Fault detection and isolation are important tasks in statistical process control. A real-time contrasts (RTC) control chart converts the statistical process-monitoring problem to the real-time classification problem, thus outperforming traditional monitoring techniques. An RTC assigns a class to reference data and the other class to a window of real-time contrasts. However, RTC control charts often fail to detect abnormal states when both normal and abnormal data exist together in the window. To enable more rapid detection of an improved RTC control chart, this paper proposes a multivariate process monitoring system with an improved RTC control chart. Although previous RTC control charts proposed by other studies outperform the original RTC chart, it is still difficult to detect an abnormal state when normal and abnormal data exist together. To overcome this problem, this paper proposes an RTC control chart using novelty detection and variable importance with random forests. Novelty detection and variable importance were used so that fault can be detected when the control limit could not be exceeded despite the abnormal state. The proposed method extracts representative data in the sliding window and adds the extracted data to the window to quickly detect the abnormal state. Experiments demonstrate the proposed method to outperform the original RTC chart.
Bibliographical noteFunding Information:
Author Contributions: K.-S.S. proposed the idea and carried out the experiments. I.-s.L. assisted with the numerical modeling and analysis. J.-G.B. validated the proposed method and guided the research. All the numearuicthalomrso hdaevlien rgeaandd ananda alypspisr.oJv.-eGd. Bthf.ienvaall imdaatnedustchreipptr. oposed method and guided the research. All the authors have read and approved the final manuscript. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the FundKinogre: aTnh gisowveorrnkmwenast (sMupSpIToN)r (tReFd-2b0y1t6hRe1NA2aBti4o0n1a3l6R7e8s).e Taisrh cwhoFrko uwnadsa atlisoon soufpKpoorretead( NbtyhReF )BgKr2a1n Pt lfuuspn rdoegdrabmy the (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd. (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd. Conflicts of Interest: The authors declare no conflict of interest. Conflicts of Interest: The authors declare no conflict of interest.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2016R1A2B4013678). This work was also supported by the BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd
© 2019 by the authors.
- Control chart
- Fault detection
- Multivariate exponentially weighted moving average (MEWMA)
- Novelty detection
- Real-time contrasts (RTC)
- Variable importance
ASJC Scopus subject areas
- Materials Science(all)
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes