Experts in industrial processes rely on domain knowledge (DK) repositories to identify the
causes of abnormal situations in order to make appropriate decisions that mitigate the …

Fault diagnosis is integral to maintenance practices, ensuring optimal machinery
functionality. While traditional methods relied on human expertise, intelligent fault diagnosis …

This paper applies the Logical Analysis of Data (LAD) to detect and diagnose faults in
industrial chemical processes. This machine learning classification technique discovers …

Faults occurring in the production line can cause many losses. Predicting the fault events
before they occur or identifying the causes can effectively reduce such losses. A modern …

One of the important problems in process operations management is how to deal effectively
with a multitude of process data. Often, this information is not presented in a manner that …

This article proposes a multiagent-based methodology for the real-time fault diagnosis in
industrial processes. This articles aims to build a decision support tool that helps process …

In large-scale industrial processes, fault diagnosis is of paramount importance, as faults
jeopardize the stability and performance of processes. However, effective fault diagnosis …

The determination of abnormal behavior at process industries gains increasing interest as
strict regulations and highly competitive operation conditions are regularly applied at the …

Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial
role in maintaining a productive and reliable chemical process system. Developments in AI …

This paper proposes an interpretable knowledge discovery approach to detect and
diagnose faults in chemical processes. The approach is demonstrated using simulated data …