• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.891-894
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• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• 융합보안논문지
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• 제6권2호
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• pp.81-89
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• 2006

시스템-레벨 진단 알고리즘은 결함의 개수가 t개를 초과하지 않는다는 t-진단가능 시스템의 특성을 이용한다. 기존의 진단 알고리즘으로 대형 멀티프로세서 시스템에서의 보다 많은 수의 결함을 처리하기에는 한계가 있다. Somani와 Peleg은 진단의 정확 여부를 판단할 수 없는 충분히 작은 개수의 노드가 존재한다는 것을 허용으로써 결함의 갯수가 t개를 초과할 경우에도 시스템을 진단하는 t/k-diagnosable 시스템을 제안하였다. 본 논문에서는 t/k-diagnosable 시스템을 이용한 하이퍼큐브 진단 알고리즘을 제안한다. 결함의 개수가 t개를 초과하는 경우에 대하여, k개의 부정확한 진단을 허용한다. 성능 실험 결과 제안 알고리즘은 HADA 알고리즘보다 우수함을 보여 주었다. 또한 제안 알고리즘은 HYP-DIAG 알고리즘과의 성능 비교에서도 비슷한 결과를 보여 준다.

• 한국산학기술학회논문지
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• 제14권9호
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• pp.4455-4461
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• 2013

본 논문은 전력기기 열화의 주요한 원인으로 알려진 부분방전의 진단을 위해 널리 사용되는 인공신경망의 계층 구조 및 입력벡터의 구성 요소의 변화에 대한 진단 성능을 검토한다. 은닉층이 1개 또는 2개인 인공신경망의 계층구조 변화에 대한 진단 성능을 비교하였으며, 입력벡터는 세라믹 커플러를 이용하여 한주기에 2048번 샘플링한 시계열 신호를 직접 사용하는 경우와 특성벡터를 추출하여 사용하는 경우를 비교하였다. 침${\leftrightarrow}$평판, 구${\leftrightarrow}$구, 침${\leftrightarrow}$침, 평판${\leftrightarrow}$평판, 구${\leftrightarrow}$평판 형태의 5가지 전극조합의 부분방전 실험으로 학습데이타를 수집하고, 시뮬레이션 연구를 수행하여 인공신경망의 진단 성능을 평가하였다.

• 한국정밀공학회지
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• 제19권3호
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• pp.52-58
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• 2002

The purpose of condition monitoring and fault diagnosis is to detect faults occurring in machinery in order to improve the level of safety in plants and reduce operational and maintenance costs. The recognition performance is important not only to gain a high recognition rate bur a1so to minimize the diagnosis failures error rate by using off effective rejection module. We examined the problem of performance evaluation for the rejection scheme considering the accuracy of individual c1asses in order to increase the recognition performance. We use the Smith's method among the previous studies related to rejection method. Nearest neighbor classifier is used for classifying the machine conditions from the vibration signals. The experiment results for the performance evaluation of rejection show the modified optimum rejection method is superior to others.

• Smart Structures and Systems
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• 제26권1호
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• pp.11-21
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• 2020

The hinge joint is the key to the overall cooperative working performance of the assembled beam bridge, and it is also the weakest part during the service period. This paper proposes a method for monitoring and evaluating the lateral cooperative working performance of fabricated beam bridges based on dynamic strain correlation coefficient indicator. This method is suitable for monitoring and evaluation of hinge joints status between prefabricated girders and overall cooperative working performance of bridge, without interruption of traffic and easy implementation. The remote cloud monitoring and diagnosis system was designed and implemented on a real assembled beam bridge. The algorithms of data preprocessing, online indicator extraction and status diagnosis were given, and the corresponding software platform and scientific computing environment for cloud operation were developed. Through the analysis of real bridge monitoring data, the effectiveness and accuracy of the method are proved and it can be used in the health monitoring system of such bridges.

• 한국지능시스템학회논문지
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• 제7권1호
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• pp.45-53
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• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

• 설비공학논문집
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• 제14권3호
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• pp.221-230
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• 2002

In this study, in-situ performance test of an air source heat pump which has a arted capacity of 10 RT is carried out. Since test conditions, such as indoor and outdoor air conditions cannot be controlled to satisfy the standard test conditions, experiments are done with the inlet air conditions as they exist. To estimate the performance for other conditions, he heat pump is modeled with a small number of selected parameters. The values of the parameters are determined from the few measurements measured on-site during normal operation. A simulation program is developed to calculate cooling capacity and power consumption t any other operating conditions. The simulation results are in good agreement with the experiment. This study provides a method of an on-site performance diagnosis of an air source heat pump.

• 한국유체기계학회 논문집
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• 제9권6호
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• pp.35-44
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• 2006

Operation conditions of a reheat cycle gas turbine for a combined cycle power plant was analyzed. Based on measured performance parameters of the gas turbine, a performance analysis program predicted component characteristic parameters such as compressor air flow, compressor efficiency, efficiencies of both the high and low pressure turbines, and coolant flows. The predicted air flow and its variation with the inlet guide vane setting were sufficiently accurate. The compressor running characteristic in terms of the relations between air flow, pressure ratio and efficiency was presented. The variations of the efficiencies of both the high and low pressure turbines were also presented. Almost constant flow functions of both turbines were predicted. The current methodology and obtained data can be utilized for performance diagnosis.