• Journal of Advanced Marine Engineering and Technology
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• 제23권1호
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• pp.79-87
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• 1999

The most part of the faculty drop a trouble and damage of machine equipment even if whatever cause they break out take place at local and trifling place and the factor dominating their trouble is due to wear debris occurred in the lubricated machine moving surface. This study has been car-ried out to identify morphology of wear debris on the lubricated machine moving system by means of computer image analysis. Namely the wear debris contained in lubricating oil extracted from movable machine equipment will be filtered through membrane filter(void diameter 0.45${\mu}m$) and will be analyzed with its data information such as 50% volume diameter aspect roundness and reflectivity. Morphological characteristic of wear debris is easily distinguished by four shape parameters it is necessary to divide small class of every 100 wear debris in total wear particles in order to distinguish morphological characteristic of wear debris more easily by computer image analysis. We are sure that operation condition diagnosis of the lubricated machine moving surfaces is possible by computer image analysis.

• International Journal of Safety
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• 제4권2호
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• pp.6-11
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• 2005

The goals of the paper are to understand the impact damage behavior and identify the effect of surface protective materials on impact resistance in filament wound composite pressure vessels. For these, a series of low velocity impact tests was performed on specimens cutting from the full scale pressure vessel by the instrumented impact testing machine. The specimens are classified into two types, which are with and without surface protective material. The visualization for impact damage by two different impactors is made by metallurgical microscope. Based on the impact force history and damage, the impact resistance parameters were employed,rod its validity in identifying the damage resistance of filament wound composite pressure vessel was reviewed. As the results, the impact resistance of the filament wound composites and its dependency on the surface protective material were evaluated quantitatively

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• 한국산학기술학회논문지
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• 제15권8호
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• pp.4790-4795
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• 2014

공작기계는 기계산업의 생산 및 시제품가공 등에 널리 적용되고 있다. 폭발시 발생하는 지반진동은 인근 구조물의 손상이나 시설에 많은 영향을 미친다. 본 논문은 발파진동이 공작기계의 가공정밀도에 미치는 영향을 고찰하였다. 발파진동과 발파시 공작기계의 진동을 측정하였고, 진동허용치를 기준으로 평가하였다. 공작기계의 진동허용치를 기준으로 할 경우 본 연구에 사용된 공작기계의 발파시 진동허용치는 SLIGHTLY ROUGH~ROUGH에 해당된다. 발파진동이 반복될 경우 정밀도가 저하될 가능성이 많다.

• 제어로봇시스템학회논문지
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• 제10권3호
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• pp.249-254
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• 2004

The radiation dosage of nuclear fuel handling machine of PHWR type NPP during normal operation is measured using semiconductor detectors. In order to predict and mitigate the damage of main components in fuel handling machine, caused by high irradiation, the radiation dosage exerted to the components by neutron and gamma ray is measured independently during one time of the fuel exchange, which is used far estimating the radiation dosage for one year. This result can guarantee the safety and economical efficiency for determining the replacement time of the high cost main components in fuel handling machine.

• 한국디지털건축인테리어학회논문집
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• 제4권1호
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• pp.46-53
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• 2004

The interest about noise and vibration is increasing by a living level elevation recently. Particularly, the interest about the influence that vibration of machine has on the safety of building is rising. However, there are a lot of the cases that the influence of machine isn't considered in designing the factory buildings. The purpose of this study is to suggest the results of measuring that the influence of machine's vibration in A factory has on the building structure.

• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• 한국공작기계학회논문집
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• 제18권2호
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• pp.201-206
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• 2009

This study investigates fatigue life and possibility damaged at disk brake of automobile by the simulation of fatigue analysis. Among nonconstant fatigue loads, the case of 'SAE Bracket History' which is the severest at the variation of load tends to be most unstable. The case of 'Sample History' which becomes slower at the variation of load tends to be most stable. The value of maximum relative damage in case of 'SAE Bracket History' is occurred near the average stress '0' and this case can be shown to have the possibility to affect more damage than another case. As the result of this study is applied to automobile parts with non constant loads, durability can be improved during drive by preventing any damage.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.