Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …

Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …

The development of the Internet of Things, cloud computing, and artificial intelligence has
given birth to industrial artificial intelligence (IAI) technology, which enables us to obtain fine …

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

Fault diagnosis of chemical process data becomes one of the most important directions in
research and practice. Conventional fault diagnosis and classification methods first extract …

Deep learning has recently emerged as a promising method for nonlinear process
monitoring. However, ensuring that the features from process variables have representative …

Most existing fault diagnosis methods may fail in the following three scenarios:(1) serial
correlations exist in the process data;(2) fault data are much less than normal data; and (3) it …

Due to the complicated production mechanism in multivariate industrial processes, different
dynamic features of variables raise challenges to traditional data-driven process monitoring …

This paper is concerned with data science and analytics as applied to data from dynamic
systems for the purpose of monitoring, prediction, and inference. Collinearity is inevitable in …

Fault detection and diagnosis (FDD) has been an active research field during the past
several decades. Methods based on deep neural networks have made some important …