Inferential process modeling, quality prediction and fault detection using multi-stage data segregation

Abstract

A process modeling 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. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., compares on-line process parameter measurements to these means and uses 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 adjusted for each process parameter to tune or adapt the model over time.