• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.354-362
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• 2010

This paper presents a non-linear control chart based on support vector machine regression (SVM-R) to improve the accuracy of fault detection of cyclic signals. The proposed algorithm consists of the following two steps. First, the center line of the control chart is constructed by using SVM-R. Second, we calculate control limits by variances that are estimated by perpendicular and normal line of the center line. For performance evaluation, we apply proposed algorithm to the industrial data of the chemical vapor deposition process which is one of the semiconductor processes. The proposed method has better fault detection performance than other existing method

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.88-94
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• 2007

This study deals with on-line fault detection and diagnosis for heat exchanger of variable speed refrigeration system. Conventional studies about fault of heat exchanger in refrigeration system have used temperature and pressure information. The temperature and pressure are able to be used valuably for faults detection of constant speed refrigeration system. However in case of variable speed refrigeration system, the temperature and pressure are no longer useful information for fault detection due to compensation effect of feedback controller. While current information is possible to detect faults of variable speed refrigeration system. The current information was detected in an inverter, it was used after transforming rms value. The faults of variable speed refrigeration system are divided into electrical faults and mechanical faults. We performed fault detection and diagnosis about heat exchanger among mechanical faults such as condenser fouling and evaporator fan fouling through some experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.10
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• pp.880-887
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• 2005

This paper presents a fault diagnosis(detection and isolation) approach for the Ammunition Transport Automation system(ATAS). Due to limited time and information available during its cyclic operation, the on-line fault detection algorithm consists of sequential test logics referring to the normal states, which can be considered as a kind of expert system. If a failure were detected, the off-line isolation algorithm finds the fault location through trained ART2 neural network. By the results of simulations and some on-line field test, it has been shown that the presented approach is effective enough and applicable to related automation systems.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.2
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• pp.151-156
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• 2019

The aim of this investigation is to detect specific waveforms in a distribution line prior to the occurrence of a fault. Conditions were introduced such that a feeder remote terminal unit (FRTU) of the distribution automation system selects and stores fault waveforms from the different waveforms detected in the distribution line. In addition, an algorithm was developed to detect specific waveforms from the fault waveforms stored using the FRTU. This algorithm exploits the duration and periodicity of harmonic changes in voltage and current. The efficacy of the algorithm was confirmed based on the measurements of fault waveforms in an actual distribution line. The results indicated that faults in a distribution line can be predicted via experimental measurements.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.343-346
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• 1996

A new method for on-line induction motor fault detection is presented in this paper. This system utilizes unsupervised-learning clustering algorithm, the Dignet, proposed by Thomopoulos etc., to learn the spectral characteristics of a good motor operating on-line. After a sufficient training period, the Dignet signals one-phase ground fault, or a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, on-line failure prediction is possible with this system without requiring information on the motor of load characteristics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.3
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• pp.376-382
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• 2014

E-Actuator is an essential part of an eVGT, it receives the command from the main ECU and controls the vane. An e-Actuator failure can cause an abrupt change in engine output and it may induce an accident. Therefore, it is required to detect anomalies in the e-Actuator in real time to prevent accidents. In this paper, an e-Actuator fault detection method using on-line parameter identification is proposed. To implement on-line fault detection algorithm, many constraints are considered. The test input and sampling rate are selected considering the constraints. And new recursive system identification algorithm is proposed which reduces the memory and MCU power dramatically. The relationship between the identified parameters and real elements such as gears, spring and motor are derived. The fault detection method using the relationship is proposed. The experiments with the real broken gears show the effectiveness of the proposed algorithm. It is expected that the real time fault detection is possible and it can improve the safety of eVGT system.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.460-467
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• 2008

This paper presents a new numerical algorithm for the fault location estimation and arcing fault detection when a single-phase arcing ground fault occurs on a transmission line. The proposed algorithm derived in the spectrum domain is based on the synchronized voltage and current samples measured from the PMUs(Phasor Measurement Units) installed at both ends of the transmission lines. In this paper, the algorithm uses DFT(Discrete Fourier Transform) for estimation. The algorithm uses a short data window for real-time transmission line protection. Also, from the calculated arc voltage amplitude, a decision can be made whether the fault is permanent or transient. The proposed algorithm is tested through computer simulation to show its effectiveness.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.1
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• pp.120-127
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• 1991

The purpose of this study is to develop a fault detection and diagnosis scheme that can monitor process fault and instrument fault of a steam generator. The suggested scheme consists of a Kalman filter and two bias estimators. Method of detecting process and instrument fault in a steam generator uses the mean test on the residual sequence of Kalman filter, designed for the unfailed system, to make a fault decision. Once a fault is detected, two bias estimators are driven to estimate the fault and to discriminate process fault and instrument fault. In case of process fault, the fault diagnosis of outlet temperature, feed-water heater and main steam control value is considered. In instrument fault, the fault diagnosis of steam genrator's three instruments is considered. Computer simulation tests show that on-line prompt fault detection and diagnosis can be performed very successfully.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.118-123
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• 2004

This paper studies islanding detection of DG(distributed generation) considering fault location. Through the past studies, we found that islanding detection has been studied that DG disconnected when power islanding was detected by power state change and output change of DG. But, fault location was not considered. For example, fault in adjacent distribution line, fault on interconnection line fault, load shave by overload and normal operation were not considered. In this paper, We distinguish these considerations through power state change. Also, we proved islanding detection algorithm through PSCAD/EMTDC simulation.