• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.627-638
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• 2022

This paper discusses the utilization of fault-tolerant technology in the industry by analyzing the status of drone failures in the unmanned vehicle industry survey conducted in 2020. Based on the survey results of the domestic unmanned vehicle industry, we identify subsystems with high fault rates and high severity when faults occur. In addition, fault simulations of the identified subsystems are conducted to analyze the effect of the fault on the vehicles. After that, the fault diagnosis and fault compensation methods studied so far are reviewed, and research cases of the methods are examined. Moreover, the ways to apply it to actual fault cases in the unmanned vehicle industry are debated. Furthermore, based on the previous discussion, the fault-tolerant system is presented, and the consideration when designing the fault-tolerant system in the industry are studied.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.52-57
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• 2019

The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.3
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• pp.143-147
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• 2005

Monitoring and diagnosis of the operating machines are very important for safety operation and maintenance in the industrial fields. These machines are most rotating machines and the diagnosis of the machines has been researched for long time. We can easily see the faulted signal of the rotating machines from the changes of the signals in frequency. The Linear Predictive Coding(LPC) is introduced for signal analysis in frequency domain. In this paper, we propose fault detection and diagnosis method using the Linear Predictive Coding(LPC) and residual signal energy. We applied our method to the induction motors depending on various status of faulted condition and could obtain good results.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.756-757
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• 2011

보호장치는 전력계통에 고장이 발생할 경우 신속 정확 하게 고장을 검출하여 고장부분을 계통으로부터 분리시킴으로써 각종 설비의 손상을 막고 정전구간을 최소화 하여 계통의 안정화를 향상시키는 장치이다. 현재 한전 및 발전사 뿐만아니라 수자원공사의 수도사업장에서도는 원격감시 제어를 통한 무인화와 함께 디지털보호계전기가 설치 운영되고 있다. 디지털 보호계전기는 기존의 기계식 보호계전기가 수행하는 보호기능보다 우수한 성능이 입증되었으며 최근 반도체 기술의 비약적 발전과 보호 알고리즘의 지속적인 개발로 보호계전기의 디지털화가 가속되고 있고 더욱 개선된 고성능, 다기능의 보호계전기 또는 IED가 산업현장에 적용되는 추세에 있다. 본 논문에서는 과전압 보호계전기와 부족전압 보호계전기의 기능 및 동작특성에 대하여 연구하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.514-516
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• 2003

현재 많은 제조 업체들이 장비를 운영하는 중에 장비의 수명이나 이상으로 인한 고장으로 전체 작업 공정이 중단되어 큰 손실은 입은 많은 사례를 가지고 있다. 본 논문에서는 이러한 피해를 조금이나마 줄여 보고자 장비의 상태를 모니터링 및 분석하여 장비의 교체 시기 및 고장 의심 부분을 사용자에게 미리 알려주는 분석 툴을 설계한다. 실제 장비의 적용 대상은 현대중공업 LNG 선박 제조의 크레인 전동기를 대상으로 하였다. 특히, 크레인에서 가장 중요하다고 할 수 있는 전동기의 진동 데이터를 파형(Wavelet)화 하고, 이것을 FFT(Fast Fourier Transform) 변환하여 이 두 형태를 분석해서 전동기의 이상 징후를 발견하는데 초점을 맞추었다. 향후 이러한 적용 사례를 활용하게 되면, 고가 장비의 갑작스러운 고장으로 인한 제조업체의 손실을 조금이라도 줄일 수 있을 것으로 본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.405-407
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• 2016

최근, ICT/loT 기술과의 융합은 다양한 산업분야에 적용되고 있으며, 안정적인 전력공급 및 지능형전력망 구축에 대해 다양한 연구가 이루어지고 있다. 특히, 수요라인과 직접적으로 연관된 배전계통의 효율적인 운영 및 배전설비의 유지/관리 기술에 대한 연구에 많은 연구를 수행하고 있다. 본 논문에서는 다양한 배전설비에 대한 환경정보를 loT 센서를 통해 수집함으로써 실시간으로 정전상황을 불러올 수 있는 기자재의 고장감지 및 예측을 위한 시스템 모델을 제안한다. 제안하는 시스템 모델은 실시간으로 수집되는 정보들에 대해 시계열 기반의 필터링 및 이상점 판단을 위한 성분 분석을 실시하고, 고장진단 및 예측을 위해 기계학습 기반의 데이터 분석실시하여 기자재들의 고장감지 및 고장 발생 여부를 예측한다.

• Journal of Aerospace System Engineering
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• v.13 no.6
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• pp.17-25
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• 2019

PHM (Prognostics and Health Management) of aircraft engines is applied to predict the remaining useful life before failure or the lifetime limit. There are two methods to establish a predictive model for this: The physics-based method and the data-driven method. The physics-based method is more accurate and requires less data, but its application is limited because there are few models available. In this study, the data-driven method is applied, in which a multi-layer perceptron based neural network algorithms is applied for the life prediction. The neural network is trained using the data sets virtually made by the C-MAPSS code developed by NASA. After training the model, it is applied to the test data sets, in which the confidence interval of the remaining useful life is predicted and validated by the actual value. The performance of proposed method is compared with previous studies, and the favorable accuracy is found.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.83-92
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• 2022

In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/m³), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO₂, CO, and NO₂ in subway stations.

• 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.