The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …

Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …

Deep learning theory has been widely used for diagnosing bearing faults. However, this
method still has same drawbacks. For example, single time or frequency domain analysis …

Deep learning has evolved to a prevalent approach for machinery fault diagnosis in recent
years. However, the high demanding for training data amount refrains its implementation. In …

As wind energy is becoming a significant utility source, minimizing the operation and
maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to …

Rolling bearings are the key components of rotating machinery. Thus, the prediction of
remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper …

Monitoring the transmission status of multi-joint industrial robots is very important for the
accuracy of the robot motion. The fault diagnosis information is an indispensable basis for …

Echo state network (ESN) is a fast recurrent neural network with remarkable generalization
performance for intelligent diagnosis of machinery faults. When dealing with high …