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The characteristics of dynamic, uncertainty and time variant are very common in the industrial processes and should be paid enough attention for process control and monitoring purposes. As a high-order Bayesian network model, autoregressive dynamic latent variable (AR-DLV) is able to effectively extract both auto-correlations and cross-correlations in data for a dynamic process. However, the operating...
In most industrial processes, both autocorrelations and cross correlations in the data need to be extracted for the purpose of process monitoring and diagnosis. However, traditional dynamic modeling methods focus on the dynamic relationship while the cross correlations are at best implicit. In this brief, a new autoregressive dynamic latent variable model is proposed to capture both dynamic and static...
This article proposes a sparse partial least squares (SPLS) for model calibration of dynamic processes. Via capturing the relationship of process inputs and measurements at different sampling instances, partial least squares (PLS) is a typical multivariable statistical process control technique to model dynamic processes. However, due to rare process measurements, large number of process variables...
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