• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.380-387
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• 2010

For the fault diagnosis of three-phase induction motors, we propose a diagnosis algorithm based on mutual information and linear discriminant analysis (LDA). The experimental unit consists of machinery module for induction motor drive and data acquisition module to obtain the fault signal. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by mutual information As the next step, feature extraction is performed by LDA, and then diagnosis is evaluated by k-NN classifier. The results to verify the usability of the proposed algorithm showed better performance than various conventional methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.663-668
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• 2024

This paper applies statistical analysis methods in the time domain to the fault diagnosis of wafer transfer robots, and proposes a methodology to discern the critical characteristics of vibration and torque signals. Subsequently, principal component analysis (PCA) is applied to diminish the data's dimensionality, followed by the development of a fault diagnosis algorithm utilizing Euclidean distance and Hotelling's T-square statistics. The algorithm establishes decision boundaries to categorize failure states based on the observed data. Our findings indicate that data classification incorporating velocity parameters enhances diagnostic accuracy. This approach serves to enhance the precision and efficacy of fault diagnosis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1232-1235
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• 2022

최근 자동차, 철도차량 등 사용자가 있는 기계 시스템에서의 고장 발생 시 사용자의 안전과 관련된 사고로 이어질 수 있어 부품에 대한 모니터링 및 고장 여부 판단은 매우 중요하다. 이러한 부품 중에서 베어링은 회전체와 회전하지 않는 물체 사이에서 회전이 원활하게 이루어질 수 있도록 하는 부품인데, 베어링에 결함이 발생하게 될 경우, 기계 시스템이 정지하거나, 마찰 열에 의해 화재 등의 치명적인 위험이 발생한다. 본 논문에서는 Resnet과 오토인코더를 활용하여 진동 신호 기반의 베어링의 고장을 감지하고 분류할 수 있는 모델을 제안한다. 제안 방법은 raw data를 이미지로 변환하여 입력으로 사용하는데, 이러한 접근을 통해 수집된 데이터의 손실을 최소화하고 데이터가 가지는 정보를 최대한 분석에 활용할 수 있다. 제안 모델의 검증을 위하여 공개된 데이터셋으로 학습/검증 하였고, 제안 방법이 기존 방법과 비교하여 더 높은 F1 Score와 정확도를 보임을 확인하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.504-509
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• 1995

컴퓨터에 의한 생산시스템의 통합체계화와 온-라인화에 따라 자동화된 설비진단 방법이 요구되어지고 있다. 이에 따라 기계설비에 각종 센서를 부착하여 실시간으로 수집된 출력신호를 이용하여 기계설비를 온-라인으로 감시하는 여러가지 기법들이 제시되고 있다. 본 연구에서는 진동센서로부터의 신호를 radial 함수에 근거한 다단계 뉴럴 네트워크(Neural Network)로 모형화하여 기계설비 상태를 감시하는 방법을 제시한다. 또한 다단계 모델링 분석을 통하여 신호를 예측하고 설비고장 원인을 분류하며, 다른 모형과의 비교를 통하여 효율성 평가와 최적 단계수를 결정하였다. 온라인 학습 알고리즘은 recursive least squares와 clustering 방법을 이용한다.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.482-483
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• 2006

배전급 가스절연개폐기(이하 개폐기)에서 발생하는 고장을 예방하기 위해서는 개폐기 내부에서 발생하는 부분방전 신호로부터 방전의 원인을 추정하는 것이 중요하다. 본 논문에서는 개폐기 내부의 결함을 다양하게 모의하여 방전원에 대한 신호 패턴을 분류하고 방전 원인별로 특성을 분석하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.492-499
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• 2022

This study experimentally investigates the effect of dimensionality reduction of vibration signal on fault diagnosis of a marine engine. By using the principal component analysis, a vibration signal having the dimension of 513 is converted into a low-dimensional signal having the dimension of 1 to 15, and the variation in fault diagnosis accuracy according to the dimensionality change is observed. The vibration signal measured from a full-scale marine generator diesel engine is used, and the contribution of the dimension-reduced signal is quantitatively evaluated using two kinds of variable importance analysis algorithms which are the integrated gradients and the feature permutation methods. As a result of experimental data analysis, the accuracy of the fault diagnosis is shown to improve as the number of dimensions used increases, and when the dimension approaches 10, near-perfect fault classification accuracy is achieved. This shows that the dimension of the vibration signal can be considerably reduced without degrading fault diagnosis accuracy. In the variable importance analysis, the dimension-reduced principal components show higher contribution than the conventional statistical features, which supports the effectiveness of the dimension-reduced signals on fault diagnosis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.417-419
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.316-317
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• 2013

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.9-20
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• 1998

In this paper, a new distance relaying scheme is proposed. Artificial neural networks are applied to the distance relaying system composed of pattern recognition based. The proposed distance relaying scheme has two blocks of pattern recognition stages to estimate the fundamental frequency and to classify the fault types. In the first block, a filtering method using neural networks called a neural networks mapping filter(NMF) is presented to efficiently extract the features. And in the sec'ond block, the estimator called neural networks fault pattern estimator(NFPE) is also presented to classify the fault types by the extracted effective features obtained from NMF. Each block of these applied schemes is trained by back-propagation algorithm of multilayer perceptron and show the fast and accurate pattern recognition by ability of multilayer neural networks. The test result of this approach are obtained the good performance from the fault transient wave signals of EMTP(e1ectromagnetic transients program) in the various fault conditions of power systems.