• Journal of the Korean Society for Precision Engineering
• /
• v.28 no.9
• /
• pp.1078-1085
• /
• 2011

Prediction of a minimum crack size for growth, which is defined as a crack size that grows fast enough to keep ahead of its removal by contact wear and periodic grinding, is the most demanding work to prevent rail from fatigue failure and develop cost effective railway maintenance strategy In this study, we investigated the wheel load increment due to a rail defect during a train ran over it, and its effect on the minimum crack size for growth. For this purpose, we developed simulation software based on the Fletcher and Kapoor's "2.5D" model and measured wheel load increment during a train passed over a defect. A maximum contact pressure and contact patch size were calculated by 3D FEM and crack growth analyses were performed by varying two of dominant contact contributors; surface friction coefficient(0.1, 0.2, 0.3 and 0.4) and crack aspect ratio. The minimum crack sizes for growth were calculated from 0.29 to 1.44mm depending on the contact conditions. They were decreasing with increasing surface friction coefficient and decreasing with crack aspect ratio(a/b).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.3
• /
• pp.151-157
• /
• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• Journal of Magnetics
• /
• v.22 no.1
• /
• pp.34-42
• /
• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• KIISE Transactions on Computing Practices
• /
• v.22 no.5
• /
• pp.235-239
• /
• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.

• Journal of Applied Reliability
• /
• v.17 no.4
• /
• pp.280-288
• /
• 2017

Purpose: PBA buried in underwater requires high reliability because of its mission critical characteristic and harsh operational environment during its life cycle. Therefore, various reliability improvement activities are necessary. The defect on PBA manufacturing process have been studied, as a result, many activities and standards have been presented. However, there are less studies regarding failure pattern on physical features based on design. In this paper, we studied a possible failure patten based on physical features that is related with manufacturing process of PBA. And reliability improvement design based on PoF (Physical of Failure) were intruduced in this paper. Methods: A reliability prediction simulation were performed on the components A and B of the H system using Sherlock Software which is a PoF commercial tool from DFR solution. Solder fatigue and PTH fatigue analysis based on thermal cycling profiles and random vibration was analyzed on three earthquake response spectrum. Result: It was validated that life time and reliability improvement design through solder fatigue and PTH fatigue analysis in case of component. For compoenet B, random vibration fatigue was additionally analyzed and validated reliability for earthquakes profile. Conclusion: In design stage prior to manufacturing, PoF can be analyzed, and it is possible to make a reliability improvement/validated design using design data. This study can be applied in every design step and contribute to make more stable development product.