| 摘要 |
<p>A process modelling technique uses a single statistical model, such as a PLS, PRC, MLR, etc. model, developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. Training data sets of various states of the process are stored and the training data divided into time slices. Mean and/or standard deviation values are determined for both the time slice parameters and variables and the training data. A set of deviations from the mean are determined for the time slice data and the model generated based on the set of deviations. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., preferably compares on-line process parameter measurements to these means and use these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. Because only the means and standard deviations of the process parameters of the process model are updated, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states. Moreover, the sensitivity (robustness) of the process model may be manually or automatically adjust each process parameter to tune or adapt the model over time. An alternative aspect is a method of displaying process alert information using a user interface having multiple screens.</p> |
