• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1403-1412
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• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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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.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.6
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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.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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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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• v.22 no.2
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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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.346-353
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• 2022

Recently, studies on the integrity of rotating machines, such as gantry cranes, which are used in the shipbuilding industry, have been actively conducted. Gantry cranes are driven at relatively low revolutions per minute (RPM), are frequently operated and stopped, and are impacted by external environmental factors, such as shock and noise in the measurement data. The purpose of this study was to construct a replica of a gantry crane hoist used in indoor shipbuilding and analyze the acquired data for errors caused by the shift in operating conditions (RPM) and the change in the position of the data acquisition sensor. Consequently, we observed that the error caused by differences in sensor positions did not occur significantly under low operating conditions but occurred significantly under relatively high operating conditions. Thus, we determined that both the operating condition and position of the acquisition sensor affected the data acquired by the rotary machine.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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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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• v.22 no.2
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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.