• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.396-410
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• 2018

To assist operators to properly assess the current situation of the plant, accurate fault diagnosis methodology should be available and used. A reliable fault diagnosis method is beneficial for the safety of nuclear power plants. The major idea proposed in this work is integrating the merits of different fault diagnosis methodologies to offset their obvious disadvantages and enhance the accuracy and credibility of on-line fault diagnosis. This methodology uses the principle component analysis-based model and multi-flow model to diagnose fault type. To ensure the accuracy of results from the multi-flow model, a mechanical simulation model is implemented to do the quantitative calculation. More significantly, mechanism simulation is implemented to provide training data with fault signatures. Furthermore, one of the distance formulas in similarity measurement-Mahalanobis distance-is applied for on-line failure degree evaluation. The performance of this methodology was evaluated by applying it to the reactor coolant system of a pressurized water reactor. The results of simulation analysis show the effectiveness and accuracy of this methodology, leading to better confidence of it being integrated as a part of the computerized operator support system to assist operators in decision-making.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.38-46
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• 2024

The city gas pipeline network is characterized by being widely distributed and hierarchically connected in a complex manner over a wide area. In order to monitor the status of the widely distributed network pressures with high precision, Multi-block PCA(MBPCA) is recommended. However, while MBPCA has excellent performance in identifying faulty sensors as the number of sensors increases, the fault detection performance deteriorates, and also there is a problem that the model needs to be updated entirely even if minor changes occur. In this study, we developed fault detectability index and fault identificability index to determine the effectiveness of MBPCA application block by block. Based on these indices, we distinguished MBPCA and PCA blocks and developed a fault detection and diagnostic system for the city gas pipeline network of Haean Energy Co., Ltd., and were able to solve the problems that arise when there are many sensors.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.55-55
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• 2000

This paper presents a fault detection and isolation(FDI) method based on Ceneralized Likelihood Ratio(GLR) test for the tightly coupled INS/GPS. State and measurement GLR tests detect INS or GPS fault. Once the fault is detected, Multi-hypothesized GLR scheme performs the fault isolation between INS and GPS and find which satellite malfunctions. Simulation results show that the GLR method is effective enough to detect and isolate a fault of the integrated navigation system.

• Journal of the Korea Safety Management & Science
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• v.11 no.1
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• pp.87-91
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• 2009

본 논문은 상대적으로 새로운 기법인 Parzen Density Estimation과 Multi-class SVM을 이용한 지능형 고장 탐색과 진단 방법을 제안하고 있다. 본 연구에서는 롤링 베어링을 대상으로 고장을 탐색하고 진단하기 위한 방법을 제안하는데 Parzen Density Estimation과 Multi-class SVM은 고장 클래스를 잘 표현할 수 있다. Parzen Density Estimation은 새로운 패턴 데이터의 거절과 알려진 데이터 패턴의 밀도의 평가에 의해 새로운 패턴을 찾아낼 수 있고, Multi-class SVM 기반의 방법은 여러 클래스의 고장을 support vector로 표현하여 고장 패턴을 찾아낼 수 있다. 본 연구에서는 실제의 다중 클래스를 가지는 롤링 베어링의 고장 데이터를 사용하여 고장 패턴을 탐색하는 과정을 보여주는데, 커널함수의 적절한 파라미터의 선택에 의한 Multi-class SVM 기반의 방법이 multi-layer perceptron이나 Parzen Density Estimation 방법보다 우수함을 입증한다.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.568-572
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• 2010

Self-healing is one of the techniques that assure dependability of mission-critical system. Self-healing consists of fault detection and fault recovery and fault detection is important first step that enables fault recovery but it causes overhead. We can detect fault based on model, the detection tasks that notify system's behavior and compare normal behavior model and system's behavior are heavy jobs. In this paper, we propose architecture-based multi-level self-adaptive monitoring method that complements model-based fault detection. The priority of fault detection per component is different in the software architecture. Because the seriousness and the frequency of fault per component are different. If the monitor is adapted to intensive to the component that has high priority of monitoring and loose to the component that has low priority of monitoring, the overhead can be decreased and the efficiency can be maintained. Because the environmental changes of software and the architectural changes bring the changes at the priority of fault detection, the monitor learns the changes of fault frequency and that is adapted to intensive to the component that has high priority of fault detection.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.169-176
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• 1994

This paper describes an efficient parallel evaluation algorithm for accelerating fault simulation, which can be applied to combinational circuits. The method is based on a combination of all the advantages in parallel, deductive and concurrent schemes in terms of evaluation and propagation of fautly gates for achieving high performance and handling multi-valued signal. We also propose a new fault grouping procedure to increase parallel operation of fault bits by packing active faults which occur in the same signal line densely into the same fault group. The algorithm has been implemented in C language on a Sun 3/260, and experimental results for ISCAS'85 benchmark circuits have been shown that this algorithm is 2.6 to 8.2 times faster than the conventional cocurrent fault simulation algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.2
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• pp.185-195
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• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.