Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in
industrial processes. In particular, bearings are mechanical components used in most …

Fault diagnosis is vital in manufacturing system. However, the first step of the traditional fault
diagnosis method is to process the signal, extract the features and then put the features into …

Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it
allows a deep network to accomplish the tasks of feature learning and fault classification …

In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken
for the manual design of fault features, which makes these methods less automatic. Among …

Rotating machinery is widely applied in various types of industrial applications. As a
promising field for reliability of modern industrial systems, early fault diagnosis (EFD) …

Many deep-learning methods have been developed for fault diagnosis. However, due to the
difficulty of collecting and labeling machine fault data, the datasets in some practical …

Various deep transfer learning solutions have been developed for machine fault diagnosis,
which are purely data driven. Plenty of prior knowledge on the fault of different machinery …

The diagnosis of a rolling bearing for monitoring its status is critical in maintaining industrial
equipment while using rolling bearings. The traditional method of diagnosing faults of the …

Rotating machinery is of vital importance in the field of engineering, including aviation and
navigation. Its failure will lead to severe loss to personnel safety and the stability of the …

Many deep transfer learning methods for fault diagnosis have been proposed in this decade.
Some of the existing methods focus on addressing the problem of fault data scarcity and …