The Experts below are selected from a list of 13440 Experts worldwide ranked by ideXlab platform
Antonio Marques J. Cardoso - One of the best experts on this subject based on the ideXlab platform.
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Reliable Detection of Stator Inter-Turn Faults of Very Low Severity Level in Induction Motors
IEEE Transactions on Industrial Electronics, 1Co-Authors: Konstantinos N. Gyftakis, Antonio Marques J. CardosoAbstract:The inter-turn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient Severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature is most past efforts is the high Level of Severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault Severity Levels that are non-catastrophic and that are by their nature very low. This is the motivation behind this work, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The paper studies induction motors with incipient inter-turn fault Severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low Level inter-turn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.
Konstantinos N. Gyftakis - One of the best experts on this subject based on the ideXlab platform.
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IECON - Reliable Detection of Very Low Severity Level Stator Inter-Turn Faults in Induction Motors
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019Co-Authors: Konstantinos N. Gyftakis, Antonio J. Marques-cardosoAbstract:Stator electrical faults are considered the most severe and challenging to detect among the various induction motor faults. This is due to the fast evolving Severity Level that leads from an early inter-turn fault to the machine breakdown. Many works have been published over the years presenting a variety of diagnostic techniques to detect this fault. However, the fault Level Severity is kept high enough to allow for the diagnosis neglecting the fact that at that Severity Level the machine will fail and thus diagnosis of the fault is useless. This paper challenges the existing technical know-how in the subject, while attempting to detect the fault at very low Severity Levels. It will be shown with extensive experimental testing and analysis that many well established methods are completely unreliable for this task. Finally, the authors propose a novel method that is able not only to detect the fault reliably but is also sensitive to the fault Level Severity.
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Reliable Detection of Very Low Severity Level Stator Inter-Turn Faults in Induction Motors
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019Co-Authors: Konstantinos N. Gyftakis, Antonio J. Marques-cardosoAbstract:Stator electrical faults are considered the most severe and challenging to detect among the various induction motor faults. This is due to the fast evolving Severity Level that leads from an early inter-turn fault to the machine breakdown. Many works have been published over the years presenting a variety of diagnostic techniques to detect this fault. However, the fault Level Severity is kept high enough to allow for the diagnosis neglecting the fact that at that Severity Level the machine will fail and thus diagnosis of the fault is useless. This paper challenges the existing technical know-how in the subject, while attempting to detect the fault at very low Severity Levels. It will be shown with extensive experimental testing and analysis that many well established methods are completely unreliable for this task. Finally, the authors propose a novel method that is able not only to detect the fault reliably but is also sensitive to the fault Level Severity.
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Reliable Detection of Stator Inter-Turn Faults of Very Low Severity Level in Induction Motors
IEEE Transactions on Industrial Electronics, 1Co-Authors: Konstantinos N. Gyftakis, Antonio Marques J. CardosoAbstract:The inter-turn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient Severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature is most past efforts is the high Level of Severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault Severity Levels that are non-catastrophic and that are by their nature very low. This is the motivation behind this work, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The paper studies induction motors with incipient inter-turn fault Severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low Level inter-turn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.
Chitra Dorai - One of the best experts on this subject based on the ideXlab platform.
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Winter Simulation Conference - A new policy for the service request assignment problem with multiple Severity Level, due date and SLA penalty service requests
2008 Winter Simulation Conference, 2008Co-Authors: Anshul Sheopuri, Sai Zeng, Chitra DoraiAbstract:We study the problem of assigning multiple Severity Level service requests to agents in an agent pool. Each Severity Level is associated with a due date and a penalty, which is incurred if the service request is not resolved by the due date. Motivated by Van Meighem (2003), who shows the asymptotic optimality of the Generalized Longest Queue policy for the problem of minimizing the due date dependent expected delay costs when there is a single agent, we develop a class of Index-based policies that is a generalization of the Priority First-Come-First-Serve, Weighted Shortest Expected Processing Time and Generalized Longest Queue policy. In our simulation study of an assignment system of a large technology firm, the Index-based policy shows an improvement of 0--20 % over the Priority First-Come-First-Serve policy depending upon the load conditions.
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A new policy for the service request assignment problem with multiple Severity Level, due date and sla penalty service requests
2008 Winter Simulation Conference, 2008Co-Authors: Anshul Sheopuri, Sai Zeng, Chitra DoraiAbstract:We study the problem of assigning multiple Severity Level service requests to agents in an agent pool. Each Severity Level is associated with a due date and a penalty, which is incurred if the service request is not resolved by the due date. Motivated by Van Meighem (2003), who shows the asymptotic optimality of the generalized longest queue policy for the problem of minimizing the due date dependent expected delay costs when there is a single agent, we develop a class of index-based policies that is a generalization of the priority first-come-first-serve, weighted shortest expected processing time and generalized longest queue policy. In our simulation study of an assignment system of a large technology firm, the index-based policy shows an improvement of 0-20 % over the priority first-come-first-serve policy depending upon the load conditions.
Costas J. Spanos - One of the best experts on this subject based on the ideXlab platform.
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CASE - Fusing system configuration information for building cooling plant Fault Detection and Severity Level identification
2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016Co-Authors: Dan Li, Yuxun Zhou, Guoqiang Hu, Costas J. SpanosAbstract:Building Fault Detection and Diagnosis (FDD) technology is indispensable for energy saving and performance improvement of built environment regulation. Recent data-driven methods have shown the advantage in dealing with complex systems with random inputs. However, existing works on data-driven FDD merely considers the task as a simple classification problem to identify fault types. Prior knowledge on system configuration and fault Severity Levels have long been ignored. This paper proposes a novel data-driven strategy that adopts hierarchical labeling to fuse system structure information and describes the Severity Levels in a unified learning framework. A Large Margin Information Fusion (LMIF) method is derived and an on-line learning algorithm for streaming data is developed. Following the ASHRAE Research Project 1043 (RP-1043), the proposed strategy is applied to the FDD of a 90-ton centrifugal water-cooled chiller. Experimental results show that LMIF can greatly improve the FDD performance as well as recognize the fault Severity Levels with high accuracy, justifying the benefit of fusing prior knowledge of fault dependence information into the learning method.
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A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis
Energy and Buildings, 2016Co-Authors: Dan Li, Guoqiang Hu, Costas J. SpanosAbstract:Chillers contribute to a significant part of the building energy consumption. In order to save energy and improve the performance of building automation systems, there is an increasing need for chiller fault detection and diagnosis (FDD). This paper proposes a two-stage data-driven FDD strategy which formulates the chiller fault detection and diagnosis task as a multi-class classification problem. Linear Discriminant Analysis (LDA) is adopted to project the high dimensional data into a lower dimensional space so as to achieve maximum class separation and original class information maintenance. At the first stage, a fault is detected and diagnosed if the monitoring data set is the closest to one of the predefined fault clusters and within the predefined Manhattan distance range of the corresponding fault. At the second stage, fault Severity Level is recognized by comparing the monitoring data set with the corresponding predefined Severity Level clusters. The fault is diagnosed as at a particular Severity Level if it is the closest to the corresponding Severity Level cluster. The proposed strategy is validated by the experimental data of ASHRAE Research Project 1043 (RP-1043). Results show that the data-driven FDD strategy using LDA can detect and diagnose chiller faults effectively.
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Fusing system configuration information for building cooling plant Fault Detection and Severity Level identification
2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016Co-Authors: Dan Li, Yuxun Zhou, Guoqiang Hu, Costas J. SpanosAbstract:Building Fault Detection and Diagnosis (FDD) technology is indispensable for energy saving and performance improvement of built environment regulation. Recent data-driven methods have shown the advantage in dealing with complex systems with random inputs. However, existing works on data-driven FDD merely considers the task as a simple classification problem to identify fault types. Prior knowledge on system configuration and fault Severity Levels have long been ignored. This paper proposes a novel data-driven strategy that adopts hierarchical labeling to fuse system structure information and describes the Severity Levels in a unified learning framework. A Large Margin Information Fusion (LMIF) method is derived and an on-line learning algorithm for streaming data is developed. Following the ASHRAE Research Project 1043 (RP-1043), the proposed strategy is applied to the FDD of a 90-ton centrifugal water-cooled chiller. Experimental results show that LMIF can greatly improve the FDD performance as well as recognize the fault Severity Levels with high accuracy, justifying the benefit of fusing prior knowledge of fault dependence information into the learning method.
Barak Rosenn - One of the best experts on this subject based on the ideXlab platform.
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insulin and glyburide therapy dosage Severity Level of gestational diabetes and pregnancy outcome
American Journal of Obstetrics and Gynecology, 2005Co-Authors: Oded Langer, Yariv Yogev, Elly M J Xenakis, Barak RosennAbstract:Objective We sought to investigate the association between glyburide dose, degree of Severity in gestational diabetes mellitus (GDM), Level of glycemic control, and pregnancy outcome in insulin- and glyburide-treated patients. Study design In a secondary analysis of our previous randomized study, 404 women were analyzed. The association among glyburide dose, Severity of GDM, and selected maternal and neonatal factors was evaluated. Severity Levels of GDM were stratified by fasting plasma glucose (FPG) from the oral glucose tolerance test (OGTT). Infants with birth weight at or above the 90th percentile were considered large-for-gestational age (LGA). Macrosomia was defined as birth weight ≥4000 g. Well-controlled was defined as mean blood glucose ≤95 mg/dL. The association between glyburide- and insulin-treated patients by Severity of GDM and neonatal outcome was evaluated. Results The dose received for the glyburide-treated patients was 2.5 mg–32%; 5 mg–23%; 10 mg–17%; 15 mg–8%; and 20 mg–20%. Patients were grouped into low (≤10 mg) and high (>10 mg) daily dose of glyburide. A comparison between Severity of the disease (fasting plasma glucose categories) and highest dose of glyburide revealed a significant difference between the low-95 FPG and the other Severity categories ( P =.02). Of patients in the well-controlled glycemic group, only 6% required the high dose of glyburide (>10 mg). In patients with poor glycemic control (mean blood glucose >95 mg/dL), 38% received the high dose of glyburide ( P =.0001). Comparison between the high glyburide (>10 mg) and the low glyburide dosages (≤10 mg) revealed that the rate of macrosomia was 16% vs 5% and LGA 22% vs 8%, ( P =.01), respectively. No significant difference was found in composite outcome, metabolic complications, and Ponderal Index between the 2 dose groups. Stratification by disease Severity revealed a significantly lower rate of LGA for both the glyburide- and insulin-treated subjects. No significant difference was found between metabolic, respiratory, and neonatal intensive care unit (NICU) for patients within each fasting plasma glucose Severity category. Conclusion Glyburide and insulin are equally efficient for treatment of GDM in all Levels of disease Severity. Achieving the established Level of glycemic control, not the mode of pharmacologic therapy, is the key to improving the outcome in GDM.
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insulin and glyburide therapy dosage Severity Level of gestational diabetes and pregnancy outcome
American Journal of Obstetrics and Gynecology, 2005Co-Authors: Oded Langer, Yariv Yogev, Elly M J Xenakis, Barak RosennAbstract:OBJECTIVE: We sought to investigate the association between glyburide dose, degree of Severity in gestational diabetes mellitus (GDM), Level of glycemic control, and pregnancy outcome in insulin- and glyburide-treated patients. STUDY DESIGN: In a secondary analysis of our previous randomized study, 404 women were analyzed. The association among glyburide dose, Severity of GDM, and selected maternal and neonatal factors was evaluated. Severity Levels of GDM were stratified by fasting plasma glucose (FPG) from the oral glucose tolerance test (OGTT). Infants with birth weight at or above the 90th percentile were considered large-for-gestational age (LGA). Macrosomia was defined as birth weight > or =4000 g. Well-controlled was defined as mean blood glucose 10 mg) daily dose of glyburide. A comparison between Severity of the disease (fasting plasma glucose categories) and highest dose of glyburide revealed a significant difference between the low-95 FPG and the other Severity categories (P = .02). Of patients in the well-controlled glycemic group, only 6% required the high dose of glyburide (>10 mg). In patients with poor glycemic control (mean blood glucose >95 mg/dL), 38% received the high dose of glyburide (P = .0001). Comparison between the high glyburide (>10 mg) and the low glyburide dosages (< or =10 mg) revealed that the rate of macrosomia was 16% vs 5% and LGA 22% vs 8%, (P = .01), respectively. No significant difference was found in composite outcome, metabolic complications, and Ponderal Index between the 2 dose groups. Stratification by disease Severity revealed a significantly lower rate of LGA for both the glyburide- and insulin-treated subjects. No significant difference was found between metabolic, respiratory, and neonatal intensive care unit (NICU) for patients within each fasting plasma glucose Severity category. CONCLUSION: Glyburide and insulin are equally efficient for treatment of GDM in all Levels of disease Severity. Achieving the established Level of glycemic control, not the mode of pharmacologic therapy, is the key to improving the outcome in GDM.
