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In the present work, a new iterative selection strategy and reconstruction modeling method is proposed for fault diagnosis. The proposed algorithm can extract those informative fault directions which are responsible for the concerned alarming monitoring statistic. First, the fault effects are decomposed in two different monitoring subspaces, principal subspace (PCS) and residual subspace (RS). Then,...
For fault detection and diagnosis, the conventional multivariate statistical process control (MSPC) methods in general quantify the distance between the new sample and the modeling samples. They, however, do not check the changes of data distribution as long as monitoring statistics stay inside normal region enclosed by control limit, which, are not sensitive to incipient changes. In the present work,...
In this article, a model migration strategy based on subspace separation is proposed for process monitoring by taking advantage of common information between an old process and a new process. Firstly, a global basis vector is extracted and deemed to enclose the cross-set similar correlations. Then two different subspaces are separated from each other in the new dataset. The kernel principal component...
In multivariate statistical monitoring, batch process models should well reflect process characteristics in order to achieve satisfactory fault detection results. In manufacturing systems, many batch processes are inherently multiphase. Usually, process features are different from one phase to another, and gradual transitions are often observed between phases. Another important characteristic of batch...
Recently, the use of spectroscopic techniques for online process monitoring has been introduced, which are deemed to be able to provide a rich source of chemical information about operation conditions within a process system. This paper presents an improved statistical analysis and modeling strategy using spectra data for online fault detection and diagnosis of batch processes. The general principle...
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