• 대한전자공학회논문지
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• 제17권1호
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• pp.1-9
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• 1980

신호처리와 analysis를 위한 여러 기초적인 기술이 소개되었다. 이들은 먼저 불확정성순리의 개입에 의하여 특히 교환불가능한 operator 들이 작용한 결과의 등호는 tolerance가 있을 수 있음과 random process 처리방법과 manmum entropy estimate적인 ,사고방식을 통하여 재래식 확정론적 사고방식으로부터의 이탈을 길잡았다. 마지막으로 검출, 추정 및 함수추정의 여러 기법과 covariance functron의 posltive semi-definite-ness 그리고 Karhunen-Loeve 전개, 한 화상의 SVD 등이 설명됐다.

• 한국생산제조학회지
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• 제8권6호
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• pp.71-77
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• 1999

Monitoring of Cutting tool wear is a critical issue in automated machining system and has been extensively studied for many years. An austempered ductile iron(ADI) exhibits the excellent mechanical properties and the wear resistance. ADI has generally the poor machinability due to the characteristic. This paper presents the in-process detection of flank wear of cutting tools using the acoustic emission sensor and the digital oscilloscope. The amplitude level of AE signal(AErms) is mainly affected by cutting speed and it is proportional to cutting speed. There have been the relationship of direct proportion between the amplitude level of AE signals and the flank wear width of cutting tool. The flank wear with corresponding to the tool life is successfully detected with the monitor-ing system used in this study.

• 한국기계가공학회지
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• 제11권4호
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• pp.154-159
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• 2012

Condition monitoring(CM) is a method based on Non-destructive test(NDT). Therefore, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days also. Because its sensitivity is higher than normal accelerometers and it can detect low energy vibration signals. A machine tool consist of many parts such as the bearings, gears, process tools, shaft, hydro-system, and so on. Condition of Every part is connected with product quality finally. To increase the quality of products, condition monitoring of the components of machine tool is done completely. Therefore, in this paper, acoustic emission method is used to detect a machine fault seeded in a gearbox. The AE signals is saved, and power spectrums and feature values, peak value, mean value, RMS, skewness, kurtosis and shape factor, were determined through Matlab.

• International Journal of Reliability and Applications
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• 제8권1호
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• pp.53-66
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• 2007

This paper proposes a new non-homogeneous Poisson process software reliability growth model based on the coverage information. The new model incorporates the coverage information in the fault detection process by assuming that only the faults in the covered constructs are detectable. Since the coverage growth behavior depends on the testing strategy, the fault detection process is first modeled for the general testing strategy and then realized for the uniform testing. Finally the model for the uniform testing is empirically evaluated by applying it to real data sets.

• International Journal of Reliability and Applications
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• 제7권2호
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• pp.155-166
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• 2006

Many software reliability growth models (SRGMs) based on nonhomogeneous Poisson process (NHPP) have been developed and applied in practice. NHPP SRGMs are characterized by their mean value functions. Mean value functions are usually derived from differential equations representing the fault detection/removal process during testing. In this paper such differential equations are regarded as frameworks for generating mean value functions. Currently available frameworks are theoretically discussed with respect to capability of representing the fault detection/removal process. Then two general frameworks are proposed.

• 한국정밀공학회지
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• 제12권10호
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• pp.149-156
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• 1995

This paper presents a method of detecting chatter vibration in end-mill process. The detecting system consists of an adaptive signal processing scheme which uses an autore- gressive time-series model and a neural network is proposed and is verified its effectiveness by using acceleration and cutting force signals recorded during slotting in end-mill operations. Expeerimental results indicate that the proposed system provides excellent detection when chatter is occured within the ranges of cutting conditions considered in this study and an effectiveness of the integration of signals is confirmed.

• 대한전자공학회논문지TC
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• 제42권3호
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• pp.15-18
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• 2005

다중 시공간 부호는 여러 개의 시공간 부호로 이루어져 있어서 대역폭 확장없이 디버시티 이득과 부호화 이득 뿐 아니라 높은 전송률을 공급할 수 있다. 본 논문에서는 이 다중 시공간 부호를 복호하기 위해서 백색화 과정(whitening process)을 이용한 계층화 수신기 구조를 제안한다 제안된 수신기는 복호 순서를 결정하는 블록과 계층화 복호를 하는 블록으로 구성되어 있다. 이때 백색화 과정은 계층화 복호과정에서 수신 디버시티 이득을 최대한 이용하기 위해서 이용되었다. 이 수신기는 기존의 MMSE를 이용한 계층화 수신기 구조에 비해 디버시티 이득을 더 얻을 수 있어서 수신 안테나의 개수를 줄일 수 있다는 장점을 가지고 있다. 제안된 방식은 기존의 부호화된 BLAST보다 $10^{-2}$의 FER에서 5dB 이득을 얻는다.

• 한국생산제조학회지
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• 제9권3호
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• pp.150-159
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• 2000

In-process diagnosis of the cutting state is essential for the automation of manufacturing systems. Especially when the cutting process becomes unstable it induces self-exited vibrations a frequent case of poor tool life rough surface finish damage to the workpiece and the machine tool itself and excessive down time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time monitoring and controlling chatter. This paper describes the detection method of chatter vibration using cutting force in turning process. In order to detect a chatter vibra-tion the dynamic fluctuation of radial force is analyzed since this components is sensitive to the chatter. The envelope sig-nal of radial force has been calculated by the use of FIR Hilbert transformer and it was useful to classify the chatter signal from the dynamically unstable circumstances. It was found that the mode and the mode width were closely correlated with the chatter amplitude was well. Finally back propagation(BP) neural network have been applied to the pattern recognition for the classification of chatter signal in various cutting conditions. The validity of this systed was confirmed by the experiments under the various cutting conditions.

• Journal of Welding and Joining
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• 제32권5호
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• pp.50-57
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• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• International Journal of Reliability and Applications
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• 제4권1호
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• pp.1-12
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• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.