• Smart Structures and Systems
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• 제21권5호
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 춘계학술대회 논문집
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• pp.107-112
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• 1997

In this study of made Autmation system is moving linear transfer system for mainly forming of small electronic unit and other at press line. This system for loading and unloading a workpiece has been installed in a press in order to load and unload a workpiece from a press die. Main Control method be used PLC. It took data of input from each sensor and send signal of output to actuator by sequence program also, we try to Characteristics test of this system has good condition when operating with raser measurmant.

• 대한기계학회논문집A
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• 제33권10호
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• pp.1139-1143
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• 2009

Pneumatic cylinder is widely used as key component of various industry fields just like automation production line. Recently, people begin to pay attention to reduce development period and cost of pneumatic cylinder so research requirements of accelerated life test of pneumatic cylinder have been increased more than ever. In this research, we shall evaluate availability of acceleration model by statistical analysis of acceleration model's predicted value and life data which acquired in a real operation condition after finish accelerated life test of pneumatic cylinder. Also to predict the life of pneumatic cylinder in the operation condition we shall develop new acceleration model equations.

• 정보처리학회논문지C
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• 제15C권3호
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• pp.199-204
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• 2008

CAN은 처음에는 차량 네트워크에서의 사용을 위해 고안되었으나 잡음에 강하고 신뢰성이 높아 공장자동화 등에서도 많이 사용되고 있다. 그러나 1:1통신에서 네트워크 기반의 통신방식으로 변화되면서 각 장치의 기계적인 동작뿐만 아니라 전자, 소프트웨어적인 동작에 대해서도 철저한 검증이 필요하게 되었다. 본 논문은 CAN 기반 시스템에서 통신의 신뢰성을 데이터의 정확성에 대한 부분과 시간상의 정확성에 대한 부분을 검증하는 방법을 제시하고 있다.

• 한국철도학회논문집
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• 제9권4호
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• pp.419-424
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• 2006

In this paper we study on a method to predict and to demonstrate the reliability of the Korea high speed train control system in quantitative point of view. For the prediction of the reliability in train control system which is composed of electronic parts, Relax Software 7.7 automation tool is employed and MIL-HDBK-217 Handbook that is a standard for the prediction of the failure rate in electronic components is used. Mean Time Between Failure (MTBF) is predicted based on the failure rate of the subsystems, State Modeling and Markov Modeling method is used to express a reliability function of the train control system composed by hardware redundancy as a function of time. We propose a Reliability Test which is performed on the level of the subsystems and Failure Report, Analysing, Correction action system which use the test operation data to prove the predicted reliability.

• 대한산업공학회지
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• 제21권2호
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• pp.183-194
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• 1995

This research paper describes several possible suggestions which are essential to develop for Built-In-Test(BIT) related systems, such as more precise BIT parameter analysis, sensitivity analysis of the impact of BIT on redundant systems, statistical inference of field data for BIT performance parameters, methods of reducing BIT false alarms, BIT application in industrial automation and remote control, prevent the system from the impact of BIT failure, undetections and false alarms, life cycle cost analysis for BIT. And, it is mainly focused on redundancy technique for solving BIT diagnostic problems. Algorithms for redundant systems : overlapping technique, flexible redundant BITs are presented and case study will be shown based on various experiment.

• 한국멀티미디어학회논문지
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• 제24권5호
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• pp.695-700
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• 2021

The Faster RCNN-based shellfish recognition algorithm is introduced for shellfish recognition studies that currently do not have any deep learning-based algorithms in a practical setting. The original feature extraction module is replaced by DenseNet, which fuses multi-level feature data and optimises the NMS algorithm, network depth and merging method; overcoming the omission of shellfish overlap, multiple shellfish and insufficient light, effectively solving the problem of low shellfish classification accuracy. In the complexifier test environment, the test accuracy was improved by nearly 4%. Higher testing accuracy was achieved compared to the original testing algorithm. This provides favourable technical support for future applications of the improved Faster RCNN approach to seafood quality classification.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.953-955
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• 1993

In this paper we propose a new stability theorem and a robust stability condition for linguistic fuzzy model systems in state space. First we define a stability in linear sense. After representing the fuzzy model by a system with disturbances, A necessary and sufficient condition for the stability is derived. This condition is proved to be a sufficient condition of the fuzzy model. The Q in the Lyapunov equation is iteratively adjusted by an gradient-based algorithm to improve its stability test. Finally, stability robustness bounds of a system having modeling error is derived. An example is also included to show that the stability test is powerful.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.92-95
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• 2019

Automation control and the data for control of industrial equipment for the diagnosis and prediction is a key to success in the 4th industrial revolution. It increases process efficiency and productivity through data collection, realtime monitoring, and the data analysis. However, university and research environment are still suffering from logging the data in manual way, and we occasionally loss the equipment data logging due to the lack of automatic data logging system. State variable presents the current condition of the equipment operation which is closely related to process result, and it is valuable to monitor and analyze the data for the equipment health monitoring. In this paper, we demonstrate the collection of equipment state variable data via programmable logic controller (PLC) and the visualization of the collected data over the Web access supervisory control and data acquisition (SCADA). Test vehicle for the implementation of the suggested SCADA system is a relay switched physical vapor deposition system in the university environment.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.