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 …

The anomaly detection and health monitoring of dams have attracted increasing attention.
To detect the temporal and spatial anomalies of the dam, a novel spatial–temporal …

This paper is concerned with the issue of the diagnostics of process faults and the detection
of cyber-attacks in industrial control systems. This problem is of significant importance to …

The anomaly detection and safety assessment of dams have attracted increasing attentions.
To assess the safety of dams, a novel dam safety assessment model is proposed. The safety …

Practical industrial processes usually operate under multiple conditions to meet the
requirements of manufacturing strategies. A general choice is to partition data according to …

The Kalman filter performs well in system state estimation by inferring a joint probability
distribution over time variables, which has numerous technological applications in time …

Traditional data-driven methods generally suppose the training dataset is not corrupted by
outliers. However, outliers are inevitable in the real industrial processes even with a …

This article deals with the cyber security of industrial control systems. Methods for detecting
and isolating process faults and cyber-attacks, consisting of elementary actions named …

In intelligent transportation systems (ITS), machine learning is highly effective in anomaly
detection for state estimation (SE) in traffic flow measurement, but it requires resource …