Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …

This paper proposes a selective kernel convolution deep residual network based on the
channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. First …

Shield tunneling machine is an important large-scale engineering machine used for tunnel
excavation. During the tunneling process, precise cutterhead torque prediction is of vital …

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …

Currently, accuracy of existing diesel engine fault diagnosis methods under strong noise
and generalisation performance between different noise levels are still limited. A novel multi …

Cutterhead torque is an important operational parameter that reflects the obstruction degree
of geological environment to shield tunneling machine. Accurate multi-step prediction for …

For the sake of solving the problem of the difficulty of extracting fault features under the
background of noise and accurately identify the state of the bearing, a compound fault …