Mechanical fault diagnosis is an important method to accurately identify the health condition
of mechanical equipment and ensure its safe operation. With the advent of the era of" big …

This survey paper provides a review and perspective on intermediate and advanced
reinforcement learning (RL) techniques in process industries. It offers a holistic approach by …

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a
new unseen fault class appears in the test set, but there is no training sample of this fault in …

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for
assessing the effectiveness of fault detection and false alarm tracking methods in intricate …

Over the past few years, massive penetrations targeting an Industrial Control System (ICS)
network intend to compromise its core industrial processes. So far, numerous advanced …

Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims
to remove redundant and uncorrelated features, thus improving the accuracy of models …

This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

Inefficiencies within compressed air systems (CASs) call for the integration of Industry 4.0
technologies for financially viable and sustainable operations. A systematic literature review …

Technological advancements are leading to a world where digital twins will become integral
to manufacturing operations management. While wide-ranging applications of digital twins …

Self-supervised learning has demonstrated state-of-the-art performance on various anomaly
detection tasks. Learning effective representations by solving a supervised pretext task with …