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

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 …

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 …

With ever-accelerating advancement of information, communication, sensing and
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …

A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …

Latent variable (LV) models have been widely used in multivariate statistical process
monitoring. However, whatever deviation from nominal operating condition is detected, an …

Modern industrial plants generally consist of multiple manufacturing units, and the local
correlation within each unit can be used to effectively alleviate the effect of spurious …

Sensor faults, which occur when sensor outputs display unacceptable deviations from the
true values of measured variable, will cause false alarms and missed detections in structural …

Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …

A novel multivariate statistical process monitoring (MSPM) method based on modified
independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to …