Performance comparison of procedures for monitoring autocorrelated processes based on classification and forecasting
Pyoungjin Ji1 · Jaeheon Lee2
12Department of Applied Statistics, Chung-Ang University
Correspondence to: 1 Graduate student, Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea.
2 Corresponding author: Professor, Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea. E-mail: jaeheon@cau.ac.kr
2 Corresponding author: Professor, Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea. E-mail: jaeheon@cau.ac.kr
Received July 24, 2023; Revised September 15, 2023; Accepted September 18, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- There has been extensive research on the procedures for monitoring autocorrelated processes. Among them, the most commonly used approach is to forecast the next observation based on a fitted model, calculate residuals, and apply control charting procedures to the residual data. In this paper, we propose a process monitoring procedure based on a recurrent neural network (RNN) to classify whether the process is in control or out of control. The performance of this procedure is compared with the forecasting procedure based on a RNN and the traditional residual control charting procedure through simulation study. The results show that the RNN-based classification procedure quickly detects changes in the process level, and the RNN-based forecasting procedure quickly detects changes in the process variance. Additionally, unlike the traditional monitoring procedure, the RNN-based procedures have the advantage that they do not require accurate model fitting for process data.
- Keywords : Autocorrelated process, deep learning, residual chart, RNN
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