Procedure for monitoring autocorrelated processes using LSTM Autoencoder
Pyoungjin Ji, Jaeheon Lee · Korean Journal of Applied Statistics · 2024
Many studies have been conducted to quickly detect out-of-control situations in autocorrelated processes.The most traditionally used method is a residual control chart, which uses residuals calculated from a fitted time series model.However, many procedures for monitoring autocorrelated processes using statistical learning methods have recently been proposed.In this paper, we propose a monitoring procedure using the latent vector of LSTM Autoencoder, a deep learning-based unsupervised learning method.We compare the performance of this procedure with the LSTM Autoencoder procedure based on the reconstruction error, the RNN classification procedure, and the residual charting procedure through simulation studies.Simulation results show that the performance of the proposed procedure and the RNN classification procedure are similar, but the proposed procedure has the advantage of being useful in processes where sufficient out-of-control data cannot be obtained, because it does not require out-of-control data for training.