• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.191-191
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• 2004

베어링은 회전기계에서 가장 일반적인 구성요소로 베어링의 초기 결함 또는 퇴화현상이 사전에 발견되지 않으면 회전기계의 고장 또는 파손으로 엄청난 손실이 초래될 수 있다. 베어링의 초기 결함을 검출하기 위한 가장 보편적인 방법으로 베어링 진동신호의 특징적인 패턴을 검출하는 것이다.(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.35 no.3
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• pp.185-192
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• 2015

Since real-time monitoring systems, such as early fault detection, have been very important, an infrared thermography technique was proposed as a new diagnosis method. This study focused on damage detection and temperature characteristic analysis of ball bearings using the non-destructive, infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with finite element analysis (FEA) results from ANSYS. In this investigation, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally, it was confirmed that the infrared thermography technique was useful for the real-time detection of damage to bearings.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.330-335
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• 2013

Since real-time monitoring system like a fault early detection has been very important, infrared thermography technique as a new diagnosis method was proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with the frequency data of the existing. As results, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally it was confirmed that the infrared technique was useful for real-time detection of the bearing damages.

• 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)

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• 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.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• 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.