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 …

Wind turbines play an increasingly important role in renewable power generation. To ensure
the efficient production and financial viability of wind power, it is crucial to maintain wind …

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 …

Time-series data is widely adopted in condition monitoring and fault diagnosis of wind
turbines as well as other energy systems, where long-term dependency is essential to form …

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in
fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type …

High-speed trains have become one of the most important and advanced branches of
intelligent transportation, of which the reliability and safety are still not mature enough for …

With the continuous increase in complexity and expense of industrial systems, there is less
tolerance for performance degradation, productivity decrease, and safety hazards, which …

With the privatization and intense competition that characterize the volatile energy sector,
the gas turbine industry currently faces new challenges of increasing operational flexibility …

The security issue in the state estimation problem is investigated for a networked control
system (NCS). The communication channels between the sensors and the remote estimator …

Traditional machine learning algorithms have made great achievements in data-driven fault
diagnosis. However, they assume that all the data must be in the same working condition …