Accurate and real-time fault diagnosis (FD) and working conditions identification (WCI) are
the key to ensuring the safe operation of mechanical systems. We observe that there is a …

Classical machine learning approaches have made remarkable contributions to the field of
data-driven techniques for bearing fault diagnosis. However, these algorithms mainly …

Fault diagnostic methods based on deep learning achieve impressive progress recently, but
most studies assume that signals from the source domain and target domain share a similar …

Data driven method has been investigated in fault diagnosis. This kind of methods usually
need of a large number of labeled data in order to obtain a good model. However, the …

The rolling bearing is a critical part of rotating machinery and its condition determines the
performance of industrial equipment; it is necessary to detect rolling bearing faults as early …

Automatic and accurate identification of rolling bearings fault categories, especially for the
fault severities and fault orientations, is still a major challenge in rotating machinery fault …

Condition monitoring and fault diagnosis of bearings play important roles in production
safety and limiting the cost of maintenance on a reasonable level. Nowadays, artificial …

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all
fault classes. There are massive target faults that cannot be collected in advance, which may …

In real engineering applications, low-quality and insufficient vibration signals of rolling
bearings, which are usually multiple faults with low signal-to-noise ratios (SNR), restricts the …

In recent years, deep learning techniques have been proved a promising tool for bearing
fault diagnosis. However, to extract deep features with better representative ability, how to …