LSTM autoencoder의 잠재 벡터를 사용한 자기상관 공정 모니터링

초록

This paper proposes a novel process monitoring procedure using latent vectors from a long short-term memory (LSTM) autoencoder to effectively detect anomalies in autocorrelated processes. While the traditional reconstruction error-based monitoring procedure is effective for detecting abnormal states in process data, it has limitations in capturing subtle or gradual variations. To address this issue, we introduce a latent vector-based procedure and analyze how the dimensionality of latent vectors affects detection performance using AR(1), AR(2), and ARMA(1,1) models. The two procedures are evaluated using average run length (ARL) as a performance metric. The results indicate that the latent vector-based procedure is more effective in detecting small shifts, such as process level changes, with lower-dimensional latent vectors enabling faster detection. In contrast, the reconstruction error-based procedure performs better in identifying large shifts, such as process variance changes, with high-dimensional latent vectors capturing complex patterns more precisely. These findings suggest that selecting an appropriate monitoring procedure based on the type of process anomaly is crucial, with the latent vector-based procedures being more suitable for detecting subtle changes and the reconstruction error-based procedures being more effective for identifying large variations.

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