• Plant Journal
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• v.11 no.2
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• pp.39-44
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• 2015

According to a research that is based on a previous study, But in a different way, This study shows fault diagnosis of Induction motor bearing which runs in coal-fired power plant industries on Desulfurization absorber agitator using Spectrum analysis of Stator Current and visual inspection. As a result of harmonic content analysis of stator current spectrum, It was possible to detect ball and outer race fault frequency. The comparison in the context of this experiment proves that the amplitude of faulty frequency is increased in three times at a fault in ball and in outer race. Spectrum analysis of stator current can be used to detect the presence of a fault condition as well as experiment in faulty bearings, besides early fault detection in bearings can prevent unexpected power generation loss and emergency maintenance cost.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.05a
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• pp.413-418
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• 2002

베어링은 산업현장에서 널리 쓰여지는 중요 부품이다. 따라서 이의 결함에 따른 손실을 예방하기 위해서는 이상을 진단하고 검지하는 기법이 요구된다. 따라서 본 연구에서는 저속회전하므로 노이즈가 많이 포함되어 절상상태의 신호검출이 어려운 저속회전베어링의 외륜이상을 웨이브렛의 Denoising 기법을 적용하여 정량적으로 진단하고 패턴인식법 중의 하나인 KDI(Kullback Discrimination Information)를 적용하여 이상상태의 진단/검지능력을 시험해 보았다. 웨이브랫의 Denoising 기법은 노이즈 캔셀링(Noise canceling)이 능력이 뛰어났고, HDI기법은 저속회전베어링의 정상과 이상의 분류에 뛰어난 검지능력이 있음을 알 수 있었다.(중략)

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.749-752
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• 2014

본 논문에서는 저속으로 동작하는 구름 베어링의 다중 결함 조기 검출을 위해 결함 특징 추출, 효과적인 특징 선택, 선택된 특징을 이용한 결함 분류의 세 단계로 구성된 결함 진단 기법을 제안한다. 1단계에서 이산 웨이블릿 변환을 이용하여 미세성분으로부터 통계적 결함 특징을 추출하고, DET(distance evaluation technique)를 이용하여 추출한 결함 특징 가운데 베어링 다중 결함 검출에 효과적인 특징을 선택한다. 마지막으로 선택된 특징을 k-NN(k-Nearest Neighbors) 분류기 입력으로 사용함으로써 결함을 진단한다. 본 논문에서는 제안한 결함 진단 기법의 성능을 분류 정확도 측면에서 평가한 결과 95.14%의 높은 분류 정확도를 보였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.1
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• pp.7-11
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• 2012

For fault detection about the abrasion stage of rotational machineries under the dynamic loading conditions unlike the traditional diagnosis method used in the past decade, the infrared thermographic method with its distinctive advantages in non-contact, non-destructive, and visible aspects is proposed. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiments were conducted as an alternative way to proceeding the traditional fault monitoring on spectrum analyzer. As results, the thermographic experiment was compared with the traditional vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results obtained as NDT, the temperature characteristics and abnormal fault detections of the ball bearing according to the abrasion stage were analyzed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.581-601
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• 1990

A variety of method to diagnose the fault of rotational mechine is suggested, the latest data. This paper uses Bispectrum which is sort of high order spectrum, diagnose the ball bearing of the rotational machine element. Apprehending the physical meaning of Bispectrum, computer simulation is performed. The result from computer simulation and the signal of the faulted ball bearing is studied from all it's aspects. It is found that the Bispectrum is more effective than the conventional Power Spectrum.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.4
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• pp.305-310
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• 2014

In this study, a non-destructive evaluation (NDE) mothod is proposed for ball bearings contaminated with micro foreign substances, which were inserted into a ball bearing to create a defective specimen. The non-contact quantitative infrared thermographic technique was applied for NDE condition monitoring. Passive thermographic experiments were conducted to perform early fault diagnosis, for bearings operated at optimized torque status under a dynamic load condition. The temperature profiles for normal and defective specimens were quantitatively compared, and the thermographic data analyzed. Based on the NDE results, the temperature characteristics and abnormal fault detection of the ball bearing were quantitatively analyzed according to the rise in temperature.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• Journal of KSNVE
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• v.5 no.3
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• pp.353-360
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• 1995

A new automatic diagnostic system for predicting multiple defects in rolling element bearings is developed by taking probbability into account. A database is constructed from the frequency characteristics of tested bearings with various types of defects. The proposed algorithms for the automatic diagnosis of bearing defects are shown to be satisfactory through the experiments. This method can be effectively used for quality control of the rolling bearing in plants.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.355-360
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• 2013

With the modern machinery towards the direction of high-speed development, the thermal issues of mechanical transmission system and its components is increasingly important. Ball bearing is one of the main parts in rotating machinery system, and is a more easily damaged part. In this paper, bearing thermal fault detection is investigated in details Using infrared thermal imaging technology to the operation state of the ball bearing, a preliminary thermal analysis, and the use of numerical simulation technology by finite element method(FEM) under thermal conditions of the bearing temperature field analysis, initially identified through these two technical analysis, bearing a temperature distribution in the normal state and failure state. It also shows the reliability of the infrared thermal imaging technology. with valuable suggestions for the future bearing fault detection.