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

Process monitoring is important for ensuring operational reliability and preventing
occupational accidents. In recent years, data-driven methods such as machine learning and …

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 …

Process monitoring is crucial to ensure operational reliability and to prevent industrial
accidents. Data-driven methods have become the preferred approach for fault detection and …

This research has focused on the development of knowledge representations and reasoning
strategies nece" sary to identify the root causes of disturbances in chemical and …

Assumption-based approaches have been proposed in recent times for the diagnosis of
process malfunctions. These methods are systematic in the derivation of the knowledge …

A prototype expert system, called MODEX, for locating the cause (s) of a set of abnormalities
in a chemical process id described. We discuss a methodology that aids the developement …

Fault detection and classifications using supervised learning algorithms are widely studied;
however, lesser attention is given to fault detection using unsupervised learning. This work …

An approach to chemical plant fault diagnosis is presented that utilizes patterns of violation
and satisfaction of the quantitative constraints governing the process. Process knowledge …

Abnormal Situation Management (ASM) has received considerable attention from industry
and academia recently. The first step towards better ASM is the timely detection and …