Hydraulic systems are usually applied in large and complex engineering fields. For
hydraulic systems or components in operation, it is difficult to obtain fault data with fault …

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 …

Intelligent fault recognition techniques are essential to ensure the long-term reliability of
manufacturing. Due to the variations in material, equipment and environment, the process …

Fault or anomaly detection is one of the key problems faced by the chemical process
industry for achieving safe and reliable operation. In this study, a novel methodology …

Representative kinetic models to describe monoclonal antibody (mAb) production processes
are needed for effective process design. The development of mechanistic models can be …

A novel methodology for visual process data analytics based on distance matrices is
proposed. A distance matrix is a two-dimensional representation that reflects intrinsic data …

An unsupervised learning method is developed for fault detection and diagnosis with
missing data for chemical processes based on the multivariate extension of joint recurrence …

A novel method of diagnosis and causality analysis of faults in chemical processes is
developed based on the recurrence theory. By applying and adapting the joint recurrence …

Fault detection and diagnosis (FDD) is crucial for ensuring process safety and product
quality in the chemical industry. Despite the large amounts of process data recorded and …

Reactive distillation integrates reaction and distillation to achieve process intensification.
And the increasing process complexity urgently requires fault diagnosis system to ensure its …