• 한국산학기술학회논문지
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• 제13권9호
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• pp.4186-4192
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• 2012

풍력발전기가 경제적, 환경적 요인에 따라 대형화, 해상화 되고 있어 접근이 어렵고, 부품 및 유지보수 비용이 증가하고 있다. 풍력발전기 상태 모니터링을 통하여 고장 요소를 최소화 하고, 고장 시 2차 사고를 예방하여 운영유지 및 보수비용을 낮추고 신뢰성을 증가시켜야 한다. 본 논문에서는 IEC 61400-25-2에서 표준으로 추진하는 풍력발전기 모니터링에 적합한 센서 중 실제 풍력발전기 상태 모니터링에 필요한 온도, 습도, 전압, 전류, 풍향, 풍속 센서를 ZigBee 무선 통신 소자와 결합하여 무선 센서노드를 구성하고 이를 이용한 간단한 네트워크를 통하여 센서 신호를 전송한다. 각 무선 센서노드에서 전송되는 신호는 라우터를 통하여 중앙 모니터링 터미널에 전송한다. 또한 LabVIEW로 신호를 수집 및 처리하고, TCP/IP 통신 변환을 통해 인터넷 환경이면 언제 어디서든지 사용자 및 관리자가 모니터링 할 수 있도록 한다.

• Smart Structures and Systems
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• 제24권6호
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• pp.723-732
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• 2019

Globally road transport networks are subjected to continuous levels of stress from increasing loading and environmental effects. As the most popular mean of transport in the UK the condition of this civil infrastructure is a key indicator of economic growth and productivity. Structural Health Monitoring (SHM) systems can provide a valuable insight to the true condition of our aging infrastructure. In particular, monitoring of the displacement of a bridge structure under live loading can provide an accurate descriptor of bridge condition. In the past B-WIM systems have been used to collect traffic data and hence provide an indicator of bridge condition, however the use of such systems can be restricted by bridge type, assess issues and cost limitations. This research provides a non-contact low cost AI based solution for vehicle classification and associated bridge displacement using computer vision methods. Convolutional neural networks (CNNs) have been adapted to develop the QUBYOLO vehicle classification method from recorded traffic images. This vehicle classification was then accurately related to the corresponding bridge response obtained under live loading using non-contact methods. The successful identification of multiple vehicle types during field testing has shown that QUBYOLO is suitable for the fine-grained vehicle classification required to identify applied load to a bridge structure. The process of displacement analysis and vehicle classification for the purposes of load identification which was used in this research adds to the body of knowledge on the monitoring of existing bridge structures, particularly long span bridges, and establishes the significant potential of computer vision and Deep Learning to provide dependable results on the real response of our infrastructure to existing and potential increased loading.

• Smart Structures and Systems
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• 제29권1호
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• pp.167-179
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• 2022

This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.

• 한국기계가공학회지
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• 제14권6호
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• International Journal of Automotive Technology
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• 제6권4호
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.

• 한국정밀공학회지
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• 제17권12호
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• pp.54-60
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• 2000

Research about tool condition monitoring has been done until now for product automation and unmaned system. But it is hard to apply it to the industrial field due to its cost and reliability. This paper presents the new method of tool wear measurement using Marpos gauge. This is a kind of touch sensor, so its cost is lower than vision system. And it is not affected by dust and illumination, which are important in vision system. This proposed method use tool clearance angle to measure flank wear. Experimental results compared with vision system shows that this method is available for tool condition monitoring system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.319-323
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• 2007

Acoustic emission (AE) was originally developed for non-destructive testing of static structure, but over the year its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison with monitoring bearing. This study was diagnosed low-speed machine which had a fault bearing for early detection by AE. And the artificial faults in a experimentation bearing was made for the bearing signals from difference speed and load were compared and analyzed by AE.

• 한국기계가공학회지
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• 제2권3호
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• pp.34-40
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• 2003

This paper describes evaluation and monitoring methods of machining characteristics for developed micro grooving machine. Experiments were conducted under various process conditions such as spindle revolution speed, feed rates and depth of groove V and U shape of blade and STD11 were used in this experiment. The status of grooving was evaluated through analysis of the Acoustic emission (AE) signal resulted in each process condition. Based on the analysis, this paper examined the possibility of monitoring adapting fuzzy logic. In conclusion, this paper presented the possibility of monitoring in process adapting AE technology and appropriate micro grooving condition.

• 대한임베디드공학회논문지
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• 제3권3호
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• pp.136-142
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• 2008

Mobile robots can offer services like intelligent monitoring in an indoor environment using network connection with remote users. In this paper, we designed and developed a networked security robot system with various sensors, such as flame detector, gas detector, sound monitoring module, and temperature sensor, etc. The robot can be accessed through a web service and the user can check the status of the environment. Using ADAMS software, we defined the motor specification for a worst-case condition of climbing over a obstacle. We applied the robot system in monitoring office condition.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1108-1113
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• 2000

A condition monitoring methodology for rotating machinery is proposed based on multivariate statistical analysis. The CMS usually are using the vibration signal amplitude such as acceleration RMS, peak and velocity RMS to detect machine faults but the information is not so enough that CMS cannot perform reliable monitoring. So new parameters are added such as shape factor, crest factor, kurtosis and skewness as time domain parameters and spectrum amplitude of rotating frequency, $2^{nd}$ harmonics and gear mesh frequency etc. as frequency domain parameters. Many parameters are combined to represent the machine state using the Hotelling's $T^2$ statistics. The proposed methodology is tested in laboratory and the on-line experiment has shown that the proposed methodology offers a reliable monitoring for rotating machinery.