Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique
plays an important role. Benefiting from the rapid developments of artificial intelligence …

The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …

Abstract Machine health management has become the focus of equipment monitoring
upgrading with the advance of digital twin (DT). The DT model is able to generate system …

Enabled by the advances in communication networks, computational units, and control
systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart …

In this article, a two-stage deep offline-to-online transfer learning framework (DOTLF) is
proposed for long-distance pipeline leakage detection (PLD). At the offline training stage, a …

Sensor networks are playing an increasingly important role in modern buildings. With the
growing size of building sensor networks and the increasing use of low-cost sensors, the …

Transfer learning has been applied to deal with the insufficient labeled target dataset
problem in data-driven fault diagnosis. However, most existing solutions cannot work well …

Deep-learning (DL)-based remaining useful life (RUL) prognostics have achieved prominent
advancements to maintain the reliability and safety of industrial equipment. The run-to …

Deep-learning-based soft sensors have been extensively developed for predicting key
quality or performance variables in industrial processes. However, most approaches …