• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.1
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• pp.23-28
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• 2015

Measuring the distances between signals in the signal space is usually determined by obtaining the ideal metric which is not easy to obtain. In this research we have investigated the scheme that measures the distances between the signals constructed with the measured voltage signals connected to electric apparatus using Kalman filter and exponential mapping. The metric is defined on the feature signals obtained via the estimation process of a Kalman filter and the mapping process using the exponential transformation. Diagnosis is on the voltage fluctuations is applied to determining whether the system is in the stable state or not due to the unexpected accidents, such as power overcharge, discharge, outages flow may be the cause of the accident. The decision making scheme evaluated with respect to the effectiveness and the degree of complication with different variances. Two methods, the Hard Limit Threshold Scheme(HLTS) and the Interval Energy Scheme(IES) are proposed and compared. In experiments the IES shows better tolerance to impulse noise than the HLTS.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.36-54
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• 2014

Resource sharing is a major advantage of distributed computing. However, a distributed computing system may have some physical or virtual resource that may be accessible by a single process at a time. The mutual exclusion issue is to ensure that no more than one process at a time is allowed to access some shared resource. The article proposes a token-based mutual exclusion algorithm for the clustered mobile ad hoc networks (MANETs). The mechanism that is adapted to handle token passing at the inter-cluster level is different from that at the intra-cluster level. It makes our algorithm message efficient and thus suitable for MANETs. In the interest of efficiency, we implemented a centralized token passing scheme at the intra-cluster level. The centralized schemes are inherently failure prone. Thus, we have presented an intra-cluster token passing scheme that is able to tolerate a failure. In order to enhance reliability, we applied a distributed token circulation scheme at the inter-cluster level. More importantly, the message complexity of the proposed algorithm is independent of N, which is the total number of nodes in the system. Also, under a heavy load, it turns out to be inversely proportional to n, which is the (average) number of nodes per each cluster. We substantiated our claim with the correctness proof, complexity analysis, and simulation results. In the end, we present a simple approach to make our protocol fault tolerant.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.10
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• pp.801-807
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• 2009

In this paper, we propose a novel shoot-through protection circuit and pulse generator for half-bridge driver IC. We designed a robust half-bridge driver IC over a variation of processes and power supplies. The proposed circuit is composed a delay circuit using a beta-multiplier reference. The proposed circuit has a lower variation rate of dead time and pulse-width over variation of processes and supply voltages than the conventional circuit. Especially, the proposed circuit has more excellent pulse-width matching of set and reset signals than the conventional circuit. Also, the proposed pulse generator is prevented from fault operations using a logic gate. Dead time and pulse-width of the proposed circuit are typical 250 ns, respectively. The variation ratio is 68%(170 ns) of maximum over variation of processes and supply voltages. The proposed circuit is designed using $1\;{\mu}m$ 650 V BCD (Bipolar, CMOS, DMOS) process parameter, and the simulations are carried out using Spectre simulator of Cadence corporation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.10
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• pp.54-59
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• 2019

The final reduction drive in tactical vehicles has a wear-pad that helps to maintain adequate end floating when the hub assay operates. The input axis and sun gear move repeatedly with the axis when tactical vehicle is operating. The hub assay is designed so that the wear pads won't seize during operation. Seizure of the wear pads during operation results in oil leakage. In our study, the fault mechanism was analyzed to prevent the seizure of the wear pads and an optimal design for the shape and material of the wear-pad was explored. We then observed the changes in temperature, shape, and material of several important parts.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.10
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• pp.102-111
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• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.7
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• pp.80-88
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• 2021

The behavior of the injection system was determined through FFT and PSD analysis of the pressure data of the common rail, and when the diesel fuel is mixed with water, the pressure data of the common rail, depending on the water content and engine rotation speed, represent a different frequency component distribution. Recently, a theory has been suggested that mixing diesel fuel with water controls engine overheating, fuel efficiency, NOx, CO, etc., but if water content exceeds 10%, it can have a fatal adverse effect on the engine's injection system. In the future, it is necessary to promote fault diagnosis and prediction studies of diesel engines using FFT and PSD results from common rail pressure data.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.11
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.14-22
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• 2021

A plan for the development of reliability-based ROK amphibious assault vehicles is proposed. By analyzing the development case of the U.S. EFV, considerations for the successful development of the next-generation Korea Forces amphibious assault vehicle are presented. If the vehicle reliability can be improved to the level of the fourth highest priority electric unit for power units, suspensions, decelerators, and body groups, which have the highest priority among fault frequency items, a system level MTBF of 36.4%↑ can be achieved, and the operational availability can be increased by 3.5%↑. The next-generation amphibious assault vehicles must fulfill certain operating and performance requirements, the underlying systems must be built, and sequencing of the hybrid engine and the modular concept should be considered. Along with big-data- and machine-learning-based failure prediction, machine maintenance based on augmented reality/virtual reality and remote maintenance should be used to improve the ability to maintain combat readiness and reduce lifecycle costs.