Patent ID: 7062417

Claim:
A computer implemented method of monitoring an industrial process using a partial least squares approach comprising: constructing predictor and response matrices from reference data of the process, the predictor matrix being comprised of signals of the manipulated and measured predictor variables of the process, and the response matrix being comprised of the response variables of the process, decomposing the predictor and response matrices into rank one component matrices, each of said component matrices being comprised of a vector product in which a score vector describes the variation and a loading vector describes the contribution of the score vector to the predictor or response matrix, decomposition being performed by creating a parametric regression matrix based upon iterations of the decomposition of the predictor and response matrices, characterized by creating generalized t-scores which describe any significant variation of the process including variations of the predictor and response variables, and generalized residual scores which represent the prediction error of the partial least squares model and residuals of the predictor matrix, and plotting the generalized t-scores and the generalized residual scores over time to generate a monitoring chart for visual display, wherein the generalized scores are calculated by constructing an augmented matrix, denoted here by Z and of the form Z=[Y{dot over (:)}X], where X is the predictor matrix and Y is the response matrix, and constructing a score matrix T n =T* n −E* n in which T* n and E* n are generally of the form: T* n =[Y{dot over (:)}X][B PLS (n) {dot over (:)}ℑ] 1 R n E* n =[E n {dot over (:)}F n ][B PLS (n) {dot over (:)}ℑ] 1 R n the columns of the matrix T* n providing the generalised t-scores and the columns of the matrix E* n the generalised residual scores, where ℑ denotes an M×M identity matrix, and B PLS (n) is the PLS regression matrix.