Most existing industrial process fault detection and diagnosis (FDD) techniques operate on
data collected at a single scale and focus only on known faults. However, actual process …

For nearly a decade, quality-related fault detection algorithms have been widely used in
industrial systems. However, the majority of these detection strategies rely on static …

Independent component analysis (ICA) is a commonly used non-Gaussian process fault
diagnosis method. A fault detection algorithm of kernel dynamic ICA (KDICA) has been …

Over past decades, kernel principal component analysis (KPCA) appeared quite popularly
in data-driven process monitoring area. Enormous work has been done to show its …

Data-driven fault detection and root cause analysis methods become attractive in modern
industrial production that can guarantee the safety and stability of process operation. If …

This work concerns the issue of quality-related fault detection and diagnosis (QrFDD) for
nonlinear process monitoring. A kernel principal component analysis (KPCA)-based …

As the main concerns of abnormal event management in process engineering, fault
detection and diagnosis have attracted more and more attention recently. A new monitoring …

In modern industrial processes, timely detection and diagnosis of process abnormalities are
critical for monitoring process operations. Various fault detection and diagnosis (FDD) …

Fault detection of nonlinear dynamic processes can ensure the safety of industrial
production processes. Industrial process data are mostly autocorrelated along with strong …

Quality-related fault detection is crucial for improving system reliability, reducing production
costs and ensuring product quality, which has been an emerging area of practical interest …