Anomaly detection is a crucial aspect for both safety and efficiency of modern process
industries. This paper proposes a two-steps methodology for anomaly detection in industrial …

Satellites are known as a remotely operated systems with high degree of complexity due to
large number of interconnected devices onboard the satellite. Consequently, it has …

In recent years, deep learning has shown outstanding performance and potential in pattern
recognition and feature extraction, which has attracted an increasing amount of attention …

This article proposes a multiagent-based methodology for the real-time fault diagnosis in
industrial processes. This articles aims to build a decision support tool that helps process …

Given that the main task of process monitoring (ie, fault detection) is actually a classical one-
class classification problem, the generalized regression neural network (GRNN) is directly …

The exergy-based fault detection method has not yet been applied to a complex industrial
system that adequately represents a dynamically changing process. One such system, the …

The thesis develops a new methodology for diagnosis and prognosis of faults in a complex
system, called Interpretable logic tree analysis (ILTA), which combines knowledge extraction …

It is difficult to deal with industrial hybrid systems involving both continuous and discrete
variables using conventional data-driven fault detection methods. While logical analysis of …

The current market is dynamic and, consequently, industries need to be able to meet
unpredictable market changes in order to remain competitive. To address the change in …

Today, industry is in continuous development, with digitalisation occurring at an increasing
rate. Industry 4.0 and Internet of Things are two common expression which both concern the …