Traditional data-driven intelligent fault diagnosis methods have been successfully
developed under the closed set assumption (CSA). CSA-based fault diagnosis assumes that …

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of
unexpected failures of rotating machinery and improve the efficiency of its scheduled …

Fault diagnosis is composed of two tasks, ie, fault location detection and fault severity
identification, which are both significant to equipment maintenance. The former can indicate …

To solve the diagnosis of misjudgment in the diagnosis of the unknown bearing fault, an
intelligent open set fault diagnosis method is proposed for rolling bearings based on the …

Cross-domain fault diagnosis of bearings has attracted significant attention. However,
traditional cross-domain diagnostic methods have the following shortcomings:(1) when the …

The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …

The success of convolutional neural networks (CNNs) in intelligent fault diagnosis is largely
dependent on massive amounts of labelled data. In a real-world case, however, massive …

Existing bearing fault diagnosis methods based on deep learning typically rely on a large
amount of labeled data for training. However, acquisition of a large amount of labeled target …

The intelligent diagnosis of rotating machinery with big data has been widely studied.
However, due to the variability of working conditions and difficulty in marking fault samples, it …

The vibration signal of a bearing is closely related to its fault. The quality of the features
extracted from the signal has a great impact on the accuracy of fault diagnosis. In this paper …