Sentence examples for multivariate statistical monitoring from inspiring English sources
In this respect, it is a generalized concept of multivariate statistical monitoring approaches.
Multivariate statistical monitoring charts are an efficient tool for checking the quality of a process by identifying abnormalities.
Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution.
In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality.
Each of the condition processes is equivalent to a multivariate statistical process monitoring (MSPM).
To solve these problems, a modified ICA algorithm based on particle swarm optimization (PSO) called PSO-ICA is proposed for the purpose of multivariate statistical process monitoring (MSPM).
Inheriting from supervised learning, SMDM has better online fault diagnosis capability of known faults compared to the unsupervised multivariate statistical process monitoring methods.
In this paper, some drawbacks of original kernel independent component analysis (KICA) and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate statistical process monitoring (MSPM).
Russell et al. [17] and Chiang et al. [18] gives a comprehensive and detailed study of multivariate statistical process monitoring using major dimensionality reduction techniques: PCA, FDA Fisherr discriminant analysis), PLS (partial least squares), and CVA (canonical variate analysis).
Multivariate statistical methods for process monitoring are attaining a lot of attention in chemical and process industries to enhance both the process performance and safety.
The proposed design is based on a novel strategy, which integrates multivariate statistical process control (MSPC) monitoring into knowledge-based (KB) fault diagnosis both qualitatively and quantitatively using expert system technology.
