2023
DOI: 10.1002/cem.3523
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Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Shi Hu,
Kuan Chang

Abstract: With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the onli… Show more

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