充气球 发表于 2025-3-25 04:44:13

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Semblance 发表于 2025-3-25 10:41:46

Global Plus Local Projection to Latent Structures,time delay (Ding .; Aumi et al. .; Peng et al. .; Zhang et al. .; Yin et al. .). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.

sleep-spindles 发表于 2025-3-25 14:13:18

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使声音降低 发表于 2025-3-25 19:28:37

Locality-Preserving Partial Least Squares Regression,principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their respective data sets. (2) The correlation between . and . is the largest. Finally, a quality-related monitoring strategy is established based on LPPLS.

AGATE 发表于 2025-3-25 22:21:11

Locally Linear Embedding Orthogonal Projection to Latent Structure,m shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least squares analysis (PLS) (Kruger et al. .; Song et al. .; Li et al. .; Hu et al. .; Zhang et al. .).

的’ 发表于 2025-3-26 01:09:07

New Robust Projection to Latent Structure,ainty part, just as the nonlinearity can be used to represent the uncertainty of a system (Wang et al. .). So this chapter also focuses on how to deal with the nonlinearity in PLS series method, but starts from an different view, i.e., robust PLS. Here the system nonlinearity is considered as uncertainty and a new robust .-PLS is proposed.

arcane 发表于 2025-3-26 06:47:10

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魔鬼在游行 发表于 2025-3-26 09:20:03

A Further Improvement on Approximating TTP-2d of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.

Countermand 发表于 2025-3-26 16:25:39

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迅速成长 发表于 2025-3-26 18:48:35

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查看完整版本: Titlebook: Data-Driven Fault Detection and Reasoning for Industrial Monitoring; Jing Wang,Jinglin Zhou,Xiaolu Chen Book‘‘‘‘‘‘‘‘ 2022 The Editor(s) (i