• 정보보호학회논문지
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• 제29권6호
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• pp.1403-1412
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• 2019

터빈, 배관 및 저장탱크와 같은 물리장치들의 경우 노후화뿐만 아니라 제어장치에 대한 사이버공격으로 인해 PLC(Programmable Logic Controller)와 같은 제어시스템의 보호 및 상태감시기능이 동작하지 않는 경우, 피해파급력이 크고, 가동 중지 시 그 비용 손실 또한 매우 크다. 가동 중인 물리장치의 작동을 중지하지 않고 간접적으로 상태감시를 함으로써 가용성을 유지하기 위한 방안으로써 온도, 가속도, 전류 등을 간접적으로 감지하고, 데이터들을 Influx DB에 저장하여 실시간으로 감시하는 시스템을 설계 및 구현한다. 실제 구현된 시스템으로부터 데이터를 얻고 이를 이용하여 이상상태를 감지할 수 있음을 검증하였다. 간접적 실시간 감시시스템의 범용화를 통해 데이터를 축적해 활용하면, 추가비용 없이 가동을 중지하지 않고 사용할 수 있을 뿐만 아니라 미리 고장을 예측하고 필요한 경우에만 조치를 취하는 고장예지기술, 이상상태를 이중으로 감시하는 신뢰도 높은 건전성 관리 기술을 통해 유지보수비용과 위험도를 대폭적으로 감소시키고, 보안위협에 대한 대비가 가능하다.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• 한국산업융합학회 논문집
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• 제25권2_1호
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• pp.169-176
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• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2021년도 제64차 하계학술대회논문집 29권2호
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• pp.5-6
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• 2021

본 논문에서는 Semi-supervised Learning 방식의 이상감지 방법을 제안한다. 취득한 소음 데이터를 이미지화 시킨 후 Convolution AutoEncoder 학습 방법을 이용하여 모델을 학습한다. 고장 데이터와 정상 데이터 간의 데이터 불균형 문제가 대두되기 때문에 정상 데이터만을 활용한 이상감지는 실제 산업현장의 상황에 알맞게 사용할 수 있을 것이라 기대한다.

• Smart Structures and Systems
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• 제22권2호
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• pp.185-192
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• 2018

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain ($QA_{HSS}$), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were then applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

• 해양환경안전학회지
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• 제28권2호
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• pp.346-353
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• 2022

최근 회전 회전기계의 건전성 관련 연구가 활발하게 진행중이며, 조선업의 대표적인 회전기계인 갠트리 크레인에도 이를 적용하고자 하는 연구가 활발하게 진행되고 있다. 하지만 조선업의 갠트리 크레인의 경우 상대적으로 낮은 RPM으로 구동되고 잦은 운전과 정지가 이루어지며 충격, 소음 등의 외부환경 인자가 측정 데이터에 영향을 크게 미쳐 오차를 발생시킬 수 있다. 본 연구에서는 조선업의 내업공정에서 사용되는 갠트리 크레인의 Hoist 모사장비를 제작하여, 운전조건(RPM) 변화와 데이터 획득 센서의 위치 차이가 획득 데이터에 미치는 오차를 통계적으로 분석하였다. 연구결과 상대적으로 낮은 운전조건에서는 센서 위치 차이에 따른 획득 데이터의 오차는 크게 발생하지 않았으나, 상대적으로 높은 운전조건에서는 획득 데이터의 오차가 크게 발생하는 것으로 확인하였으며, 회전기계의 데이터 획득 시 운전조건과 획득 센서위치가 획득 데이터에 영향을 미치는 것으로 확인하였다.

• 인터넷정보학회논문지
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• 제25권1호
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• pp.109-121
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• 2024

섬유, 자동차와 같은 여러 제조 공정에서 설비가 고장이 나 멈추게 되면 기계가 작동하지 않게 되고 이는 기업의 시간적, 금전적 손실로 이어진다. 따라서 설비의 고장이 발생하기 전, 고장을 예측하여 정비할 수 있도록 설비의 이상을 사전에 탐지하는 것이 중요하다. 대부분의 설비 고장 원인은 설비의 필수 부품인 베어링의 고장으로, 베어링의 고장을 진단하는 것은 설비예지보전 연구의 핵심이기도 하다. 본 논문에서는 베어링의 진동 신호를 분석하여 SWT-SVD 전처리 알고리즘을 제안하고 이를 시계열 이상탐지 모델 네트워크 중 하나인 어노멀리 트랜스포머에 적용하여 베어링 이상탐지 모델을 구현한다. 제조공정의 베어링 진동신호는 실시간으로 센서값들의 이력이 작성되어 노이즈가 존재하므로, 이를 줄이기 위해 본 연구에서는 정상 웨이블릿 변환(Stationary Wavelet Transform)을 사용하여 주파수 성분을 추출하고, 특이값 분해(Singular Value Decomposition) 알고리즘을 통해 유의미한 특징들을 추출하는 전처리를 진행한다. 제안하는 SWT-SVD 전처리 방법을 적용한 베어링 이상탐지 모델 실험을 위해 IEEE PHM학회에서 제공하는 PHM-2012-Challenge 데이터 세트를 활용하였으며, 실험 결과는 0.98의 정확도와 0.97의 F1-Score로 우수한 성능을 보였다. 추가로, 성능 향상을 입증하기 위해 선행 연구들과 성능 비교를 진행한다. 비교 실험을 통해 제안한 전처리 방법이 기존의 전처리보다 높은 성능을 보임을 확인하였다.

• 한국기계가공학회지
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• 제18권9호
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• pp.29-35
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• 2019

The structure of the machinery industry due to the 4th industrial revolution is changing from precision and durability to intelligent and smart machinery through sensing and interconnection(IoT). There is a growing need for research on prognostics and health management(PHM) that can prevent abnormalities in processing machines and accurately predict and diagnose conditions. PHM is a technology that monitors the condition of a mechanical system, diagnoses signs of failure, and predicts the remaining life of the object. In this study, the vibration generated during machining is measured and a classification algorithm for normal and fault signals is developed. Arbitrary fault signal is collected by changing the conditions of un stable supply cutting oil and fixing jig. The signal processing is performed to apply the measured signal to the learning model. The sampling rate is changed for high speed operation and performed machine learning using raw signal without FFT. The fault classification algorithm for 1D convolution neural network composed of 2 convolution layers is developed.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.