In semiconductor manufacturing, especially in electrical test data, but also in other parameters, there are often sets of parameters that are very highly correlated. Even a change in the correlation ...

In this article, we present a method for monitoring multivariate process data based on the Gabriel biplot. In contrast to existing methods that are based on some form of dimension reduction, we use ...

Abstract: Monitoring techniques play an important role in ensuring consistent product quality and safe operation in the process industry. Data-based models such Principal Component Analysis (PCA) are ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Multivariate statistical process monitoring (MSPM) methods have been widely used in ...

CAMO Software has announced the release of a new version of their easy-to-use multivariate process monitoring solution, Unscrambler® X Process Pulse II. The new version provides users with new and ...

Abstract: One goal of integrated vehicle health management (IVHM) for commercial airplane customers is to monitor sensor data to anticipate problems before flight deck effects (FDEs) ground the ...

This article develops a new multivariate statistical process control (SPC) methodology based on adapting the LASSO variable selection method to the SPC problem. The LASSO method has the sparsity ...

Validating drug production processes need not be a headache, according to AI researchers, who say machine learning could be a single answer to biopharma’s multivariate problem. The FDA defines process ...

Near infrared spectroscopy in-line monitoring and modelling of soybean oil methanolysis has been done using multivariate curve resolution alternating least squares (MCR-ALS) with correlation ...