• 대한전기학회논문지:전력기술부문A
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• 제54권11호
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• pp.525-532
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• 2005

This paper presents a new two-terminal numerical algorithm for fault location estimation and for faults recognition using the synchronized phaser in time-domain. The proposed algorithm is also based on the synchronized voltage and current phasor measured from the assumed PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. Also the arc voltage wave shape is modeled numerically on the basis of a great number of arc voltage records obtained by transient recorder. From the calculated arc voltage amplitude it can make a decision whether the fault is permanent or transient. In this paper the algorithm is given and estimated using DFT(discrete Fourier Transform) and the LES(Least Error Squares Method). The algorithm uses a very short data window and enables fast fault detection and classification for real-time transmission line protection. To test the validity of the proposed algorithm, the Electro-Magnetic Transient Program(EMTP/ATP) is used.

• Journal of Magnetics
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• 제19권1호
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• pp.72-77
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• 2014

This paper proposes an indirect fault detection method for an onboard degaussing coil system, installed to reduce the underwater magnetic field from the ferromagnetic hull. The method utilizes underwater field signals measured at specific magnetic treatment facilities instead of using time-consuming numerical field solutions in a three-dimensional space. An equivalent magnetic charge model combined with a material sensitivity formula is adopted to predict fault coil locations. The purpose of the proposed method is to yield reliable data on the location and type of a coil breakdown even without information on individual degaussing coils, such as dimension, location and number of turns. Under several fault conditions, the method is tested with a model ship equipped with 20 degaussing coils.

• 동력기계공학회지
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• 제14권2호
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• pp.40-47
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• 2010

This paper proposes a fault detection system to detect the strip rupture in six-high stand Cold Rolling Mills based on transient current signal of an electrical motor. For this work, signal smoothing technique is used to highlight precise feature between normal and fault condition. Subtracting the smoothed signal from the original signal gives the residuals that contains the information related to the normal or faulty condition. Using residual signal, discrete wavelet transform is performed and acquire the signal presenting fault feature well. Also, feature extraction and classification are executed by using PCA, KPCA and SVM. The actual data is acquired from POSCO for validating the proposed method.

• Journal of Positioning, Navigation, and Timing
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• 제12권3호
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• pp.257-269
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• 2023

This paper presents a method for detecting faults in data obtained from the Automatic Identification System (AIS) of surface vessels. The data include latitude, longitude, Speed Over Ground (SOG), and Course Over Ground (COG). We derive two methods that utilize two models: a constant state model and a derivative augmented model. The constant state model incorporates noise variables to account for state changes, while the derivative augmented model employs explicit variables such as first or second derivatives, to model dynamic changes in state. Generally, the derivative augmented model detects faults more promptly than the constant state model, although it is vulnerable to potentially overlooking faults. The effectiveness of this method is validated using AIS data collected at a harbor. The results demonstrate that the proposed approach can automatically detect faults in AIS data, thus offering partial assistance for enhancing navigation safety.

• 한국항해항만학회지
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• 제33권10호
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• pp.671-678
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• 2009

현재 진동 정보를 통해 기계 설비의 상태나 고장 유무를 판단하는 연구들이 다수 진행 중에 있는데, 대부분의 연구에서는 설비에 대한 진동을 모니터링하거나 고장 유무를 판별하여 사용자에게 알리는 수준이다. 본 논문에서는 진동 정보 적용 대상을 선박으로 정하고, 진동에 의한 고장 진단과 판별을 보다 정교하게 수행하는 선박 엔진 감지 기법과 시스템을 제안하였다. 일차적으로 이중화된 진동 정보 판별 기법을 적용하여 진동 정보를 확인한 다음에 고장 유무를 검사한다. 만일 고장이 발생한 경우에는 적분을 이용하여 고장 진동 파형에 대한 넓이를 기준으로 어떤 유형의 고장인지를 판별할 수 있는 기법을 적용하였다. 또한 선박의 진동 경향 분석과 엔진 안전 보존을 목적으로 진동 정보를 데이터베이스에 저장하고 추적할 수 있도록 시스템을 구현하였다. 제안 시스템을 선박 엔진의 고장 판별 유무와 고장 진동 파형 감별 인자에 대해 실험을 수행한 결과 고장 판별은 약 98% 정확성을 가졌고 고장 진동 파형 감별에서는 약 72% 정확성을 가졌다.

• 한국CDE학회논문집
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• 제17권2호
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• pp.97-110
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• 2012

High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.

• 지구물리와물리탐사
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• 제23권2호
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• pp.97-114
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• 2020

최근 과학기술 및 공학 전 분야에서 기계 학습을 적용하는 연구들이 매우 활발하게 수행되고 있다. 탄성파 탐사 분야 또한 해석, 처리, 취득 등 모든 영역에서 기계 학습을 적용한 연구들이 빠르게 증가하는 추세이다. 그 중 단층 해석은 탄성파 자료 해석 분야에 있어 가장 중요한 기술 중 하나이며, 기계 학습을 적용하기에 가장 적합한 분야이기도 하다. 이 논문에서는 다양한 기계 학습 기법들에 대해 소개하고 단층 해석에 적합한 기법들과 그 이유를 기술하였다. 물리탐사 분야의 저명한 국제 학술지에 게재된 논문과 국제 학술대회 발표 사례들을 조사하여 연도별, 분야별 연구 현황을 정리하였으며, 그 중 기계 학습을 사용한 단층 해석 연구들을 집중적으로 분석하였다. 단층 해석 기술은 입력 자료 및 기계 학습 모델의 형태에 따라 탄성파 속성 기반 기술, 탄성파 이미지 기반 기술, 원시자료 기반 기술로 나누어 그 장단점을 기술하였다.

• 산업경영시스템학회지
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• 제44권3호
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• 한국항행학회논문지
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• 제17권5호
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• pp.560-567
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• 2013

본 논문에서는 일반적으로 사용되고 있는 개발 및 분석용 프로그램을 이용하여 시험요구서가 개발되지 않은 ASIC을 대상으로 결함을 검출하는 방법을 제안한다. 시험요구서가 없는 경우, 회로의 동작을 파악하기 힘들어 어떤 칩에서 결함이 발생하였는지 발견하기 어렵다. 따라서 ASIC의 로직 데이터를 분석하여 결함 검출을 위한 시험요구서를 작성하고, 시험요구서에 따라 제작된 Dynamic Pattern 신호를 이용하여 게이트 레벨에서 입출력 핀 신호 제어를 통해 고장진단을 한다. 실험결과 제안된 기법을 비메모리 회로에 적용하여 우수한 결함 검출능력을 확인하였다.