• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1507-1515
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• 2008

It is essential for electric power companies to have a quick restoration system of the faulted pole transformers which occupy most of transformers to supply stable electricity. However, it takes too much time to restore it when a transformer is out of order suddenly because we now count on operator in investigating causes of failure and making decision of recovery methods. This paper presents the concept of 'Fault pattern analysis and Restoration prediction model using Data mining techniques’, which is based on accumulated fault record of pole transformers in the past. For this, it also suggests external and internal causes of fault which influence the fault pattern of pole transformers. It is expected that we can reduce not only defects in manufacturing procedure by upgrading quality but also the time of predicting fault patterns and recovering when faults occur by using the result.

• Transactions of the Korean hydrogen and new energy society
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• v.34 no.6
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• pp.682-688
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• 2023

In this paper, analyzes the type of failure and its effect on the hydrogen fueling nozzle used in hydrogen station. Failure of hydrogen fueling nozzle was analyzed using a qualitative risk assessment method, failure mode and effect analysis. The failure data of hydrogen fueling nozzles installed in domestic hydrogen stations are collected, and the failure types are classified, checked the main components causing the failure. Criticality analysis was derived based on frequency and severity depending on the failure mode performed. A quality function is developed by a performance test evaluation item of the hydrogen fueling nozzle, and the priority order of design characteristics is selected. Through the analysis results, the elements to improve the main components for enhancing the quality and maintenance of the hydrogen fueling nozzle were confirmed.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.205-211
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• 2020

Wiring defect is a major concern for safe aircraft operations. However, troubleshooting process of a wiring defect is very difficult due to extensive and complex wiring system and installed location. Recently, time domain reflectometer (TDR) equipment that enables effective defected wiring troubleshooting has been introduced. Unfortunately, TDRs have not practically adopted by most of airlines' maintenance departments because the effectiveness and usefulness of TDRs have not been verified. This study was conducted to verify if TDRs can identify the location and type of defected aircraft wiring, and whether they can be applied for troubleshooting purposes. Experimental plan was established by using various wires and connections applied to actual aircraft and the observed results were compared with the TDR operation guide. The usability of the TDR in actual aircraft wiring defect detection may be acceptable as the experimental results showed similar results to the TDR operation guide.

• Resources Recycling
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• v.25 no.4
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• pp.11-22
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• 2016

In global automobile industry, the remanufacturing for used products has the merit to be reduced nearly 80 percent of energy consumption and resources of new product. The objective of this paper is the analysis of detailed remanufacturing processes about research object and failure modes of each process of throttle body which is one of automobile parts, to draw a FMEA and determine the degree of seriousness (S), detection (D) and occurrence (O) of many failures. And we compared the current RPN method of being used to calculate values of RPN with three suggested methods. : Summation method, Square root method, Volume method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.1
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• pp.18-23
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• 2006

Recently as the power system has been becoming massive and complicated, most of the faults bring on severe proliferation effects. Because of the complexity of the power system it is not easy to analyze faults-the calculation of current flows under fault conditions. Therefore many researches have been performed in this area. As a result of those efforts, the protective equipments for a power system have been designed to operate properly and without damage when the highest possible fault current is flowing in the power system. Most of the fault data can be also acquired from intelligent protection equipments. The fault data saved in them don't always include the fault type information. n you don't have knowledge about the fault analysis, it becomes useless. So this paper presents 3 topics to increase a reusability of them as followings. First, describes a fault data using the XML(extensible Markup Language). It would be a well-formed and valid document complied with suggested XML DTD(Document Type Definition). In this paper I suggest a standard DTD to describe the power system fault. If the XML document describes any power system faults is validated against suggested DTD, it is possible to be used in any applications. Second, sends them through the web using the XML web service. Last, presents the rapid and accurate algorithm for a fault type determination of the fault data received from the web. In the ultimate the client to request the server to analyze a fault data is provided the correct information what kind of fault is occurred.

• Journal of IKEEE
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• v.21 no.3
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• pp.252-255
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• 2017

$Mo_xW_{1-x}Si_2$ heaters were fabricated by self-propagating high-temperature synthesis (SHS) process and post sintering process. To validate the reliability of the $Mo_xW_{1-x}Si_2$ heaters, the accelerated life test (ALT) was conducted, and then lifetime to $Mo_xW_{1-x}Si_2$ heaters was estimated by using Minitab programs. Also, the failure analysis of $Mo_xW_{1-x}Si_2$ heaters after ALT was performed through electrical and structural properties. As the results, it was confirmed that the dominant failure mode of $Mo_xW_{1-x}Si_2$ heaters is the crack formation in heaters and the delamination of protective $SiO_2$ layers.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.6
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• pp.441-449
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• 2023

With the enforcement of environmental regulations by the International Maritime Organization, the market for eco-friendly ships is expanding, and ships using electric propulsion devices are emerging as a promising solution. Many studies have been conducted to predict the failure of ships, but most of them are mainly research on the main diesel engine of ships. As the ship's propulsion method changes, new data is needed to predict the failure of electric propulsion ships. In this paper aims to analyze the failure characteristics of the electric propulsion system in consideration of the difference in the type of failure between the internal diesel engine and the electric propulsion system. The ship's propulsion unit assumed a DC motor and a signal pattern for normal conditions and general failure modes, but the failure record of the electric propulsion device operated on the actual ship was not available, so it generated a failure signal for small electric motor equipment to identify the failure signal. Assuming unbalance, misalignment, and bearing failure, which are the primary failure modes of the ship's electric motor, a failure signal was generated using a "rotator vibration data generator," and the frequency band, size, and phase difference of the measured vibration signal were analyzed to analyze the characteristics of each failure condition. Finally, the characteristics of each failure condition were identified so that the signals according to the failure type could be classified.

• The Journal of the Acoustical Society of Korea
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• v.30 no.8
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• pp.446-460
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• 2011

The use of induction motors has been recently increasing with automation in aeronautical and automotive industries, and it playes a significant role. This has motivated that many researchers have studied on developing fault detection and classification systems of an induction motor in order to minimize economical damage caused by its fault. With this reason, this paper proposed feature vector extraction methods based on STE (short-time energy)+SVD (singular value decomposition) and DCT (discrete cosine transform)+SVD techniques to early detect and diagnose faults of induction motors, and classified faults of an induction motor into different types of them by using extracted features as inputs of BPNN (back propagation neural network) and multi-layer SVM (support vector machine). When BPNN and multi-lay SVM are used as classifiers for fault classification, there are many settings that affect classification performance: the number of input layers, the number of hidden layers and learning algorithms for BPNN, and standard deviation values of Gaussian radial basis function for multi-layer SVM. Therefore, this paper quantitatively simulated to find appropriate settings for those classifiers yielding higher classification performance than others.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.174-179
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• 2015

Failure Mode and Effects Analysis (FMEA) has been used by Dynamic Positioning (DP) system for risk and reliability analysis. However, there are limitations associated with its implementation in offshore project. 1) since the failure data measured from the SCADA system is missing or unreliable, assessments of Severity, Occurrence, Detection are based on expert's knowledge; 2) it is not easy for experts to precisely evaluate the three risk factors. The risk factors are often expressed in a linguistic way. 3) the relative importance among three risk factors are rarely even considered. To solve these problems and improve the effectiveness of the traditional FMEA, we suggest a Fuzzy-FMEA method for risk and failure mode analysis in Dynamic Positioning System of offshore. The information gathered from DP FMEA report and DP FMEA Proving Trials is expressed using fuzzy linguistic terms. The proposed method is applied to an offshore Dynamic Positioning system, and the results are compared with traditional FMEA.