랜덤포레스트 기반 다 범주 분류기를 이용한 RTC(Real-time Contrast) 관리도

Abstract

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.