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Good performance of a controller in Model Predictive Control (MPC) system keeps the whole industrial process running well. Because of the complexity of the process, data-driven performance assessment approach, instead of model approach, becomes a popular topic. However, performance assessment is inaccurate when operating condition changes, because the performance benchmark should be different. This...
This paper investigates the monitoring of continuous processes using dynamic nonlinear principal component analysis (NPCA). Previously, it was shown that integrating the RBF networks with principal curves significantly had increased the sensitivity of fault detection for nonlinear processes. Despite this, the previous method may not function well for processes which exhibit strong dynamic characteristics...
A new data-driven approach is proposed for the estimation of the Minimum Variance Control (MVC) benchmark, which eliminates the need of estimating the interactor-matrix or extracting the model/Markov parameter matrices. Using the parity space, the proposed subspace approach gives equivalent estimation of the MVC performance bounds in multivariable feedback control system. The basic procedure is to...
A novel data-driven process monitoring method based on dynamic independent component analysis-principle component analysis (DICA-DPCA) is proposed to compensate for shortcomings in the conventional component analysis based monitoring methods. The primary idea is to first augment the measured data matrix to take the process dynamic into account. Then perform independent component analysis (ICA) and...
Since the strip transient temperature can only be measured at a few positions inside the cooling section in hot-rolled strip laminar cooling process, a new approach of monitoring strip transient temperature in cooling process is proposed. A simple and accurate state space representation is designed firstly to describe the temperature drop of strip. An Extended Kalman Filter (EKF) is designed to reconstruct...
In this paper, a novel data-driven approach is presented to monitor processes influenced by gradual small shifts. The primary idea is to first build multivariate exponentially weighted moving average (MEWMA) model based on the originally measured variables to keep the memory effect of the process trend. Then introduce a unified Mahalanobis distance based monitoring statistic, which makes full use...
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