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Intelligent Fault Diagnosis System for Enhancing Reliability of Coil-Spring Manufacturing Process
허준,백준걸,이홍철,Hur Joon,Baek Jun Geol,Lee Hong Chul Korea Safety ManagementScience 2004 안전경영과학회지 Vol.6 No.3
The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.
적은 소모량과 불분명한 소모패턴을 가진 수리부속의 수요예측
박민규,백준걸,Park, Min-Kyu,Baek, Jun-Geol 한국군사과학기술학회 2018 한국군사과학기술학회지 Vol.21 No.4
As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.
랜덤포레스트 기반 다 범주 분류기를 이용한 RTC(Real-time Contrast) 관리도
이준헌(Jun Heon Lee),백준걸(Jun Geol Baek) 대한산업공학회 2018 대한산업공학회지 Vol.44 No.4
Abnormality detection and causal variables isolation are very important in the manufacturing process. However traditional multivariate statistical process control charts should assume the distribution and are challenged by high dimensional and non-linear data. To overcome these limitations, random forest based real-time contrast (RTC) control chart that transform test procedures to sequential classifications was proposed. Although RTC control chart has the advantage to isolate causal variables, monitoring statistics of the RTC control chart is the probability limited between 0.5 and 1; this could deteriorate abnormality detection ability. Features that use the sliding window can also reduce the sensitivity of detecting process changes. Therefore, we propose improved RTC control chart using random forest based multi-class classifier. This improved RTC control chart has the wider range of monitoring statistics and can detect process changes more quickly. In addition, the causal variable can be detected in the same way as the existing RTC control chart.
선형회귀모델의 변수선택을 위한 다중목적 유전 알고리즘과 응용
김동일,박정술,백준걸,김성식,Kim, Dong-Il,Park, Cheong-Sool,Baek, Jun-Geol,Kim, Sung-Shick 한국시뮬레이션학회 2009 한국시뮬레이션학회 논문지 Vol.18 No.4
The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.