• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.81-84
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• 2000

Rotating machine is used extensively and plays important roles in the industrial field. Therefore when rotating machine get out of order, it is necessary to know reasons then deal with the troubles immediately. So many studies far diagnosis of rotating machine are being done. However by this time most of study has an interest in gaining a high recognition But without considering error $rate^{(1)(2)(3)}$ , it is not desirable enough to apply h the actual application system. If the manager of system receives the result misjudging the condition of rotating machine and takes measures, we would lose heavily. So in order to play the creditable diagnosis, we must consider error rate. T h ~ t is. it must be able to reject the result of misjudgment. This study uses nearest neighbor classifier for diagnosis of rotating $machine^{(4)(8)}$ And the Smith's rejection $method^{(1)}$ used to recognize handwritten charter is done. Consequently creditable diagnosis of rotating machine is proposed.

• 한국지능시스템학회논문지
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• 제25권5호
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• pp.425-430
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• 2015

본 논문은 딥러닝 알고리즘을 적용한 깊은신경망을 이용하여 회전 객체의 분류 효율성을 높이기 위한 연구이다. 회전객체의 분류 실험을 위하여 데이터는 COIL-20을 사용하며 객체의 2/3영역을 학습시키고 1/3영역을 유추하여 분류한다. 연구에 이용된 3가지 분류기는 주성분 분석법을 이용해 데이터의 차원을 축소하면서 특징값을 추출하고 유클리디안 거리를 이용하여 분류하는 PCA분류기와 오류역전파 알고리즘을 이용하여 오류 에너지를 줄여가는 방식의 MLP분류기, 마지막으로 pre-training을 통하여 학습데이터의 관찰될 확률을 높여주고 fine-tuning으로 오류에너지를 줄여가는 방식의 딥러닝을 적용한 DBN분류기이다. 깊은신경망의 구조별 오류율을 확인하기 위하여 은닉층의 개수와 은닉뉴런의 개수를 변경해가며 실험하고 실제로 가장 낮은 오류율을 나타내는 구조를 기술한다. 가장 낮은 오류율을 보였던 분류기는 DBN을 이용한 분류기이다. 은닉층을 2개 갖는 깊은신경망의 구조로 매개 변수들을 인식에 도움이 되는 곳으로 이동 시켜 높은 인식률을 보여줬다.

• 비파괴검사학회지
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• 제15권4호
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• pp.520-530
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• 1996

본 연구에서는 음향방출법을 이용하여 로터리 압축기의 인공 결함을 분류하기 위한 연구를 수행하였다. 이를 위해 프로그램을 개발하였고 선형 분류기, 경험적 Bayesian 분류기, 신경 회로망 분류기를 함께 사용하여 비교하였다. 그 결과 신경 회로망 분류기가 인식률 면에서 유리하였으며 신경 회로망 분류기의 경우 99%이상의 인식률을 얻을 수 있었다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회B
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• pp.2423-2428
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• 2008

A turbo classifier having a rotating rotor of two stage classification region has been developed to have a cut size of 1 micro meter. Particle number concentrations were counted using Aerosol Particle Sizer (APS, TSI co., USA) at inlet and outlet of the classifier. Partial classification efficiency was obtained at various rotation speeds, total flow rates, and feed rates of powders, and classification characteristic depending on design parameters was discussed. Classification performance was enhanced as rotation speed of rotor increased and total flow rate decreased.

• 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.

• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.950-955
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• 2003

This paper introduces an enhanced condition monitoring and diagnosis system recently developed for rotating machinery. In the system, the data aquisition/monitoring signal processing, machine condition classifier, case-based reasoning and demonstration modules are effectively integrated with user-friendliness so that machine operators can easily monitor and diagnose the status of rotating machinery in operation. Some of the new features include the directional spectrum, case-based reasoning and neural network techniques. And the demonstrator modules for fault diagnosis of a Bear driving system and for basic understanding of the rotor dynamics are provided to help the potential users better understand the system.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 1999년도 유체기계 연구개발 발표회 논문집
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• 한국입자에어로졸학회지
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• 제4권2호
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• pp.37-50
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• 2008

Concerns on centrifugal classifiers, of which have cut sizes are below few micrometers, have been increased and it is prospected to be used in extensive industries, such as manufacturing the fine minerals, cosmetics, advanced electric materials, and life science. This paper reviews the recent progress of research and development on the centrifugal classifiers. General categorization of classifiers for feeds was assessed and separation mechanism of the classifiers was followed. History of centrifugal classifiers was explored and some points to be improved were briefly indicated. Fundamental theory of the classification by centrifugal classifiers was pearly studied, and advanced and further understandings on factors affecting the separation or grading efficiency are described. Factors determining the classification precision and efficiency of centrifugal classifiers, such as geometry, rotational speed and inclined angle of rotating vanes, feed and air flow rates, and rotor dimensions are reviewed.