The Experts below are selected from a list of 26988 Experts worldwide ranked by ideXlab platform
Jooho Choi - One of the best experts on this subject based on the ideXlab platform.
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information value based fault diagnosis of train door system under multiple operating conditions
Sensors, 2020Co-Authors: Seokgoo Kim, Nam H Kim, Jooho ChoiAbstract:While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional Probability Table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.
J L Guardado - One of the best experts on this subject based on the ideXlab platform.
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multi state system reliability analysis of hvdc transmission systems using matrix based system reliability method
International Journal of Electrical Power & Energy Systems, 2018Co-Authors: J S Contrerasjimenez, F Rivasdavalos, Junho Song, J L GuardadoAbstract:Abstract This paper presents a new reliability analysis method for High Voltage Direct Current (HVDC) transmission systems based on the Matrix-Based System Reliability method. The proposed method can compute the failure Probability of HVDC transmission systems by use of efficient matrix-based procedures. Unlike conventional system reliability methods whose practicability strongly depends on the system size and the complexity of its multiple states, the proposed method can describe any general system event in a simple matrix form and therefore provides a straightforward way of handling the system events and estimating their probabilities. The main qualities of the proposed method are demonstrated by the reliability assessment of two multi-terminal HVDC (MTDC) transmission systems with multiple derated states. The performance of the proposed method and the results obtained were compared with two traditional assessment methods: Capacity Outage Probability Table (COPT) and Monte Carlo simulation. In this comparative analysis it is shown that the proposed method is a competitive alternative for reliability analysis of MTDC systems in terms of simplicity and efficiency.
Hyun Gook Kang - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Belief Network Model Quantification Using Distribution-Based Node Probability and Experienced Data Updates for Software Reliability Assessment
IEEE Access, 2018Co-Authors: Seung Jun Lee, Sang Hun Lee, Tsong-lun Chu, Athi Varuttamaseni, Meng Yue, Jaehyun Cho, Hyun Gook KangAbstract:Since digital instrumentation and control systems are expected to play an essential role in safety systems in nuclear power plants (NPPs), the need to incorporate software failures into NPP probabilistic risk assessment has arisen. Based on a Bayesian belief network (BBN) model developed to estimate the number of software faults considering the software development lifecycle, we performed a pilot study of software reliability quantification using the BBN model by aggregating different experts’ opinions. In this paper, we suggest the distribution-based node Probability Table (D-NPT) development method which can efficiently represent diverse expert elicitation in the form of statistical distributions and provides mathematical quantification scheme. Besides, the handbook data on U.S. software development and V&V and testing results for two nuclear safety software were used for a Bayesian update of the D-NPTs in order to reduce the BBN parameter uncertainty due to experts’ different background or levels of experience. To analyze the effect of diverse expert opinions on the BBN parameter uncertainties, the sensitivity studies were conducted by eliminating the significantly different NPT estimates among expert opinions. The proposed approach demonstrates a framework that can effectively and systematically integrate different kinds of available source information to quantify BBN NPTs for NPP software reliability assessment.
Sanjoy Das - One of the best experts on this subject based on the ideXlab platform.
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bayesian network model with monte carlo simulations for analysis of animal related outages in overhead distribution systems
IEEE Transactions on Power Systems, 2011Co-Authors: Min Gui, Anil Pahwa, Sanjoy DasAbstract:This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional Probability Table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns.
Seokgoo Kim - One of the best experts on this subject based on the ideXlab platform.
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information value based fault diagnosis of train door system under multiple operating conditions
Sensors, 2020Co-Authors: Seokgoo Kim, Nam H Kim, Jooho ChoiAbstract:While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional Probability Table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.