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 …

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features
from input data. Nowadays, researchers have intensively investigated deep learning …

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

In the conventional approaches to the design of fault diagnosis systems, little effort is usually
paid to the selection of the parity vectors. As a result, the systems' performance can be …

Fault diagnosis is an important topic both in practice and research. There is intense pressure
on industrial systems to continue reducing unscheduled downtime, performance …

With the growing complexity of the modern industrial process, monitoring large-scale plant-
wide processes has become quite popular. Unlike traditional processes, the measured data …

Decentralized process monitoring based on purely data-based methods has recently gained
considerable attention in multivariate statistical process monitoring circle. Although the …

With the advance of Industry 4.0, new data collectors are appearing at different points of the
process generating blocks of data whose integrity should be preserved during data analysis …

Multivariate statistical process monitoring (MSPM) can conduct dimensionality reduction on
process variables and can obtain low-dimensional representations that capture most of the …

Traditional kernel principal component analysis (KPCA) based nonlinear process monitoring
method may not perform well because its Gaussian distribution assumption is often violated …