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 …

Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

Numerous accidents in chemical processes have caused emergency shutdowns, property
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …

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 …

For complex industrial processes with multiple operating conditions, the traditional
multivariate process monitoring techniques such as principal component analysis (PCA) and …

Industrial needs are evolving fast towards more flexible manufacture schemes. As a
consequence, it is often required to adapt the plant production to the demand, which can be …

Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …

This paper investigates the performance of several Machine Learning (ML) techniques for
online defect detection in the Laser Powder Bed Fusion (L-PBF) process. The research aims …