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 …

Technological advances led to increasingly larger industrial quality-related datasets calling
for process monitoring methods able to handle them. In such context, the application of …

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

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 …

Multivariate statistical process monitoring involves dimension reduction and latent feature
extraction in large-scale processes and typically incorporates all measured variables …

Modern industrial processes have become increasingly complicated, consequently, the
nonlinearity of data collected from these systems continues to increase. However, the …

In order to deeply exploit intrinsic data feature information hidden among the process data,
an improved kernel principal component analysis (KPCA) method is proposed, which is …

This article proposes a robust end-to-end deep learning-induced fault recognition scheme
by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked …

In order to ensure the long-term stable operation of a large-scale industrial process, it is
necessary to detect and solve the minor abnormal conditions in time. However, the large …

Large-scale plant-wide processes have become more common and monitoring of such
processes is imperative. This work focuses on establishing a distributed monitoring scheme …