The past decade has witnessed the adoption of artificial intelligence (AI) in various
applications. It is of no exception in the area of prognostics and health management (PHM) …

The main roles of fault detection and diagnosis (FDD) for industrial processes are to make
an effective indicator which can identify faulty status of a process and then to take a proper …

In process industries, accurate prediction of critical quality variables is particularly important
for process control and optimization. Usually, soft sensors have been developed to estimate …

Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

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 …

Early detection of incipient faults in industrial processes is increasingly becoming important,
as these faults can slowly develop into serious abnormal events, an emergency situation, or …

Anomaly detection is a crucial aspect for both safety and efficiency of modern process
industries. This paper proposes a two-steps methodology for anomaly detection in industrial …

Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

In recent decades, machine learning (ML) techniques have been effectively applied for
industrial process monitoring to assure safety and high-quality yield. Traditional process …

In this paper, we consider a machine learning approach merged with statistical testing
hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The …