Deep learning has gained significant success in fault diagnosis. However, the number of
gearbox health samples is inevitably much larger than that of fault samples in real-world …

Fault diagnosis methods based on deep learning have achieved remarkable success in the
field of mechanical fault diagnosis. However, most data obtained in the industrial field come …

Ensuring the safety of mechanical driving systems relies heavily on accurate gearbox fault
diagnosis. However, the presence of actual multiworking conditions and uneven sample …

Due to its powerful capability of extracting high dimensional nonlinear features, deep
learning has proven to be an effective tool for gearbox fault diagnosis. It can recognize …

Lots of recent deep learning based intelligent fault diagnosis methods of planetary gearbox
have achieved satisfactory accuracy with balanced training samples. Nevertheless, the fault …

Existing fault diagnosis methods based on Deep Learning require complicated and
cumbersome preprocessing procedures heavily relying on signal processing and manual …

Deep neural network (DNN), with the capacity for feature inference and nonlinear mapping,
has demonstrated its effectiveness in end-to-end fault diagnosis. However, the intermediate …

Gearboxes are one of the most widely used speed and power transfer elements in rotating
machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance …

Playing a vital role in keeping gearbox working reliably and safely, smart fault diagnosis
(FD) technology has attracted much attention in recent years. However, in practical industrial …

Convolutional neural network (CNN)-based intelligent fault diagnosis approaches have
showcased remarkable performance in the assessment of machine safety. The data …