• Smart Structures and Systems
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• 제16권2호
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• pp.329-345
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• 2015

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order $H_{\infty}$ FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.

• Smart Structures and Systems
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• 제14권3호
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• pp.377-395
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• 2014

Vibration-based fault detection and condition monitoring of rotating machinery, using statistical process control (SPC) combined with statistical pattern recognition methodology, has been widely investigated by many researchers. In particular, the discrete wavelet transform (DWT) is considered as a powerful tool for feature extraction in detecting fault on rotating machinery. Although DWT significantly reduces the dimensionality of the data, the number of retained wavelet features can still be significantly large. Then, the use of standard multivariate SPC techniques is not advised, because the sample covariance matrix is likely to be singular, so that the common multivariate statistics cannot be calculated. Even though many feature-based SPC methods have been introduced to tackle this deficiency, most methods require a parametric distributional assumption that restricts their feasibility to specific problems of process control, and thus limit their application. This study proposes a nonparametric multivariate control chart method, based on multiscale wavelet scalogram (MWS) features, that overcomes the limitation posed by the parametric assumption in existing SPC methods. The presented approach takes advantage of multi-resolution analysis using DWT, and obtains MWS features with significantly low dimensionality. We calculate Hotelling's $T^2$-type monitoring statistic using MWS, which has enough damage-discrimination ability. A bootstrap approach is used to determine the upper control limit of the monitoring statistic, without any distributional assumption. Numerical simulations demonstrate the performance of the proposed control charting method, under various damage-level scenarios for a bearing system.

• 한국정보통신학회논문지
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• 제13권4호
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• pp.737-746
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• 2009

본 연구에서는 정보가전기기에서 과부하를 방지하고 이상신호를 검출하며 화재방지를 제어할 수 있는 원 칩(one chip) 시스템 IC를 개발하였다. 이를 위하여 화재에 직접적으로 영향을 미치는 아크(arc) 검출회로와 감전을 방지하기 위한 누전검출회로를 설계하였다. 시뮬레이션과 표준트랜지스터를 이용한 bread boarding 평가에 의해 블록별로 설계된 회로를 검증하였다. 평가결과로부터 34 V 2 Metal $1.5{\mu}m$바이폴라 트랜지스터 공정을 이용하여 시스템 IC를 제작하였다. 제작된 시스템 IC와 IC응용회로를 PCB보드에 실장하여 화재방지 시스템보드의 전기적 성능을 평가하였으며, 아크와 GF(ground fault) 신호에 잘 동작함을 확인하였다.

• 한국컴퓨터산업학회논문지
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• 제3권7호
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• pp.837-844
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• 2002

소프트웨어 개발 과정에서 시스템 내에 잔존하는 결함을 발견하거나 수정하기 위해 테스트를 실시한다. 테스트 단계에서 결함을 발견하고 소프트웨어 신뢰성을 평가할 수 있다. 수리적으로 신뢰성을 평가할 수 있는 모델이 소프트웨어 신뢰도 성장 모델이다. 이 모델의 대부분은 결함의 형태가 하나이고 결함율은 일정하다라는 가정에서 진행되고 있다. 본 연구에서는 테스트 단계에서 발견되는 결함이 일정하지 않다라는 관점에서 새로운 모델을 제안하고 결함 데이터를 적용해보았다. 또한 기존의 모델과 비교 및 분석하여 타당성을 증명하였다.

• 산업경영시스템학회지
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• 제46권2호
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• pp.168-175
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• 2023

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

• Journal of applied mathematics & informatics
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• 제41권6호
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 추계공동학술대회논문집
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• pp.155-159
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• 2002

Wire ropes are used in a myriad of various industrial applications such as elevator, mine hoist, construction machinery, lift, and suspension bridge. Especially, wire rope of crane is important component to container transfer. If it happens wire rope failures in operating, it may lead to safety accident, economic power loss by productivity decline, competitive power decline of container terminal and so on. To solve this problem, we developed wire rope fault detecting system as a portable instrument, and this system is consisted of 3 parts that fault detecting part using hall sensor, permanent magnets and analog unit, and digital signal processing part using data acquisition card, monitoring part using wavelet transform, denoising method. In this paper, a wire rope is scanned by this system after makes several broken parts on the surface of wire rope artificially. All detected signal has external noise or disturbance according to circumstances. So, we applied to discrete wavelet transform to extract a signal from noisy data that was used filter. In practical applications of denoising, it is shown that wavelet pursue it with little information loss and smooth signal display. It is verified that the detecting system by denoising has good efficiency for inspecting faults of wire ropes in service. As a result, by developing this system, container terminal could reduce expense because of extension of wire ropes exchange period and could competitive power. Also, this system is possible to apply in several fields like that elevator, lift and so on.

• 한국시뮬레이션학회논문지
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• 제20권4호
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• pp.67-79
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• 2011

본 연구에서는 시계열 공정데이터 관리를 위한 모델모수 기반 이상 탐지방법을 제안한다. 일반적인 공정관리에 널리 쓰이는 전통적인 통계적 관리기법의 관리도(SPC chart)는 측정되는 데이터가 특정 분포를 따르며 상관관계가 없는 상황을 가정한다. 따라서 공정데이터 형태가 시계열데이터와 같이 특정분포를 따르지 않고, 자기상관관계를 갖는다면 전통적인 관리도로는 관리에 한계를 보인다. 본 연구는 시계열을 따르는 공정의 이상을 탐지를 위한 MPBC(Model Parameter Based Control-chart) 방법을 제안한다. 제안된 MPBC는 시계열공정을 모델링하고, 모델모수의 변화를 감지하여 공정의 이상을 탐지하는 방법이다. 시계열 공정은 ARMA(p,q) 모델을 가정하며, RLS(Recursive Least Square)를 이용하여 시계열 모델의 모수를 추정하고, 추정된 모수를 $K^2$관리도로 관리한다. 제안된 방법은 기존 알고리즘과 비교하여 시계열 공정 변화 탐지에 우수한 성능을 보였으며 시계열 데이터에 있어서 보다 효율적인 공정관리 방향을 제시한다.

• 전기학회논문지
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• 제62권4호
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• pp.577-583
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• 2013

We have analyzed influence of potential rise in negative bus, which caused by decrease of power feeding line insulation, upon protecting method of DC ground protection device which detecting potential rise between negative bus and ground in order to detect ground fault in the rubber wheel system. For this purpose, we proposed negative potential equation between negative bus and ground and calculated negative potential according to system condition changes by estimating power feeding line insulation changes in steel wheel system and rubber wheel system, and equalizing DC power feeding system when ground fault occurred. Also, in order to estimate negative potential of real system, we modeled the rubber wheel system, and simulated normal status, grounding fault occurrence and power feeding line insulation changes. In normal status, negative potential did not rise significantly regardless of vehicle operation. When ground fault occurred, negative potential rose up over 300V regardless of fault resistance. However, we also observed that negative potential rose when power feeding line insulation dropped down under $1M{\Omega}$. In conclusion, our result shows that in case of rubber wheel system unlike steel wheel system, relay will be prevented maloperation and insulation status observation can be ensured when ground over voltage relay will be set 200V ~ 300V.

• 한국정보처리학회논문지
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• 제4권1호
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• pp.311-323
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• 1997

본 논문에서는 게이트 레벌 소자와 스위치 레벨 소자가 함께 사용한 혼합형 조합 회로에서의 고착 고장(stuck-at fault) 검출을 위한 고장 시뮬레이션에 대하여 기술 한다. 실용적인 혼합형 회로의 고장 검출용으로 사용하기 위하여 게이트 레벨 및 정 적 스위치 레벨 회로는 물론 동적 스위치 레벨의 회로들도 처리할 수 있도록 한다. 또한, wired 논리 소자에서의 다중 신호 충돌 현상을 해결하기 위하여 새로운 6치 논 리값과 연산 규칙을 정의하여 신호 세기의 정보와 함께 사용한다. 고장 시뮬레이션의 기본 알고리즘으로는 게이트 레벨 조합 회로에서 주로 사용되는 병렬 패턴 단일 고장 전달(PPSFP:parallel pattern single fault propagation) 기법을 스위치 레벨 소자에 확장 적용한다. 마지막으로 스위치 레벨 소자로 구현된 ISCAS85 벤치 마크 회로와 실 제 혼합형 설계 회로에 대한 실험 결과를 통하여 본 연구에서 개발된 시스템의 효율 성을 입증한다.