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

Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …

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

Unsupervised domain adaptation (UDA)-based methods have made great progress in
mechanical fault diagnosis under variable working conditions. In UDA, three types of …

Bearing fault is the most common causes of rotating machinery failure. Therefore, accurate
bearing fault identification technique is of tremendous significance. Vibration monitoring has …

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 …

Convolutional neural network (CNN), with the ability of feature learning and nonlinear
map**, has demonstrated its effectiveness in prognostics and health management (PHM) …

Existing data-driven fault diagnosis methods assume that the label sets of the training data
and test data are consistent, which is usually not applicable for real applications since the …

Deep learning (DL) is progressively popular as a viable alternative to traditional signal
processing (SP) based methods for fault diagnosis. However, the lack of explainability …

Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal
to deep learning (DL)-based methods over the decades. However, the “black box” nature of …