Abstract A novel Dynamic Kernel PCA (DKPCA) method is developed for process monitoring
in nonlinear dynamical systems. Classical DKPCA approaches still exhibit vague linearity …

This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected
PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults …

Optimal operations of industrial control systems require rigorous monitoring to ensure safety,
increase profitability, and minimize plant maintenance downtime. Thus, controller …

Data-driven fault detection has made significant advancements. However, detecting
incipient faults is still a challenging problem for traditional data-driven methods, because it …

A common method based on variational modal decomposition (VMD) and an integrated
depth model is proposed to address the problem that it is difficult to precisely anticipate the …

In modern industrial processes, multimodality is a common characteristic and process
monitoring tools should be capable of detecting the occurrence of abnormalities in the …

How to detect incipient faults has been an important problem in the field of fault detection.
Although many types of machine and deep learning methods have been proposed, their …

The multivariate statistic method has been widely applied, but no clear mapping relationship
exists between the latent variables and the fault information, which leads to various …

Anomaly detection is critical to process modeling, monitoring, and control since successful
execution of these engineering tasks depends on access to validated data. The industrial …

In the recent years, deep learning has been widely used in process monitoring due to its
strong ability to extract features. However, with the increasing layers of the deep network, the …