Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …

The advancement of artificial intelligence algorithms has gained growing interest in
identifying the fault types in rotary machines, which is a high-efficiency but not a human-like …

In this article, a novel time-constrained global and local nonlinear analytic stationary
subspace analysis (Tc-GLNASSA) is proposed to enhance blast furnace ironmaking process …

The real-time recognition of operating conditions is always critical to ensuring the efficient
and stable operation of industrial flotation processes. Although the widespread use of smart …

Small samples are prone to overfitting in the neural network training process. This paper
proposes an optimization approach based on L2 and dropout regularization called a hybrid …

This paper is dedicated to control theoretically explainable application of autoencoders to
optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a …

The data collected by sensors in modern chemical process systems are always
contaminated by industrial noise, so robust fault detection is an important technology to …

Process monitoring is essential for ensuring the safety of geological drilling processes, but
most existing monitoring systems suffer from false alarms. This study is motivated by the fact …

In this study, we propose a Graph neural Differential Auto-encoder (GNDAE) model for fault
detection and process monitoring. The GNDAE framework is capable of dealing with graph …

In real chemical processes, the collected data is often subject to interference from ambient
environmental noise, resulting in a decline in the detection performance. Although denoising …