Random-forest-based real-time contrasts control chart using adaptive breakpoints with symbolic aggregate approximation

Abstract

For high yield management, process monitoring has become an increasingly important task. The real-time contrasts (RTC) control chart uses the real-time classification method for process monitoring and outperforms the existing real-time control chart. The original RTC control chart identifies the cause of faults using a random forest classifier. However, the random forest provides discrete monitoring statistics that could make the overall performance less efficient. To improve the performance of the RTC control chart, we propose a random-forest-based RTC control chart that uses adaptive breakpoints with symbolic aggregate approximation (ABP-SAX). The monitoring statistics of the RTC control chart indicate the process condition, and the quality of the monitoring statistics is determined by the classification performance of the classifier. Therefore, to improve the classification performance of individual decision trees, we proposed ABP-SAX. The original SAX causes time-information loss and distortions in the data slope and pattern. We prevent these problems using the mean squared error to minimize the difference between the represented and original data. After the applied ABP-SAX representation, the raw data are represented by categorical values that preserve information from the original data, and the represented data improve the performance of the RTC control chart. Therefore, the proposed RTC control chart could detect shifts more quickly and identify the cause of the faults. Our improvements can contribute to high yield management and quick response to abnormalities.