy quality provided by variable frequency drives and the power grid as they generate diverse conditions for fault-diagnosis techniques. The effects produced by the vibration and sound signals harmonics are presented and evaluated quantitatively by the calculation and comparison of the vibration and sound energy of the signals generated by the frequency drives and the power grid. Also, the spectra of vibration and sound are obtained for steady-state and startup transient operation.
{"title":"Vibration, Acoustic Noise Generation and Power Quality in Inverter-fed Induction Motors","authors":"P. A. Delgado-Arredondo, R. Romero-Troncoso, Ó. Duque-Pérez, D. Morinigo-Sotelo, R. Osornio-Ríos","doi":"10.1109/DEMPED.2019.8864844","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864844","url":null,"abstract":"The use of inverter-fed electric motors in the industry is currently of great importance. These inverters generate harmonics and noise in the current and voltage signals, which affect the generation of vibration and acoustic noise. The power supply should be considered for establishing fault thresholds in a diagnostic system or meeting certain specifications for acoustic noise and vibrations. This paper presents an experimental analysis of the energy quality provided by variable frequency drives and the power grid as they generate diverse conditions for fault-diagnosis techniques. The effects produced by the vibration and sound signals harmonics are presented and evaluated quantitatively by the calculation and comparison of the vibration and sound energy of the signals generated by the frequency drives and the power grid. Also, the spectra of vibration and sound are obtained for steady-state and startup transient operation.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"143 6 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":"129461193","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.2019.8864874
M. Ramírez Chávez, L. Ruiz Soto, F. Arellano Espitia, J. J. Saucedo, M. Delgado Prieto, L. Romeral
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.
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864829
Genyi Luo, Jed Hurwitz, T. Habetler
A survey of the existing multi-sensor systems for condition monitoring and fault detection of electric motors is presented in this paper. Various types of sensors and their capability of serving as information sources are discussed and compared. This paper then listed a series of different type of multi-sensor systems examples. Different fusion models and their best fitted situation are detailed. At the end of the paper, feasibility of different type of systems is compared. The potentials and shortcoming of multi-sensor systems are also discussed.
{"title":"A Survey of Multi-Sensor Systems for Online Fault Detection of Electric Machines","authors":"Genyi Luo, Jed Hurwitz, T. Habetler","doi":"10.1109/DEMPED.2019.8864829","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864829","url":null,"abstract":"A survey of the existing multi-sensor systems for condition monitoring and fault detection of electric motors is presented in this paper. Various types of sensors and their capability of serving as information sources are discussed and compared. This paper then listed a series of different type of multi-sensor systems examples. Different fusion models and their best fitted situation are detailed. At the end of the paper, feasibility of different type of systems is compared. The potentials and shortcoming of multi-sensor systems are also discussed.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"113 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113940633","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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