Abstract: A method for monitoring of and fault detection in an industrial process, comprising at least a first sub-process and at least one second sub-process arranged in a process chain, comprising, for the at least one second sub-process the steps of collecting data and calculating a multivariate sub-model based on the collected data, said method being characterized by the steps of receiving in the first sub-process from the at least second sub-process information related to the multivariate sub-model calculated for the at least second sub-process, collecting data related to the first sub-process, and calculating a multivariate sub-model for the first sub-process based on collected data and received information.

Abstract: The present invention provides a method and system for controlling the quality of a product produced by an injection molding production process. The invention includes performing a multivariate analysis on injection molding process data collected real-time and determining whether the real-time data is within a predetermined production control limit. When the real-time production data exceeds the control limit, the process is considered out of control and product produced during the out of control condition is removed real-time from the injection molding production process.

Abstract: The present invention provides a method and system for controlling the quality of a product produced by an injection molding production process. The invention includes performing a multivariate analysis on injection molding process data collected real-time and determining whether the real-time data is within a predetermined production control limit. When the real-time production data exceeds the control limit, the process is considered out of control and product produced during the out of control condition is removed real-time from the injection molding production process.

Abstract: A method for monitoring of and fault detection in an industrial process, comprising at least a first sub-process and at least one second sub-process arranged in a process chain, comprising, for the at least one second sub-process the steps of collecting data and calculating a multivariate sub-model based on the collected data, said method being characterized by the steps of receiving in the first sub-process from the at least second sub-process information related to the multivariate sub-model calculated for the at least second sub-process, collecting data related to the first sub-process, and calculating a multivariate sub-model for the first sub-process based on collected data and received information.

Abstract: The invention provides a method and an arrangement for filtering or pre-processing most any type of multivariate data exemplified by NIR or NMR spectra measured on samples in order to remove systematic noise such as base-line variation and multiplicative scatter effects. This is accomplished by differentiating the spectra to first or second derivatives, by Multiplicative Signal Correction (MSC), or by similar filtering methods. The pre-processing may, however, also remove information from the spectra, as well as other multiple measurement arrays, regarding (Y) (the response variables). Provided is a variant of PLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given (y) vector or (Y) matrix. Hence, ensuring that the signal correction removes as little information as possible regarding (Y). A filter according to the present invention is named Orthogonal Partial Least Squares (OPLS).

Abstract: NIR spectra are often pre-processed in order to remove systematic noise such as base-line varation and multiplicative scauer effects. This is done by differentiating the spectra to first or second derivatives, by Mtltiplicative Siga Correction (MSC), or by sirnilar filtering methods. This pre-processinp may, however, also remove infosmaion fror The spectra regarding Y (the measured response variable in multivarate calibration applications). Provided is a variant ofPLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given Y-vector or Y-matrix (430). Thus, ensung that the signal correction removes as little information as possible regarding Y. In the case when the number of X-variables, in an X matrix, K exceeds the number of observations N, strict orthogonality is obtined. A filter (470) according to the present invention is named Orthogonal Signal Correction (OSC).

Abstract: The invention regards a method and an arrangement for filtering or pre-processing most any type of multivariate data exemplified by NIR or NMR spectra measured on samples in order to remove systematic noise such as base line variation and multiplicative scatter effects. This is accomplished by differentiating the spectra to first or second derivatives, by Multiplicative Signal Correction (MSC), or by similar filtering methods. The pre-processing may, however, also remove information from the spectra, as well as other multiple measurement arrays, regarding (Y) (the response variables). Provided is a variant of PLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given (y) vector or (Y) matrix. Hence, ensuring that the signal correction removes as little information as possible regarding (Y). A filter according to the present invention is named Orthogonal Partial Least Squares (OPLS).

Abstract: Variables associated with a multivariate process are monitored on a periodic basis. As a result of an observation of the variables, a residual standard deviation between the monitored variable values and a predictive model can be determined. The residual standard deviation is plotted as a series of connected points. Through the use of an adaptive model, the observations of the variables, and in turn, the residual standard deviation and the predictive model are adapted to changes in the multivariate process.