The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Frederic S. Resnic - One of the best experts on this subject based on the ideXlab platform.
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Vascular Closure Device Failure: Frequency and Implications: A Propensity-Matched Analysis
Circulation. Cardiovascular interventions, 2009Co-Authors: Sripal Bangalore, Nipun Arora, Frederic S. ResnicAbstract:Background —Vascular closure devices (VCDs) are effective in reducing the time to ambulation for patients undergoing cardiac catheterization procedures and in reducing the risk of vascular complications in selected patient cohorts. However, the Frequency and consequence of Failure of VCDs is not well defined. Methods and Results —From a prospective registry of consecutive patients undergoing cardiac catheterization at our center, 9823 patients who received either a collagen plug-based (Angio-Seal) or a suture-based (Perclose) VCD were selected for the study. VCD Failure was defined as unsuccessful deployment or Failure to achieve hemostasis. Major vascular complication was defined as any retroperitoneal hemorrhage, limb ischemia, or any surgical repair. Minor vascular complication was defined as any groin bleeding, hematoma (≥5 cm), pseudoaneurysm, or arteriovenous fistula. Any vascular complication was defined as either a major or minor vascular complication. Among the 9823 patients in the study, VCD failed in 268 patients (2.7%; 2.3% diagnostic versus 3.0% percutaneous coronary intervention; P =0.029). Patients with VCD Failure had significantly increased risk of any (6.7% versus 1.4%; P
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vascular closure device Failure Frequency and implications a propensity matched analysis
Circulation-cardiovascular Interventions, 2009Co-Authors: Sripal Bangalore, Nipun Arora, Frederic S. ResnicAbstract:Background —Vascular closure devices (VCDs) are effective in reducing the time to ambulation for patients undergoing cardiac catheterization procedures and in reducing the risk of vascular complications in selected patient cohorts. However, the Frequency and consequence of Failure of VCDs is not well defined. Methods and Results —From a prospective registry of consecutive patients undergoing cardiac catheterization at our center, 9823 patients who received either a collagen plug-based (Angio-Seal) or a suture-based (Perclose) VCD were selected for the study. VCD Failure was defined as unsuccessful deployment or Failure to achieve hemostasis. Major vascular complication was defined as any retroperitoneal hemorrhage, limb ischemia, or any surgical repair. Minor vascular complication was defined as any groin bleeding, hematoma (≥5 cm), pseudoaneurysm, or arteriovenous fistula. Any vascular complication was defined as either a major or minor vascular complication. Among the 9823 patients in the study, VCD failed in 268 patients (2.7%; 2.3% diagnostic versus 3.0% percutaneous coronary intervention; P =0.029). Patients with VCD Failure had significantly increased risk of any (6.7% versus 1.4%; P <0.0001), major (1.9% versus 0.6%; P =0.006), or minor (6.0% versus 1.1%; P <0.0001) vascular complication compared with the group with successful deployment of VCD. The increased risk of vascular complication was unchanged in a propensity score-matched cohort. Conclusions —In contemporary practice, VCD Failure is rare, but when it does fail, it is associated with a significant increase in the risk of vascular complications. Patients with VCD Failure should be closely monitored.
Sripal Bangalore - One of the best experts on this subject based on the ideXlab platform.
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Vascular Closure Device Failure: Frequency and Implications: A Propensity-Matched Analysis
Circulation. Cardiovascular interventions, 2009Co-Authors: Sripal Bangalore, Nipun Arora, Frederic S. ResnicAbstract:Background —Vascular closure devices (VCDs) are effective in reducing the time to ambulation for patients undergoing cardiac catheterization procedures and in reducing the risk of vascular complications in selected patient cohorts. However, the Frequency and consequence of Failure of VCDs is not well defined. Methods and Results —From a prospective registry of consecutive patients undergoing cardiac catheterization at our center, 9823 patients who received either a collagen plug-based (Angio-Seal) or a suture-based (Perclose) VCD were selected for the study. VCD Failure was defined as unsuccessful deployment or Failure to achieve hemostasis. Major vascular complication was defined as any retroperitoneal hemorrhage, limb ischemia, or any surgical repair. Minor vascular complication was defined as any groin bleeding, hematoma (≥5 cm), pseudoaneurysm, or arteriovenous fistula. Any vascular complication was defined as either a major or minor vascular complication. Among the 9823 patients in the study, VCD failed in 268 patients (2.7%; 2.3% diagnostic versus 3.0% percutaneous coronary intervention; P =0.029). Patients with VCD Failure had significantly increased risk of any (6.7% versus 1.4%; P
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vascular closure device Failure Frequency and implications a propensity matched analysis
Circulation-cardiovascular Interventions, 2009Co-Authors: Sripal Bangalore, Nipun Arora, Frederic S. ResnicAbstract:Background —Vascular closure devices (VCDs) are effective in reducing the time to ambulation for patients undergoing cardiac catheterization procedures and in reducing the risk of vascular complications in selected patient cohorts. However, the Frequency and consequence of Failure of VCDs is not well defined. Methods and Results —From a prospective registry of consecutive patients undergoing cardiac catheterization at our center, 9823 patients who received either a collagen plug-based (Angio-Seal) or a suture-based (Perclose) VCD were selected for the study. VCD Failure was defined as unsuccessful deployment or Failure to achieve hemostasis. Major vascular complication was defined as any retroperitoneal hemorrhage, limb ischemia, or any surgical repair. Minor vascular complication was defined as any groin bleeding, hematoma (≥5 cm), pseudoaneurysm, or arteriovenous fistula. Any vascular complication was defined as either a major or minor vascular complication. Among the 9823 patients in the study, VCD failed in 268 patients (2.7%; 2.3% diagnostic versus 3.0% percutaneous coronary intervention; P =0.029). Patients with VCD Failure had significantly increased risk of any (6.7% versus 1.4%; P <0.0001), major (1.9% versus 0.6%; P =0.006), or minor (6.0% versus 1.1%; P <0.0001) vascular complication compared with the group with successful deployment of VCD. The increased risk of vascular complication was unchanged in a propensity score-matched cohort. Conclusions —In contemporary practice, VCD Failure is rare, but when it does fail, it is associated with a significant increase in the risk of vascular complications. Patients with VCD Failure should be closely monitored.
Chanan Singh - One of the best experts on this subject based on the ideXlab platform.
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A Fast and Accurate Failure Frequency Approximation for $k$ -Terminal Reliability Systems
IEEE Transactions on Reliability, 2018Co-Authors: Anoosheh Heidarzadeh, Alex Sprintson, Chanan SinghAbstract:This paper considers the problem of approximating the Failure Frequency of large-scale composite $\boldsymbol{k}$ -terminal reliability systems. In such systems, the nodes ( $\boldsymbol{k}$ of which are terminals) are connected through components, which are subject to random Failure and repair processes. At any time, a system Failure occurs if the surviving system fails to connect all the $\boldsymbol{k}$ terminals together. We assume that each component's up times and down times follow statistically independent stationary random processes, and these processes are statistically independent across the components. In this setting, the exact computation of Failure Frequency is known to be computationally intractable (NP-hard). In this paper, we present an algorithm to approximate the Failure Frequency for any given multiplicative error factor that runs in polynomial time in the number of (minimal) cutsets. Moreover, for the special case of all-terminal reliability systems, i.e., where all the nodes are terminals, we propose an algorithm for approximating the Failure Frequency within an arbitrary multiplicative error that runs in polynomial time in the number of nodes (which can be much smaller than the number of cutsets). Our simulation results confirm that the proposed method is much faster and more accurate than the standard Monte Carlo simulation technique for approximating the Failure Frequency.
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A Fast and Accurate Approximation Algorithm for Failure Frequency of Power Distribution Systems
2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2018Co-Authors: Anoosheh Heidarzadeh, Alex Sprintson, Chanan SinghAbstract:This paper considers the problem of approximating the Failure Frequency of power distribution systems modeled as k-terminal reliability systems. In such systems, the nodes (i.e., buses), among which $k$ nodes are terminals (i.e., generation and load buses), are connected via components (i.e., lines) that are subject to random Failure and repair processes. Any time the surviving system fails to connect all terminals, a system Failure occurs. Assuming that the up-times and down-times of each component follow statistically independent stationary random processes, and these processes are statistically independent across the components, the exact computation of Failure Frequency is known to be intractable (NP-hard). In this work, we propose a randomized algorithm for approximating the Failure Frequency within an arbitrary multiplicative error that runs in polynomial time in the number of cutsets in the system, and has an arbitrarily small error probability. We illustrate the application of the proposed algorithm by simulating the distribution system of Micropolis, a virtual city designed to represent a typical small community in the United States. Our simulation results confirm that the proposed algorithm is faster and more accurate than the standard Monte Carlo simulation for approximating the Failure Frequency.
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A Fast and Accurate Failure Frequency Approximation for $k$-Terminal Reliability Systems
arXiv: Data Structures and Algorithms, 2017Co-Authors: Anoosheh Heidarzadeh, Alex Sprintson, Chanan SinghAbstract:This paper considers the problem of approximating the Failure Frequency of large-scale composite $k$-terminal reliability systems. In such systems, the nodes ($k$ of which are terminals) are connected through components which are subject to random Failure and repair processes. At any time, a system Failure occurs if the surviving system fails to connect all the k terminals together. We assume that each component's up-times and down-times follow statistically independent stationary random processes, and these processes are statistically independent across the components. In this setting, the exact computation of Failure Frequency is known to be computationally intractable (NP-hard). In this work, we present an algorithm to approximate the Failure Frequency for any given multiplicative error factor that runs in polynomial time in the number of (minimal) cutsets. Moreover, for the special case of all-terminal reliability systems, i.e., where all nodes are terminals, we propose an algorithm for approximating the Failure Frequency within an arbitrary multiplicative error that runs in polynomial time in the number of nodes (which can be much smaller than the number of cutsets). In addition, our simulation results confirm that the proposed method is much faster and more accurate than the Monte Carlo simulation technique for approximating the Failure Frequency.
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Determination of Failure Frequency indices from state space decomposition
2006Co-Authors: Joydeep Mitra, Chanan SinghAbstract:This paper describes a technique for the determination of Frequency and duration indices, in the steady state, of discrete capacity systems, using the method of state space decomposition. For the first time direct, closed form expressions are developed and presented, and an efficient implementation is outlined. The approach is illustrated by means of a numerical example and validated using an enumeration-based method.
Jovica R. Riznic - One of the best experts on this subject based on the ideXlab platform.
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A Stochastic Model for Piping Failure Frequency Analysis Using OPDE Data
Journal of Engineering for Gas Turbines and Power, 2009Co-Authors: Xian-xun Yuan, Mahesh D. Pandey, Jovica R. RiznicAbstract:The piping Failure event can be mathematically modeled as a stochastic process with a time variant intensity function. The rea- son being that pipe degradation as a function of aging could in- crease the Failure Frequency over time. Several approaches applied to the estimation of piping Failure frequencies have been reviewed in detail in this paper. The as- sumption of homogeneous Poisson process with a constant Failure rate is implied in many studies. This paper develops a stochastic point process model to investigate the aging trend in the piping Failures. To illustrate the proposed method, the paper analyzes Class 1 piping Failure data collected from the U.S. nuclear power plants NPPs and summarized in OPDE Database.
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A Stochastic Model for Piping Failure Frequency Analysis Using OPDE Data
2009Co-Authors: Xian-xun Yuan, Mahesh D. Pandey, Jovica R. RiznicAbstract:The accurate estimation of piping Failure Frequency is an important task to support the probabilistic risk assessment and risk-informed in-service inspection of nuclear power plants. Although probabilistic models have been reported in the literature to analyze the piping Failure Frequency, this paper proposes a stochastic point process model that incorporates both a time dependent trend and plant-specific (or cohort) effects on the Failure rate. A likelihood based statistical method is proposed for estimating the model parameters. A case study is presented to analyze the Class 1 pipe Failure data given in the OPDE Database.
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Bayesian Analysis of Piping Failure Frequency Using OECD/NEA Data
Volume 1: Plant Operations Maintenance Engineering Modifications and Life Cycle; Component Reliability and Materials Issues; Next Generation Systems, 2009Co-Authors: Min Wang, Mahesh D. Pandey, Jovica R. RiznicAbstract:The estimation of piping Failure Frequency is an important task to support the probabilistic risk analysis and risk-informed in-service inspection of nuclear power plant systems (NPPs). Although various probabilistic models have been proposed in the literature, this paper describes a hierarchical or two-stage Poisson-gamma Bayesian procedure to analyze this problem. In the first stage, a generic distribution of Failure rate is developed based on the Failure observations from a group of similar plants. This distribution represents the interplant (plant-to-plant) variability arising from differences in construction, operation and maintenance conditions. In the second stage, the generic prior obtained from the first stage is updated by using the data specific to a particular plant, and thus a posterior distribution of plan specific Failure rate is derived. The two-stage Bayesian procedure is able to incorporate different levels of variability in a more consistent manner. The proposed approach is applied to estimate the Failure Frequency using the OECD/NEA pipe leakage data for the U.S. nuclear plants.Copyright © 2009 by ASME
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A Point Process Model for Piping Failure Frequency Analysis Using OPDE Data
Volume 1: Plant Operations Maintenance Installations and Life Cycle; Component Reliability and Materials Issues; Advanced Applications of Nuclear Tech, 2008Co-Authors: Xian-xun Yuan, Mahesh D. Pandey, Jovica R. RiznicAbstract:The accurate estimation of piping Failure Frequency is an important task to support the probabilistic risk assessment and risk-informed in-service inspection of nuclear power plants. Although probabilistic models have been reported in the literature to analyze the piping Failure Frequency, this paper proposes a stochastic point process model that incorporates both a time dependent trend and plant specific (or cohort) effects on the Failure rate. A likelihood based statistical method is proposed for estimating the model parameters. A case study is presented to analyze the Class 1 pipe Failure data given in the OPDE Database. NOMENCLATURE
S.v. Amari - One of the best experts on this subject based on the ideXlab platform.
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Addendum to: generic rules to evaluate system-Failure Frequency
IEEE Transactions on Reliability, 2002Co-Authors: S.v. AmariAbstract:In the paper on Generic rules to evaluate system-Failure Frequency, (see ibid., vol.49, p.85-7, 2000) the authora did not consider the case of shorter mission times while presenting the rules to evaluate system-Failure Frequency. Time-specific Failure-Frequency calculations are required for the systems with shorter mission times. One of the practical uses of time-specific Failure-Frequency is in finding reasonably accurate estimates of Failure-rate and reliability of large systems consisting of repairable components, by using combinatorial methods (without using Markov models). This paper shows that, with some minor modifications, the rules in the original paper for evaluating steady-state Failure-Frequency can be used to find the time-specific Failure-Frequency.
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Generic rules to evaluate system-Failure Frequency
IEEE Transactions on Reliability, 2000Co-Authors: S.v. AmariAbstract:Frequency of Failure of a system with s-independent components can be obtained from the system availability (unavailability) expression and Failure and repair rates of the components. Although, Grouped Variable Inversion is an efficient technique to find the system availability, there is no convenient method to convert the "availability expression obtained by this technique" into an "expression for system-Failure Frequency." This paper present generic rules to find system-Failure Frequency, particularly, when the availability or unavailability expression of a system is obtained using this technique. The rules are straightforward, and produce appreciably shorter expressions for system-Failure Frequency. Examples illustrate the simplicity and efficiency of the proposed rules.
