• Structural Monitoring and Maintenance
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• 제1권4호
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• pp.357-369
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• 2014

In the existing bridge management systems, assessment of the structural behavior is based on the results of visual inspections in which corresponding condition states are assigned to individual elements. In this process, limited attention is given to the correlation between bridge elements from structural perspective. Also, the uncertainty of parameters which affect the structural capacity is ignored. A system reliability-based assessment model is potentially an appropriate replacement for the existing procedures. The aim of this research is to evaluate the system reliability of existing conventional Steel-Reinforced bridge decks over time. The developed method utilizes the reliability theory and evaluates the structural safety for such bridges based on their failure mechanisms. System reliability analysis has been applied to simply-supported concrete bridge superstructures designed according to the Canadian Highway Bridge Design Code (CHBDC-S6) and the deterioration pattern is achieved based on the reliability estimates. Finally, the bridge condition index of an old existing bridge in Montreal has been estimated using the developed deterioration pattern. The results obtained from the developed reliability-based deterioration model and from the evaluation done by bridge engineers have been found to be in accordance.

• 한국구조물진단유지관리공학회 논문집
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• 제24권2호
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• pp.33-40
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• 2020

광섬유를 이용한 구조물 건전도 모니터링은 다양한 연구가 이루어졌다. 광섬유는 다중 및 분포로 변형률을 계측 할 수 있다. 광섬유 센서 중, FBG 센서는 동적 응답 계측과 정밀도가 높은 장점이 있지만, 계측 포인트의 제한이 있다. 분포형 광섬유 센서는 계측 포인트가 1000개가 넘지만, 샘플링 속도가 낮아 동적 계측이 불가능하다. 본 연구에서는 FBG와 브릴루안 상관영역 측정법의 장점만을 이용한 하이브리드 신경망 센서 계측 시스템이 제안하였다. 광섬유 브래그 격자를 포함한 광섬유를 이용하여 정적응답과 동적 응답을 선택적으로 계측 할 수 있는 계측 시스템이다. 제안된 시스템검증을 위하여 실내 실험을 수행하였으며, 기존의 센서와의 비교를 통해 정확도와 재현성을 검증하였다. 제안된 시스템을 활용하여, 동적 응답을 상시 계측하고, 전역적인 구조물의 상태를 평가한다. 이상 상태가 감지 되면, 분포형 계측 시스템을 이용하여 정적 응답을 계측하여, 구조물의 국부적인 상태를 평가한다. 제안된 시스템을 통해 효율적인 구조물 건전도 모니터링에 활용될 수 있을 것으로 판단된다.

• 한국구조물진단유지관리공학회 논문집
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• 제14권6호
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• pp.171-178
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• 2010

가스관, 송유관 등의 배관구조물은 주요자원의 수송을 책임지는 핵심 지하시설물 중 하나이다. 이들은 사고 및 자연적인 노후화로 인해 국부적인 손상이 발생할 위험에 노출 되어있다. 하지만 대부분의 배관구조물은 지하의 좁은 공간에 복잡하게 연결되어있기 때문에 구조물의 건전성을 지속적으로 모니터링 하는데 어려움이 있었다. 이러한 지금까지 관리방식의 한계점을 극복하기 위해 최근 유비쿼터스 센서 네트워크 기반의 온라인 방식의 상시적 구조물 건전성 평가방법에 대한 연구가 활발히 이뤄지고 있다. 본 논문에서는 전기-역학적 임피던스 기반의 실시간 배관 구조물 건전성 평가방법에 대하여 연구하였다. 배관 구조물에 발생하기 쉬운 볼트 풀림과 균열의 두 가지 국부손상을 가정하였고 압전효과를 가진 PZT와 MFC 센서를 이용하여 구조물의 상태에 따른 임피던스를 계측하여 손상탐색 실험을 수행하였다. 하나의 센서로 가진과 센싱을 동시에 수행할 수 있는 저비용 셀프센싱 기법을 사용하였고 배관 상태에 대한 객관적인 판단을 위해 손상지수인 RMSD 값을 사용하여 계측된 신호를 이용하여 손상의 정도를 정량화 시켰다. 손상여부의 판단을 위해 일반 극치 분포를 이용하여 최적화된 통계적인 정상상태의 임계값을 설정하였다. 위와 같은 실험적 연구과정을 통해 제안된 실시간 배관 구조물 건전성 평가 방법의 타당성과 효율성을 확인해 보았다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.742-746
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• 2008

With the trend of factory automation, nowadays, much industrial machinery tends to be put into 24-hours operation a day. However, these trends in industrial equipments also increase the possibility of various mechanical problems and bring about innumerable maintenance cost. There is a strong need of the condition monitoring and diagnosis for industrial equipment, especially rotating machinery, since they are connected not only to the reduction in the maintenance costs but also connected to the enhancement of production efficiency. Generally, to evaluate the operating conditions in the machinery in the industrial field, various physical properties are monitored. Among them, vibration and Noise signals are the mist important indicator and it is effectively used in many diagnosis systems for machinery. Much previous research is based in the FFT (Fast Fourier Transform) method. The spectral analysis is assumed that the signal is stationary. However, almost random signals are non-stationary. The wavelet transform has been recognized an efficient Method. Most interesting sounds have time-varying features. Signal processing techniques for the analysis of transient sound have been not clearly given yet.

• Geomechanics and Engineering
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• 제4권3호
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• pp.219-227
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• 2012

Pavement management systems require systematic monitoring of pavement surfaces to determine preventive and corrective maintenance. The process involves the accumulation of large amounts of visual data, typically obtained from site visitation. The pavement surface condition is then correlated to a pavement distress index that is based on a scoring system previously established by state or federal agencies. The scoring system determines if the pavement section requires maintenance, overlay or reconstruction. One of the surface distresses forming part of the overall pavement distress index is the Alligator Crack Index (AC Index). The AC Index involves the visual evaluation of the crack severity of a section of a pavement as being low, medium, or high. This evaluation is then integrated into a formula in order to obtain the AC Index. In this study a quantification of the visual evaluation of the severity of alligator cracking is carried out using photographs and the fractal dimension concept from fractal theory. Pavements with low levels of cracking were found to have a fractal dimension equal to 1.051. Pavements with moderate levels of cracking had a fractal dimension equal to 1.1754. Pavements with high degrees of cracking had a fractal dimension that varied between 1.5037 (high) and 1.7111 (very high). Pavements with a level of cracking equal to 1.8976 represented pavements that disintegrated and developed potholes. Thus, the visual evaluation of the state of cracking of a pavement (the AC Index) could be enhanced with the use of the fractal dimension concept from fractal theory.

• Nuclear Engineering and Technology
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• 제52권10호
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• 조명전기설비학회논문지
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• 제28권5호
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• pp.60-67
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• 2014

Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method.

• Smart Structures and Systems
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• 제32권5호
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• 한국전자통신학회논문지
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• 제11권7호
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• pp.671-676
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• 2016

산업현장에서의 회전체 기기의 유지보수안전관리를 위한 설비에 대한 설계에 대한 연구이다. 중급 진동 모니터링 시스템 시장에 진입하기 위한 다채널 설비 자가 진단 모니터링 장치를 개발하였고, 이 시스템의 특징은 확장성과 유연성을 부여하였다. 한 개의 신호를 처리하기 위해서는 한 개의 독립적 채널이 필요하나, 개발된 제품은 신호 처리 카드 한 장이 4채널을 처리하다. 한 개 랙에는 독립적인 이중화 전원장치, 디스플레이가 설치되어 진동측정값을 별도의 컴퓨터 없이 현장에서 바로 확인할 수 있다. 베어링 결함 감지는 진동 감시의 기본이지만 일반적인 진동 속도나 가속도에서 나타나지 않는 경우가 있다. 따라서 저주파대역의 반복적이지 않은 진동은 필터링하고 진폭 변조 시키는 기술이 기속도 엔벨로핑 기술이다. 이 기술이 적용된 진동 분석 장비를 개발하였다. 개발된 시제품은 진동오차 범위에서 작동함을 보여주었다.

• 한국정밀공학회지
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• 제29권6호
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• pp.600-606
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• 2012

Upon investigation of the damaged wheels for high speed train it was determined that the damage was caused by rolling contact fatigue during operation of train. The major problems that railway vehicle system using wheel-rail has to face during operation of railway vehicle are rolling contact fatigue, cracks in wheels, cracks in rails and wheel-rail profile wear. If these deficiencies are not controlled at early stages the huge economical problems due to unexpected maintenance cost in railway vehicle can be happened. Also, If the accurate knowledge of contact conditions between wheel and rail can be evaluated, the damage of wheel can be prevented and the maintenance operation can save money. This paper presents the applicability of electro-magnetic technique to the detection and sizing of defects in wheel. Under the condition of continuous rolling contact fatigue the damage of wheel has continuously monitored using the applied sensor. It was shown that the usefulness of the applied sensor was verified by twin disc test and the measured damaged sizes showed good agreement with the damaged sizes estimated by electro-magnetic technique.