This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

원자력 발전소 증기발생기 2차측의 전열관에서 생길 수 있는 슬러지나 침전물의 양을 정량적으로 분석하고 측정하기 위해 레이저 거리 측정기(laser range finder)를 이용하여 피사체의 기하학적 정보를 얻을수 있는 원격 검사 장치를 개발하였다. 이 장치는 거리 측정부와 영상 획득부로 이루어져 있다. 거리 측정부의 광원으로는 654nm 반도체 레이저를 사용하였고, 광 검출 은 3.6×3.6mm 크기의 silicon pin diode를 사용하였다. 영상 획득부의 CCD 카메라로는 29만 화소의 고성능 카메라를사용하였다. 본 연구에서는 피사체의 크기 측정 및 피사체에서 반사된 반사광량의 측정을 통해 거리 측정부의 성능을 알아보고, 영상 획득부에 각도 변화를 주어 명확한 영상 획득의 가능성에 대해 실험을 하였다. 실험 결과 얻어진 피사체의 크기는 실제 값과 약 2.8% 이내의 범위에서 일치하였다.

In this study, the integrated management code of damage information in bridges using the data from unmanned inspection equipment (i.e., drone with hybrid imaging equipment) was proposed. It was found that the integrated management code could be used in developing bridge management system based on 3-dimensional information model of bridge.

In this study, the integrated management code of damage information in bridges using the data from unmanned inspection equipment (i.e., drone with hybrid imaging equipment) was proposed. It was found that the integrated management code could be used in developing bridge management system based on 3-dimensional information model of bridge.

This study carried out an investigation about damage characteristic of substructure such as pier and abutment of highway bridge. The applied methodology was the average damage analysis according to service life. By using above methodology, damage characteristic of substructure penetrated water included de-icing salt was investigated. It was found that the area where was under heavy snow exhibited higher amount of average damage than that of the area where was under much less snow.

A filming technique using Side Scan SONAR should be varied upon condition in order to obtain high-resolution data. Because the theoretical measurement scope is different from the actual measurement scope. It is possible to obtain the accurate data only after adjusting the water depth, distance from a structure and emission angle. Via the multiple regression analysis of data accumulated through field experiments and relations of 3 variables, and equation was devised.

In this study database on the results of in-depth inspection was built and statistical analysis was performed on the major faults for concrete dams. using condition evaluation results for existing concrete dams.

과거에는 생애주기에 기반 유지관리 계획에 대한 인식이 부족하였기 때문에 검측자료의 축적은 이루어졌으나 이러한 검측 자료를 이용한 구성품의 수명예측 및 보수보강 시나리오 선정 등 유지관리 의사결정 지원을 위해 사용되지는 못하였다. 따라서 축적된 검측 데이터로부터 궤도 구성품의 건전도를 평가할 수 있는 방법을 정립하고 잔존수명을 예측하여 효율적 유지관리를 실현할 수 있는 기법 개발의 필요성이 대두되고 있다. 이에 본 연구에서는 검측된 레일 마모데이터를 이용한 불확실성 기반 궤도성능 예측모델 개봘과 관련한 연구를 수행하였다.