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 …

Data-driven fault diagnosis has been a hot topic in recent years with the development of
machine learning techniques. However, the prerequisite that the training data and the test …

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

This work presents the development of novel convolutional neural network (NCNN) for
effective identification of bearing defects from small samples. For effective feature learning …

Deep learning (DL) based methods have swept the field of mechanical fault diagnosis,
because of the powerful ability of feature representation. However, many of existing DL …

This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

In the recent years, data-driven machinery fault diagnostic methods have been successfully
developed, and the tasks where the training and testing data are from the same distribution …

Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …

In the past years, deep learning-based machinery fault diagnosis methods have been
successfully developed, and the basic diagnostic problems have been well addressed …

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …