Component reliability plays a pivotal role in industrial systems, which are evolving with
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …

With the advances in artificial intelligence, there is a growing expectation of more automatic
and intelligent prognostics and health management (PHM) systems for the real-time …

Bearing fault diagnosis is of great importance to decrease the damage risk of rotating
machines and further improve economic profits. Recently, machine learning, represented by …

Data-driven models for wind turbine (WT) gearbox health monitoring have garnered
significant attention. However, these models usually depend on extensive manually labeled …

Deep learning has become a popular approach for fault diagnosis due to its powerful feature
extraction and adaptability. However, its reliance on extensive annotations poses …

Data-driven methods have pushed mechanical fault diagnostics to an unprecedented height
recently. However, their satisfactory performance heavily relies on the availability of …

Deep learning-based fault diagnosis models achieve great success with sufficient balanced
data, but the imbalanced dataset in real industrial scenarios will seriously affect the …

In practical scenarios, it is difficult to acquire fault data from rotating machinery, resulting in
class-imbalanced problems in the fault diagnosis field. Training a fault diagnosis model …

Electromechanical systems is widely employed in the manufacturing industry, with fault
diagnosis being critical for ensuring the reliable operation of them. Vibration signals exhibit …

Recently, the emerging graph convolutional network (GCN) has been applied into fault
diagnosis with the aim of providing additional fault features through topological information …