This paper proposes an interpretable knowledge discovery approach to detect and
diagnose faults in chemical processes. The approach is demonstrated using simulated data …

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

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 …

This paper focuses on the Tennessee Eastman (TE) process and for the first time
investigates it in a cognitive way. The cognitive fault diagnosis does not assume prior …

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 …

Fault diagnosis in the area of process operations is critical for modern production and is
receiving increasing theoretical and practical attention. In spite of many research and …

The increasing complexity of process plants often requires more intelligent support to
diagnose process abnormalities and perform remedial control actions. Essentially two …

This open access book assesses the potential of data-driven methods in industrial process
monitoring engineering. The process modeling, fault detection, classification, isolation, and …

Accurate detection and diagnostics of faults in complex industrial plants are important for
preventing unplanned downtime, optimizing operations and maintenance decisions …

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