In process industries, early detection and diagnosis of faults is crucial for timely identification
of process upsets, equipment and/or sensor malfunctions. Machine learning techniques …

The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …

In order to ensure the long-term stable operation of a large-scale industrial process, it is
necessary to detect and solve the minor abnormal conditions in time. However, the large …

Jiaozuo coalfield is the most typical in Northern China type coalfield. When it mined the
Permian coal seams, it was threatened by water inrush from the confined aquifer of thin karst …

Dynamic information is a non-negligible part of time-correlated process data, and its full
utilization can improve the performance of fault detection. Traditional dynamic methods …

Optimal operations of industrial control systems require rigorous monitoring to ensure safety,
increase profitability, and minimize plant maintenance downtime. Thus, controller …

Due to the high available and reliable requirements of petrochemical processes, it is critical
to develop real-time fault detection approaches with high performance. Some machine …

This paper proposes a data‐driven sensor fault detection and isolation approach for the
general class of nonlinear systems. The proposed method uses deep neural network …

This paper proposes a data-driven actuator fault detection and isolation approach for the
general class of nonlinear systems. The proposed method uses a deep neural network …

This work considers industrial process monitoring using a variational autoencoder (VAE). As
a powerful deep generative model, the variational autoencoder and its variants have …