The vibration monitoring data of high-speed train (HST) bogie bearings exhibit high
redundancy and limited effective fault information, impacting diagnostic accuracy and speed …

Rolling bearing fault detection has developed rapidly in the field of fault diagnosis
technology, and it occupies a very important position in this field. Deep learning-based …

Deep learning has exhibited remarkable performance and achieved significant
breakthroughs in railway transportation equipment fault diagnosis. However, in engineering …

This article aims to provide insights into a challenging rubbing signal-recovering problem
that arises when analyzing acoustic signals caused by rubbing in multirotor systems. Since …

Efficient and timely identification of bearing faults is imperative to ensure operational
normalcy, reduced down-times and health hazards in motor fault tolerant control systems …

The intelligent fault diagnosis (IFD) method based on incremental learning (IL) can expand
new fault categories without retraining the model, making it a research hotspot in the field of …

In the era of intelligent manufacturing, faced with the explosive growth of equipment and
sensor data, deep learning (DL)-based fault diagnosis methods with significant feature …

Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to
cause a series of serious consequences. Traditional deep learning-based bearing fault …

To address the challenges associated with the YOLOv4-Tiny algorithm's complex structure,
high computational resource requirements, and extensive parameter count—which …