• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.709-711
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• 2005

Developing fault location system for underground power cable which can detect its fault location exactly require very high speed data acquisition and signal processing capability. We are developing fault location system which is different from conventional fault locator. This fault location system monitor underground power cable by using on-line speed current sensor and if there are an accident, it record its transient signal and calculate fault location by analyzing it. Signals which acquired when power cable fault arise, showed transient characteristics and its frequency band is very hish. So, to develop fault location system, we designed special high speed data acquisition and signal processing board. In this thesis, we describe on data acquisition and signal processing H/W design for fault location system on underground power cable.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.811-814
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• 1998

REcent research about current testing($\textrm{I}_{DDQ}$ testing) has been emphasizing that $\textrm{I}_{DDQ}$ testing in addition to the logical voltage testing is necessary to increase the fault coverage. The $\textrm{I}_{DDQ}$. testing can detect physical faults other than the classical stuck-at type fault, which affect reliability. One of the most critical issues in the $\textrm{I}_{DDQ}$ testing is to insert a built-in current sensor (BICS) that can detect abnormal static currents from the power supply or to the ground. This paper presents a new BICS for internal current testing for large CMOS logic circuits. The proposed BICS uses a single phase clock to minimize the hardware overhead. It detects faulty current flowing and converts it into a corresponding logic voltage level to make converts it into a corresponding logic voltage level to make it possible to use the conventional voltage testing techniqeus. By using current mirroring technique, the proposed BICS can work at very high speed. Because the proposed BICS almost does not affects normal operation of CUT(circuit under test), it can be used to a very large circuit without circuit partitioning. By altenating the operational modes, a circuit can be $\textrm{I}_{DDQ}$-tested as a kind of self-testing fashion by using the proposed BICS.

• 산업경영시스템학회지
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• 제47권2호
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• pp.10-20
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• 2024

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder's status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

• 드라이브 ㆍ 컨트롤
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• 제16권3호
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• pp.23-32
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• 2019

This study proposed a fault tolerant control algorithm for electro-hydraulic brake systems where permanent magnet synchronous motor (PMSM) drive is adopted to boost the braking pressure. To cope with motor position sensor faults in the PMSM drive, a braking pressure controller based on an open-loop speed control method for the PMSM was proposed. The magnitude of the current vector was determined from the target braking pressure, and motor rotational speed was derived from the pressure control error to build up the braking pressure. The position offset of the pump piston resulting from a leak in the hydraulic system is also compensated for using the open-loop speed control by moving the piston backward until it is blocked at the end of stroke position. The performance and stability of the proposed controller were experimentally verified. According to the results, the control algorithm can be utilized as an effective means of degraded control for electro-hydraulic brake systems in the case that a motor position sensor fault occurs.

• 한국산업융합학회 논문집
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• 제23권5호
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권3호
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• pp.384-390
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• 2012

This paper proposes a fault diagnosis method of the line-to-line voltage sensors in 3-phase AC/DC pulse width modulation (PWM) converters. The line-to-line voltage sensors are an essential device to obtain the information of the grid voltages for controlling the 3-phase AC/DC PWM converters. If the line-to-line voltage sensors are mismeasured by various faults, the voltage sensors can obtain wrong information of the grid voltage. It has an adverse effect on the control of the converter. Therefore, the converter causes the unbalance input AC current and the DC-link voltage ripple in the 3-phase AC/DC PWM converter. Hence, fast fault detection and fault tolerant control are needed. In this paper, the fault diagnosis method is proposed and verified through simulations and experiments.

• 대한임베디드공학회논문지
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• 제4권1호
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• pp.23-28
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• 2009

Testing of analog(and mixed-signal) circuits has been a difficult task for test engineers and effective test techniques to solve these problems are required. This paper develops a new technique which increases fault detection and diagnosis rates for analog circuits by using extended MTSS (Modified Time Slot Specification) technique based on MTSS proposed by the author. High performance current sensors with digital outputs are used as core components for these techniques. A fault diagnosis structure with minimal hardware overhead in ATE is also described.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1005-1018
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• 2017

This paper proposes an App Visualization (AppV) based on IoT Self-diagnosis Micro Control Unit (ISMCU) for accident prevention. It collects a current status of a vehicle through a sensor, visualizes it on a smart phone and prevents vehicles from accident. The AppV consists of 5 components. First, a Sensor Layer (SL) judges noxious gas from a current vehicle and a driver's driving habit by collecting data from various sensors such as an Accelerator Position Sensor, an O2 sensor, an Oil Pressure Sensor, etc. and computing the concentration of the CO collected by a semiconductor gas sensor. Second, a Wireless Sensor Communication Layer (WSCL) supports Zigbee, Wi-Fi, and Bluetooth protocol so that it may transfer the sensor data collected in the SL to ISMCU and the data in the ISMCU to a Mobile. Third, an ISMCU integrates the transferred sensor information and transfers the integrated result to a Mobile. Fourth, a Mobile App Block Programming Tool (MABPT) is an independent App generation tool that changes to visual data just the vehicle information which drivers want from a smart phone. Fifth, an Embedded Module (EM) records the data collected through a Smart Phone real time in a Cloud Server. Therefore, because the AppV checks a vehicle' fault and bad driving habits that are not known from sensors and performs self-diagnosis through a mobile, it can reduce time and cost spending on accidents caused by a vehicle's fault and noxious gas emitted to the outside.

• Progress in Superconductivity
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• 제6권2호
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• pp.108-112
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• 2005

We investigated temperature behavior of superconducting fault current limiters (SFCLs) during quenches. Knowledge on temperature behavior during quenches is important to the design of SFCLs, because the temperature of SFCLs is related to their stability. SFCLs were fabricated by patterning $Au/YBa_2Cu_3O_7$ thin films grown on sapphire substrates into meander lines by photolithography. A gold film grown on the back side of the substrate was patterned into a meander line, and used as a temperature sensor. The front meander line was subjected to simulated AC fault currents, and the back line to DC current. They were immersed in liquid nitrogen during the experiment for effective cooling. Overall, temperature at the back side of SFCLs was close to that at the front side. It was closer at the beginning of faults, and at lower applied voltages. Temperature distribution at the back side was even except at the edge, as at the front side. These results tell that the whole SFCL was heated to similar degree during quenches, and that effective cooling of SFCLs at the back side is as important to the stability of SFCLs as at the front side. The results could be explained with the concept of heat transfer within the film.

• 대한조선학회논문집
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• 제59권2호
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• pp.125-133
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• 2022

This paper proposes a fault detection method for a Unmanned Surface Vehicle (USV) with overactuated system. Current status information for fault detection is expressed as a scalogram image. The scalogram image is obtained by wavelet-transforming the USV's control input and sensor information. The fault detection scheme is based on Convolutional Neural Network (CNN) algorithm. The previously generated scalogram data was transferred learning to GoogLeNet algorithm. The data are generated as scalogram images in real time, and fault is detected through a learning model. The result of fault detection is very robust and highly accurate.