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적응형 의사결정 트리와 최단 경로법을 이용한 기계 진단 및 보전 정책 수립
백준걸 한국경영과학회 2002 韓國經營科學會誌 Vol.27 No.2
CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM problem is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Marlov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.
Fault Detection of Cycle-Based Signals Using Wavelet Transform in FAB Processes
Kim, Jun-Seok,Lee, Jae-Hyun,Kim, Ji-Hyun,Baek, Jun-Geol,Kim, Sung-Shick 한국정밀공학회 2010 International Journal of Precision Engineering and Vol.11 No.2
This paper presents a wavelet multiresolution analysis based process fault detection algorithm to improve the accuracy of fault detection. Using Haar wavelet, coefficients that well reflect the process condition are selected and Hotelling's T2 control chart that uses the selected coefficients is constructed for assessing the process condition. To enhance the overall efficiency and accuracy of fault detection, the following two steps are suggested: First, a denoising method that is based on wavelet transform and soft-thresholding. Second, coefficient selection methods that use the difference in the variance. For performance evaluation, various types of abnormal process conditions are simulated and the proposed algorithm is compared with other methodologies. Also, We apply the proposed algorithm to the industrial data of the dry etching process, which is one of the FAB processes. Our method has a better fault-detection performance for various sections and various changes in mean than other methods.
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.