• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.1-12
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• 2017

Preventive maintenance is required for best performance of facilities in large scale industry. Ultimately, the efficiency of production is maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually; however, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent or diagnosis error is made by an unskilled expert. Alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this study, we designed and developed an automated preventive maintenance system based on expert's experience in detecting failure, determining the cause, and predicting future system failure. We also discussed the system structure designed to reuse the expert's knowledge and its applications.

• Journal of Astronomy and Space Sciences
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• v.14 no.1
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• pp.166-178
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• 1997

Many research works are on going to monitor and diagnose diverse malfunctions of satellite systems as the complexity and number of satellites increase. Currently, many works on monitoring and diagnosis are carried out by human experts but there are needs to automate much of the routine works of them. Hence, it is necessary to study on using expert systems which can assist human experts routine work by doing automatically, thereby allow human experts devote their expertise more critical and important areas of monitoring and diagnosis. In this paper, we are employing artificial intelligence techniques to model human expert's knowledge and inference the constructed knowledge. Especially, case-based approaches are used to construct a knowledge base to model human expert capabilities which use previous typical exemplars. We have designed and implemented a prototype case-based system for diagnosing satellite malfunctions using cases. Our system remembers typical failure cases and diagnoses a current malfunction by indexing the case base. Diverse methods are used to build a more user friendly interface which allows human experts can build a knowledge base in an easy way.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.90-95
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• 2009

The application of a large power transformer into a power distribution system was inevitable due to the increase of power demand and distributed generation. However, the decrease of the power transformer‘s impedance causes the short-circuit current of the power distribution system to increase and thus, the higher short-circuit current exceeds the cut-off ratings of the protective devices such as a circuit breaker. To solve these problems, several countermeasures have been proposed to protect the power system effectively from the higher fault current and the superconducting fault current limiter (SFCL) has been expected to be the promising countermeasure. However, the current limiting effect of SFCL including its bus voltage drop compensation depends on SFCL's application location in a distributed power system. In this paper, the current limiting and the bus-voltage drop compensating characteristics of the SFCL applied into a power distribution system were studied. In addition, the quench and the recovery characteristics of the SFCLs in each location of the power distribution system were compared each other.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.367-372
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• 2008

In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the State Transition Model (STM). Based on a proposed STM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that STM method could be a useful tool for diagnosing the condition of identical BLDE motors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.87-92
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• 2019

This study is the analysis of causes of printed circuit board (PCB) in collective protection equipment failure and quality improvement. The equipment is a component of the weapon system currently in operation and serves to defend against enemy chemical and biological attack as well as heating and cooling functions. However, during operation in the military, fans of condensate assembly failed to operate. The cause of the failure is the burning of PCB. It was found that the parts were heated according to the continuous cooling operation under the high temperature environmental conditions. Accordingly, the electronic components exposed to high temperature were deteriorated and destroyed. To solve this problem, PCB apply to heatsink. The performance test of improved PCB has been completed. Futhermore system compatibility, positive pressure maintenance and noise test were performed. This improvement confirmed that no faults have occurred in PCB so far. Therefore, the quality of the equipment has improved.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.71-76
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• 2022

Since brightness is proportional to the operating current, a method of connecting several LEDs in series and driving with a constant current source is widely used for driving circuits of LED lights. Because several LEDs are connected in series, if some LEDs open due to a fault, the current path is broken and all other LEDs connected in series are turned off. In this paper, we designed a circuit to solve this problem by connecting a Zener diode having a breakdown voltage of about 0.4V higher than the LED operating voltage in parallel with each LED to create a current bypass in case of LED failure. Through simulations and experiments, it was confirmed that the current of the Zener diode hardly flows when the LED is operating normally, and that the Zener diode stably operates as a current bypass when the LED fails.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.377-384
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• 2007

With the advance of VLSI technology, the capacity and density of memories is rapidly growing. In this paper, We proposed a reallocation algorithm for faulty memory part to efficient reallocation with row and column redundant memory. Reallocation information obtained from faulty memory by only every test. Time overhead problem occurs geting reallocation information as every test. To its avoid, one test resulted from reallocation information can save to flash memory. In this paper, reallocation information increases efficiency using flash memory.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.599-601
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• 2011

무선 센서 네트워크를 구성하는 센서노드들이 클러스터를 구성하고 선출된 클러스터 헤드가 클러스터 내의 센서노드들로부터 데이터를 받아서 병합한 다음, 기지국으로 데이터를 전달하는 클러스터 기반 라우팅 방법이 연구되어 왔다. 이 클러스터 기반 라우팅 방법에서 클러스터 헤드에 고장이 발생한다면, 해당 클러스터의 데이터는 기지국으로 전달할 수 없어 데이터 신뢰성에 문제가 생긴다. 이러한 문제를 해결하기 위해, 본 논문에서는 고장감내를 지원하는 클러스터 기반 라우팅 방법을 제안한다. 제안한 방법은 각 클러스터마다 백업 클러스터 헤드를 지정하여 원래의 클러스터 헤드에 고장이 발생한다면 백업 클러스터 헤드가 그 역할을 대신하도록 함으로써 데이터 전달의 신뢰성을 보장한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.2
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• pp.366-372
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• 2020

The Army's rocket missile is a one-shot weapon system, which is produced and used for only one mission, and requires high reliability. While reliability analysis with failure data can result in underestimation of the life distribution, reliability analysis with all the non-failure data can result in overestimation of the life distribution. Under or overestimation of the life distribution can lead to cost increase by early disposal or complete observation of all rocket missiles. In order to overcome this problem, the Army suggests the guideline of the number of samples from non-failure data for reliability analysis with failure data. However, the currently used sampling method can generate errors for predicting the failure rate. To solve this problem, this study proposes a new sampling procedure for predicting a future failure rate using non-failure data. The comparison test between the currently used sampling method and the proposed sampling method is conducted and the result shows that the proposed sampling method can predict the future failure rate more accurately.