• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.774-779
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• 2004

This paper describes the fault detection and failsafe logic to be used in the Electronic Stability Program (ESP). The Aim of this paper is prevention of erroneous control in the ESP. This paper introduces the fault detection logic and evaluation of residual signals. Failsafe logic consist of four redundant sub-models and they can be used for the detection of faults in each sensor (yaw rate, lateral acceleration, steering wheel angle). We presents two mathematical residual generation method ; one is the method by the average value, and the other is the method by the minimum value of the each residual. We verify a failsafe logic using vehicle test results, also we compare vehicle model based simulation results with test vehicle results.

• 전기학회논문지
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• 제57권8호
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• pp.1470-1477
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• 2008

In this paper, a new detection method of series arc-fault signals occurring at the wiring of home appliances is proposed. The discrete wavelet transform was used for the numerical analysis of the variation rate in peak, RMS, noise energy, shoulder of the arc-fault current wave. As a results, the arc distinction threshold value of these variation rates was about 0.1 in most cases. The arc-fault current of the loads with the active PFC circuit showed a high rate of variation in noise energy and shoulder, but arc-fault current of the loads without the active PFC circuit showed a high rate of variation in peak and RMS. The arc fault current in resistive loads showed a high rate of variation in shoulder.

• 디지털융복합연구
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• 제11권11호
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• pp.335-342
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• 2013

본 연구에서는 소프트웨어 제품 테스팅 과정에서 관측고장시간에 근거한 로그형 결함 발생률을 고려한 소프트웨어 신뢰성 비용 모형에 대하여 연구 하였다. 신뢰성 분야에서 많이 사용되는 Goel-Okumoto모형을 이용한 새로운 로그 형 결함 확률을 반영한 문제를 제시하였다. 수명분포는 유한고장 비동질적인 포아송과정을 이용하고 모수 추정법은 최우 추정법을 이용 하였다. 따라서 본 논문에서는 로그형 결함 발생률을 고려한 소프트웨어 비용모형 분석을 위하여 소프트웨어 고장 시간간격 자료를 적용하여 비교 분석하였다. 이 연구를 통하여 소프트웨어 개발자들은 방출최적시기를 파악 하는데 어느 정도 도움을 줄 수 있을 것으로 사료 된다.

• Journal of Mechanical Science and Technology
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• 제14권7호
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• pp.773-781
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• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• ETRI Journal
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• 제43권1호
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• 한국소음진동공학회논문집
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• 제16권1호
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• 한국음향학회지
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• 제36권1호
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• pp.30-38
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• 2017

본 논문에서는 수동 소나 시스템을 위한 수중 음향 센서 결함 탐지 기법을 제안하였다. 일반적으로 수동 소나 시스템에서는 수십개의 음향 센서를 통해 얻은 음향 신호를 이용하여 배열 신호처리 기법을 이용해 처리된 신호를 협대역 또는 광대역 분석을 위한 2차원 영상 형태로 전시한다. 운용 소프트웨어에서 전시되는 탐지 결과는 배열 신호처리를 통해 누적된 결과값을 전시하기 때문에, 단일 센서 채널의 결함 또는 고장에 따른 신호의 이상 여부를 판단하는데 어려움이 있다. 따라서 본 논문에서는 인접 채널간 실효치 비교 및 실효치교차율(Root Mean Square Crossing-Rate, RMSCR) 분석기반 센서 자동 결함 탐지 기법을 제안하고, 결함 센서 채널에 대한 처리 기법을 비교 분석하였다. 제안된 기법의 성능 분석을 위하여 일부 연안 지역에서 실제 운용 중인 센서 배열을 통해 획득된 신호를 이용하여 결함 탐지 정확도를 측정하고, 결함 처리 기법의 성능을 비교하였다. 실험을 통해 제안된 기법이 높은 RMS의 주변소음 환경에서도 높은 결함 탐지 정확도를 보였으며, 결함 처리 기법으로는 0으로 설정 처리 기법이 가장 높은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.932-951
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• 2021

The effectiveness of testing in Model-based Testing (MBT) for Software Product Line (SPL) can be achieved by considering fault detection in test case. The lack of fault consideration caused test case in test suite to be listed randomly. Test Case Prioritization (TCP) is one of regression techniques that is adaptively capable to detect faults as early as possible by reordering test cases based on fault detection rate. However, there is a lack of studies that measured faults in MBT for SPL. This paper proposes a Test Case Prioritization (TCP) approach based on dissimilarity and string based distance called Last Minimal for Local Maximal Distance (LM-LMD) with Dice-Jaro-Winkler Dissimilarity. LM-LMD with Dice-Jaro-Winkler Dissimilarity adopts Local Maximum Distance as the prioritization algorithm and Dice-Jaro-Winkler similarity measure to evaluate distance among test cases. This work is based on the test case generated from statechart in Software Product Line (SPL) domain context. Our results are promising as LM-LMD with Dice-Jaro-Winkler Dissimilarity outperformed the original Local Maximum Distance, Global Maximum Distance and Enhanced All-yes Configuration algorithm in terms of Average Fault Detection Rate (APFD) and average prioritization time.

• 설비공학논문집
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• 제16권10호
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• pp.932-941
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• 2004

The objective of this study is to detect the multi-fault of HVAC system using a new pattern classification technique. To classify the effect of single-fault in determining the pattern, supply air temperature, OA-damper, supply fan, and air flowrate were chosen as experimental parameters. The combination of supply temperature, flow rate, supply fan and OA-damper were chosen as multi-fault conditions. Three kinds of patterns were introduced in the analysis of multi-fault problem. To solve multi-fault problem, the new pattern classification technique using residual ratio analysis was introduced to detect the multi-fault as well as single-fault. The residual ratio could diagnose single-fault or multi-fault into several patterns.

• 한국주조공학회지
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• 제42권6호
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• pp.369-376
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• 2022

다이캐스팅 공정은 다양한 산업군의 인프라 역할을 수행하는 중요한 공정이지만, 높은 불량률로 인하여 관련 기업들의 수익성 및 생산성의 한계가 있는 상황이다. 이를 타개하기 위하여, 본 연구에서는 다이캐스팅 공정의 불량 검출을 위한 산업인공지능 기반 모듈을 구성하였다. 개발된 불량 검출 모듈은 제공되는 데이터의 특징에 따라서 3단계로 동작되는 모델로 구성된다. 1단계 모델은 비지도학습 기반 이상 검출을 진행하며, 레이블이 없는 데이터셋을 대상으로 작동한다. 2단계 모델은 반지도학습 기반으로 이상 검출을 진행하며, 양품 데이터의 레이블만 존재하는 데이터셋을 대상으로 작동하며, 3단계 모델은 소수의 불량 데이터가 제공된 상황의 지도학습 모델을 기반으로 작동한다. 개발된 모델은 실제 다이캐스팅 양품 데이터를 바탕으로 96% 이상의 우수한 양품 검출 성능을 보였다.