Recently, the availability of extensive catalysis-related data generated by experimental data
and theoretical calculations has promoted the development of machine learning (ML) …

Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

Deep-neural network-based fault diagnosis methods have been widely used according to
the state of the art. However, a few of them consider the prior knowledge of the system of …

Modern data-driven fault detection and diagnosis (FDD) techniques show impressive high
diagnostic accuracy in recognizing various air handling units (AHUs) faults. Most existing …

Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …

Deep learning is attracting widespread attention in the field of chemical process fault
diagnosis recently. However, most deep learning methods are based on supervised …

Recently, broad learning system (BLS) has been introduced to solve industrial fault
diagnosis problems and has achieved impressive performance. As a flat network, BLS …

Data-driven process monitoring methods have attracted many attentions and gained wide
applications. However, the real industrial process data are much more complex which is …

Recurrent neural networks (RNNs), particularly those with gated units, such as long short-
term memory (LSTM) and gated recurrent unit (GRU), have demonstrated clear superiority in …

Timely and accurate state detection and fault diagnosis of rolling element bearings are very
critical to ensuring the reliability of rotating machinery. This paper proposes a novel method …