• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.692-701
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• 2019

Recently, instrumentation and control (I&C) systems in nuclear power plants have undergone digitalization. Owing to the unique characteristics of digital I&C systems, the reliability analysis of digital systems has become an important element of probabilistic safety assessment (PSA). In a reliability analysis of digital systems, fault-tolerant techniques and their effectiveness must be considered. A fault injection experiment was performed on a safety-critical digital I&C system developed for nuclear power plants to evaluate the effectiveness of fault-tolerant techniques implemented in the target system. A software-implemented fault injection in which faults were injected into the memory area was used based on the assumption that all faults in the target system will be reflected in the faults in the memory. To reduce the number of required fault injection experiments, the memory assigned to the target software was analyzed. In addition, to observe the effect of the fault detection coverage of fault-tolerant techniques, a PSA model was developed. The analysis of the experimental result also can be used to identify weak points of fault-tolerant techniques for capability improvement of fault-tolerant techniques

• Proceedings of the KSR Conference
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• 2000.05a
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• pp.188-194
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• 2000

This study is about fault detection and fault area detection of LV circuit in Cab Cubicle system which have control of train to keep safety in High Speed Train. LV circuit is operated with diagnosis system like safety system. In this paper, we suggest a design and an implementation method to detect fault or to detect fault area automatically about LV circuit. The implemented system is tested successfully after implementation of some function. We expect reduction to diagnosis area or repair time by fault area module

• Journal of the Korean Society of Safety
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• v.34 no.4
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• pp.59-67
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• 2019

Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.49-58
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• 2011

Fault Tree Analysis(FTA) builds fault trees to perform safety analysis of systems. However, building fault trees depends on domain knowledge and expertize on target systems and consumes lots of time and efforts. In this paper, we propose a technique that builds fault trees systematically based on state-transition models which are software design artifacts. For the end, this paper identifies conditions that should be satisfied to guarantee safety of state-transition models and develop templates for fault tree construction. This paper also describes the results of appling the proposed method to railway crossing control system.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1583-1589
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• 2016

This paper proposes an improved spread spectrum time domain reflectometry (ISSTDR) using time-frequency correlation and reference signal elimination method in order to have more accurate fault determination and location detection than conventional (SSTDR) despite increased signal attenuation due to the long distance to cable fault location. The proposed method has a two-step process: the first step is to detect a peak location of the reference signal using time-frequency correlation analysis, and the second step is to detect a peak location of the correlation coefficient of the reflected signal by removing the reference signal. The proposed method was validated through comparison with existing SSTDR methods in open-and short-circuit fault detection experiments of low voltage power cables. The experimental results showed that the proposed method can detect correlation coefficients at fault locations accurately despite reflected signal attenuation so that cable faults can be detected more accurately and clearly in comparison to existing methods.

• Proceedings of the KSR Conference
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• 1999.05a
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• pp.299-306
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• 1999

The ${\mu}$-processor based-controlled system is widely used in railway signaling system. The railway signaling systems are highly required safety and reliability. It is necessary to have a fault-tolerant and fail safe concept in ${\mu}$-processor based railway signaling system. In this paper, several architectures and circuits of fault-tolerant computer system is reviewed. The basic concept of the fault-tolerant computer system will be adapted total self checking, strong fail safe, fault display circuit, logic testing circuit and system switching concepts.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.396-410
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• 2018

To assist operators to properly assess the current situation of the plant, accurate fault diagnosis methodology should be available and used. A reliable fault diagnosis method is beneficial for the safety of nuclear power plants. The major idea proposed in this work is integrating the merits of different fault diagnosis methodologies to offset their obvious disadvantages and enhance the accuracy and credibility of on-line fault diagnosis. This methodology uses the principle component analysis-based model and multi-flow model to diagnose fault type. To ensure the accuracy of results from the multi-flow model, a mechanical simulation model is implemented to do the quantitative calculation. More significantly, mechanism simulation is implemented to provide training data with fault signatures. Furthermore, one of the distance formulas in similarity measurement-Mahalanobis distance-is applied for on-line failure degree evaluation. The performance of this methodology was evaluated by applying it to the reactor coolant system of a pressurized water reactor. The results of simulation analysis show the effectiveness and accuracy of this methodology, leading to better confidence of it being integrated as a part of the computerized operator support system to assist operators in decision-making.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.43-50
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• 1990

Fault tree analysis(FTA) is available to the engineer for determining reliability of complex industrial safety system. Therefore quantitative aspects of FTA greatly multiply its power this paper proceeds of presenting the methodology of FTA, including an approach to constructing in fault tree. A working guide to the use of FTA for the purpose of cost/benefit determination in industrial safety system is given. Finally, an analytic method for uncertainty analysis of the top event of a complex system is described.