Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Pearson's correlation measure is only able to model linear dependence between random
variables. Hence, conventional principal component analysis (PCA) based on Pearson's …

After introducing process data analytics using latent variable methods and machine
learning, this paper briefly review the essence and objectives of latent variable methods to …

Dimensionality reduction is important for the high-dimensional nature of data in the process
industry, which has made latent variable modeling methods popular in recent years. By …

A novel data‐driven adaptive robust optimization framework that leverages big data in
process industries is proposed. A Bayesian nonparametric model—the Dirichlet process …

Robust multi-scale principal component analysis (RMSPCA) improves multi-scale principal
components analysis (MSPCA) techniques by incorporating the uncertainty of signal noise …

To implement on-line process monitoring techniques such as principal component analysis
(PCA) or partial least squares (PLS), it is necessary to extract data associated with the …

Datasets with missing values arising from causes such as sensor failure, inconsistent
sampling rates, and merging data from different systems are common in the process …

Gaussian mixture model (GMM) has been widely used for soft sensor modeling of multimode
industrial processes. However, it has been recognized that the performance of GMM …

Prediction of anomalous behavior in industrial assets based on sensor reading represents a
key focus in modern business practice. As a matter of fact, forecast of forthcoming faults is …