• Journal of the Korean Society for Railway
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• v.14 no.6
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• pp.515-525
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• 2011

In this study, Urban maglev is applied to inductive-loop speed and position detection system for the top-level classification system for the entire system, and performed functional analysis On-board signal equipment, Wayside-signal equipment divided by the reliability, availability, maintainability, and safety through analysis of the proposed formula. RDB and by applying a system service for each device was calculated to availability, safety analysis. The PHA, FMEA, HAZOP over the Top Event of the FTA is performed by presenting the results. This also shows approach methods and relative activities for project to accomplish and ensure the system requirements.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.11
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• pp.65-73
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• 2007

This paper introduces design-for-test (DFT) techniques for low-cost system-on-chip (SoC) test. We present a Scan-Test method that controls IEEE 1500 wrapper thorough IEEE 1149.1 SoC TAP (Test Access Port) and design an at-speed test clock generator for delay fault test. Test cost can be reduced by using small number of test interface pins and on-chip test clock generator because we can use low-price automated test equipments (ATE). Experimental results evaluate the efficiency of the proposed method and show that the delay fault test of different cores running at different clocks test can be simultaneously achieved.

• Journal of Drive and Control
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• v.14 no.2
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• pp.30-36
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• 2017

This paper presents a sliding mode observer-based fault detection algorithm for steering inputs of an all-terrain crane. All-terrain cranes with multi-axles have several steering modes for various working purposes. Since steering angles at the other axles except the first wheel are controlled by using the information of steering angle at the first wheel, a reliable signal of the first axle's steering angle should be secured for the driving safety of cranes. For the fault detection of steering input signal, a simplified crane model-based sliding mode observer has been used. Using a sliding mode observer with an equivalent output injection signal that represents an actual fault signal, a fault signal in steering input was reconstructed. The road steering mode of the crane's steering system was used to conduct performance evaluations of a proposed algorithm, and an arbitrary fault signal was applied to the steering angle at the first wheel. Since the road steering mode has different steering strategies according to different speed intervals, performance evaluations were conducted based on the curved path scenario with various speed conditions. The design of algorithms and performance evaluations were conducted on Matlab/Simulink environment, and evaluation results reveal that the proposed algorithm is capable of detecting and reconstructing a fault signal reasonably well.

• The KIPS Transactions:PartC
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• v.11C no.3
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• pp.387-394
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• 2004

One of the main challenges in personal communication services(PCS ) Is to locate many mobile terminals that nay move from place to place frequently. This kind of system operation is called location management. This task entails sophisticated signaling traffic and database queries. Several strategies have been proposed to improve the efficiency of location management. These strategies use location register databases to store the current locations of mobile terminals, and are vulnerable to failure of the location register databases. In this paper, we propose a fault-tolerant pointer forwarding scheme with distributed home location register in order to tolerate the failure of location registers. The performance of the proposed scheme is evaluated analytically by simulation, and Is compared with Biaz's bypass forwarding strategy and two-path forwarding strategy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.11
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• pp.1009-1017
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• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• Plant Journal
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• v.8 no.3
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• pp.49-54
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• 2012

In this study, the way how we can find the defects of motor windings in advance will be discussed. The magnetic flux spectrum in the high frequency region of the large motor was analyzed based on the actual fault practices related with motor windings. In case of defective motor relative amplitude ratio of the stator slot frequency to its sideband was very high compared to that of healthy motor. And the defective signal related with motor windings was indicated in advance in the magnetic flux spectrum prior to over 1 month before failure. Considering this aspect it can be estimated that magnetic flux spectrum in the high frequency region has the excellent predictive diagnostic capability.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• 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.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.1-9
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• 2024

The introduction of smart factory digital twins is a concept that has already been proposed to increase productivity in the manufacturing industry through CPS(Cyber Physics System), and has been applied to specific industrial process stages or partially introduced in stages where simulation is required. However, with the recent development of the 4th Industrial Revolution technology, it is receiving attention again along with XR (Extended Reality) technology. However, because there are not many effective cases, this study analyzed the devices, equipment, and technology of the manufacturing process to build a digital twin applying digital threads and synchronized signals and information to control, remote control, and produce intelligent process automation equipment. A platform capable of analyzing information was proposed and developed. Through this, we designed and built an XR content service platform that can support artificial intelligence and developed it to enable control, remote control, and analysis of production information. A possible platform was proposed and developed. We hope that this study will be helpful in conducting research on many cases, and in the future, expanded research on increasing productivity in each part of the process and production is needed through intelligent models.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1656-1666
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• 1999

The more complex and large digital circuits become, the more important delay test becomes which guarantees that circuits operate in time. In this paper, the proposed algorithm is developed, which enable the fast indirect implication for efficient test pattern generation in sequential circuits of standard scan environment. Static learning algorithm enables application of a new implication value using contrapositive proposition. The static learning procedure found structurally, analyzes the gate structure in the preprocessing phase and store the information of learning occurrence so that it can be used in the test pattern generation procedure if it satisfies the implication condition. If there exists a signal line which include all paths from some particular primary inputs, it is a partitioning point. If paths passing that point have the same partial path from primary input to the signal or from the signal to primary output, they will need the same primary input values which separated by the partitioning point. In this paper test pattern generation can be more effective by using this partitioning technique. Finally, an efficient delay fault test pattern generator using indirect implication is developed and the effectiveness of these algorithms is demonstrated by experiments.