In this final part, we discuss fault diagnosis methods that are based on historic process
knowledge. We also compare and evaluate the various methodologies reviewed in this …

Multivariate statistical approaches to fault detection based on historical operating data have
been found to be useful with processes having a large number of measured variables and …

Numerous accidents in chemical processes have caused emergency shutdowns, property
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features
from input data. Nowadays, researchers have intensively investigated deep learning …

Fault diagnosis in industrial processes are challenging tasks that demand effective and
timely decision making procedures under the extreme conditions of noisy measurements …

This paper proposes a hierarchical artificial neural network (HANN) for isolating the faults of
the Tennessee–Eastman process (TEP). The TEP process is the simulation of a chemical …

Fault diagnosis is important to the industrial process. This paper proposes an orthogonal
incremental extreme learning machine based on driving amount (DAOI-ELM) for recognizing …

The objective ofthis study is to describe the application of artificial neural networks to the
problem offault diagnosis in an air-handling unit. Initially, residuals of system variables that …

This paper proposes a hierarchical deep neural network (HDNN) for diagnosing the faults
on the Tennessee-Eastman process (TEP). The TEP process is a benchmark simulation …

This paper presents an intelligent methodology for diagnosing incipient faults in rotating
machinery. In this fault diagnosis system, wavelet transform techniques are used in …