Feature selection is a crucial step in fault diagnosis. When rolling bearings are susceptible
to compound faults, causal relationships are hidden within the signal features. Complex …

A rolling bearing is a complex system consisting of the inner race, outer race, rolling
element, etc. The interaction of components may lead to composite faults. Selecting the …

In order to address the problem that redundant condition attribute nodes and poor reasoning
ability of flow graph may lead to high computational burden and low diagnosis accuracy, a …

Rolling bearing faults inevitably occur during the long-term continuous operation of rotating
machinery. Therefore, fault diagnosis is greatly important for ensuring the normal and safe …

Recent advancements in data-driven deep-learning methods have significantly improved
rolling bearing fault diagnosis. However, these frameworks face limitations due to data …

In real industrial scenarios, the working conditions of bearings are variable, and it is
therefore difficult for data‐driven diagnosis methods based on conventional machine …

Fault diagnosis technology can effectively prevent the occurrence of faults and reduce safety
hazards, which is of great significance in nuclear power, aerospace, manufacturing, and …

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized
in the fault diagnosis field. However, there are two common problems in deep-learning …

Benefitting from the rapid development of artificial intelligence, the end-to-end fault
diagnosis mode based on deep learning has become one of the most potential research …

This study introduces an efficient methodology for addressing fault detection, classification,
and severity estimation in rolling element bearings. The methodology is structured into three …