• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.965-970
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• 2011

We develop a diagnostic system to monitor failures in an engine-assembly line. Existing techniques such as sensory analysis, time domain analysis, frequency analysis, and statistical analysis have limitations in the diagnosis of engine-assembly failure when there are abnormal vibration waveforms (crashing and damping signals) during the assembly. We use a wavelet technique to deal with crashing and damping signals. We also implement a new technique for developing diagnostic rules from sensor data, and we demonstrate its validity.

• Transactions of the Korean hydrogen and new energy society
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• v.29 no.4
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• pp.307-316
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• 2018

In this study, the failure mode and effect analysis (FMEA) of hydrogen production process by using the Thermococcus onnurineus NA1 was conducted and advanced methodology to compensate the weakness of previous FMEA methodology was applied. To bring out more quantitative and precise FMEA result for bio-hydrogen production process, fuzzy logic and potential loss cost estimated from ASPEN Capital Cost Estimator (ACCE) was introduced. Consequently, risk for releasing the flammable gases via internal leakage of steam tube which to control the operating temperature of main reactor was caution status in FMEA result without applying the fuzzification and ACCE. Moreover, probability of the steam tube plugging caused by solid property like medium was still caution status. As to apply the fuzzy logic and potential loss cost estimated from ACCE, a couple of caution status was unexpectedly upgraded to high dangerous status since the potential loss cost of steam tube for main reactor and decrease in product gases are higher than expected.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.230-231
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• 2019

This study is a development on condition based maintenance(CBM) technology which is a core item of future autonomous ships. It is developing to design & installation of condition monitoring system and acquisition & processing of data from ongoing ships for fault prediction & prognosis of engine in operation. The ultimate goal of this study is to develop a predicts and decision support software for marine engine faults. To do this, the FMEA and fault tree analysis of the main engine should be accompanied by the analysis of classification of system, identification of the components, the type of faults, and the cause and phenomenon of the failure. Finally, the CBM system solution software could predict and diagnose the failure of main engine through integrated analysis for bid-data of ongoing ships and engineering knowledge. Through this study, it is possible to pro-actively cope with abnormal signals of engine and to manage efficiently, and as a result, expected that marine accident and ship operation loss during navigation will be prevented in advance.

• Journal of Engineering Education Research
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• v.8 no.2
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• pp.64-83
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• 2005

The purpose of this study is to investigate the correlations between technological problem solving strategies and variables related with self-regulation of students in engineering college. The subjects for this study are 120 students from engineering college. After using the problem solving strategy task and self-regulation questionnaire, they were classified into two groups, upper 25% group and bottom 25% group. The data was analyzed using the SPSS 10.0 for windows. The statistical technique used for data analysis was Pearson's correlation coefficient and t-test. The major conclusions of this study are as follows. Frist, there is positive correlation between strategies of design and self-efficacy & planning. Second, there is positive correlation between strategies of trouble shooting and self-monitoring, planning and effort. Third, especially self-efficacy, one of the self-regulation subvariables, directly affects on technological problem solving strategies.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.1-10
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• 1999

A mobile computing environment is one that support user's mobility through the wireless communication technology. Users access the database and get results what they want by running mobile transactions. To run the mobile transaction correctly and to maintain the consistency I database, we need a concurrency control method to schedule transactions, a caching method to manage the cache, and a recovery method to construct a fault tolerant system. A mobile computing system is based on the existing distributed system, but we can't use recovery methods of the existing distributed system directly because of the user's mobility and the features of wireless media. So this paper presents a recovery mechanism to construct a fault tolerant mobile computing systems. Especially. we develop and analyze a recovery algorithm for server failure among types of failure which can arise in mobile computing environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.4
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• pp.278-286
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• 2008

This paper investigates the fault generation type and the cause of output interruptionsin bulky with $30^{\sim}500[KVA]$ double conversion UPS, and proposes the fault detection method to improve the reliability of power supply used in the critical load in industry. Identifies its existing way of detecting a quality of inverter output it to bypass when exceeds its expectation. Under a UPS managing system, when an inner (Power device, Controller, CPU) fault occurs it disrupts the power supply and these occurrences has been verified by the results of experiments and application results. To overcome these problems, the proposed method constructs independently a fault-detection, a bypass-control device and a triple power supply apart from the conventional UPS operation. Also the detection point is changed to the preceding of a circuit breaker, a reference of fault detection is modified to avoid any clash and the breaking equipment is attached to intercept a spread of accident. As a result of applications of these developed systems to 242 UPS which was installed purposefully to the communication power supply, the service errors has not occurred in the UPS for two years since 2006.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.35-40
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• 2017

Aircraft diagnostic systems identify system failures and nip aircraft accidents in the bud by removing hazards that hinder Safety Of Flight (SOF). The necessity for diagnostic systems is increasing as aircraft manufacturing technology is modernized. Many countries have conducted studies and developed diagnostic systems. However, studies about diagnostic systems are very few in Korea. This study defines the scope of aircraft diagnostics systems and closely considers methods to ensure the Safety Of Flight (SOF) for military aircraft.