Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …

Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …

Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

In recent years, multi-modal measurements of process and product properties have become
widely popular. Sometimes classical chemometric methods such as principal component …

This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …

Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …

Multivariate statistical process monitoring involves dimension reduction and latent feature
extraction in large-scale processes and typically incorporates all measured variables …

In order to deal with the modeling and monitoring issue of large-scale industrial processes
with big data, a distributed and parallel designed principal component analysis approach is …

This paper provides an overview and analysis of statistical process monitoring methods for
fault detection, identification and reconstruction. Several fault detection indices in the …

This paper presents a new method to perform fault diagnosis for data-correlation based
process monitoring. As an alternative to the traditional contribution plot method, a …