The Experts below are selected from a list of 72531 Experts worldwide ranked by ideXlab platform
Mohamed Benbouzid - One of the best experts on this subject based on the ideXlab platform.
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eemd based wind turbine bearing Failure Detection using the generator stator current homopolar component
Mechanical Systems and Signal Processing, 2013Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Wind Turbine Bearing Failure Detection Using Generator Stator Current Homopolar Component Ensemble Empirical Mode Decomposition
2012Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homo polar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering-iree, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Yassine Amirat - One of the best experts on this subject based on the ideXlab platform.
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eemd based wind turbine bearing Failure Detection using the generator stator current homopolar component
Mechanical Systems and Signal Processing, 2013Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Wind Turbine Bearing Failure Detection Using Generator Stator Current Homopolar Component Ensemble Empirical Mode Decomposition
2012Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homo polar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering-iree, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Vincent Choqueuse - One of the best experts on this subject based on the ideXlab platform.
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eemd based wind turbine bearing Failure Detection using the generator stator current homopolar component
Mechanical Systems and Signal Processing, 2013Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homopolar component of the generator stator current and attempts to highlight the use of the ensemble empirical mode decomposition as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Wind Turbine Bearing Failure Detection Using Generator Stator Current Homopolar Component Ensemble Empirical Mode Decomposition
2012Co-Authors: Yassine Amirat, Vincent Choqueuse, Mohamed BenbouzidAbstract:Failure Detection has always been a demanding task in the electrical machines community; it has become more challenging in wind energy conversion systems because sustainability and viability of wind farms are highly dependent on the reduction of the operational and maintenance costs. Indeed the most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early Detection of the generator health degeneration, facilitating a proactive response, minimizing downtime, and maximizing productivity. This paper provides then an assessment of a Failure Detection techniques based on the homo polar component of the generator stator current and attempts to highlight the use of the Ensemble Empirical Mode Decomposition (EEMD) as a tool for Failure Detection in wind turbine generators for stationary and non stationary cases.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering-iree, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Sylvie Turri - One of the best experts on this subject based on the ideXlab platform.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox.
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Hilbert Transform-Based Bearing Failure Detection in DFIG-Based Wind Turbines
International Review of Electrical Engineering-iree, 2011Co-Authors: Yassine Amirat, Mohamed Benbouzid, Vincent Choqueuse, Sylvie TurriAbstract:Cost-effective, predictive and proactive maintenance of wind turbines assumes more importance with the increasing number of installed wind farms in more remote location (offshore). A well-known method for assessing impeding problems is to use current sensors installed within the wind turbine generator. This paper describes then an approach based on the generator stator current data collection and attempts to highlight the use of the Hilbert transform for Failure Detection in a doubly-fed induction generator-based. Indeed, this generator is commonly used in modern variable-speed wind turbines. The proposed Failure Detection technique has been validated experimentally regarding bearing Failures. Indeed, a large fraction of wind turbine downtime is due to bearing Failures, particularly in the generator and gearbox. Copyright © 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Mouloud Koudil - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Failure Detection in low power lossy wireless sensor networks
Journal of Network and Computer Applications, 2014Co-Authors: Fatima Zohra Benhamida, Yacine Challal, Mouloud KoudilAbstract:In this paper, we investigate the use of Failure detectors (FD) in Wireless Sensor Networks (WSN). We provide a classification of FD with respect to some WSN criteria. The focus will be on energy depletion and lossy links. We propose then a new general FD model tailored to WSN constraints, called Adaptive Neighborhood Failure Detector for Low-power lossy WSN (AFDEL). AFDEL provides adaptive local Failure Detection robust against packet loss (intermittent Failures) and saves the use of energy, bandwidth and memory storage. Furthermore, we introduce in AFDEL model an adaptive timer strategy. This strategy offers the possibility to customize the dynamic timer pattern with respect to application requirements in terms of completeness and accuracy. We illustrate the use of this strategy by proposing three Failure Detection techniques based on AFDEL general model. As a pat of this work, we give a stochastic based approach for timer adaptation. We evaluate
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Efficient Adaptive Failure Detection for Query/Response based Wireless Sensor Network
2011Co-Authors: Fatima Zohra Benhamida, Yacine Challal, Mouloud KoudilAbstract:We consider the problem of Failure management in wireless sensor networks (WSN). In such networks, nodes can be subject to frequent Failures due to energy depletion and the hostile deployment environment. Our work focuses on local crash process Detection in WSN considering intermittent Failures due to lossy radio links. As a part of this problem, we introduce a new type of Adaptive Neighborhood Failure Detection mechanism (ANFD) that relies on adaptive timers. Then we analyze properties of the introduced Failure Detection algorithm and determine how and when timers' update is necessary. We carried out intensive simulations using TinyOS-2 and evaluated the performance of our solution. Simulation results show that our solution improves Failure Detection rate and delay, and reduces fault positive Detections and packet loss ratio. Moreover, we analyzed energy consumption using PowerTossimZ and results demonstrate that our solution does not induce extra energy consumption compared to other solutions in the literature
