Existing researches about unsupervised cross-domain bearing fault diagnosis mostly
consider global alignment of feature distributions in various domains, and focus on relatively …

Earthquakes, as natural phenomena, have consistently caused damage and loss of human
life throughout history. Earthquake prediction is an essential aspect of any society's plans …

Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …

Bearing fault diagnosis in real-world applications has challenges such as insufficient
labeled data, changing working conditions of the rotary machinery, and missing data due to …

In recent years, many researchers have attempted to achieve cross-domain diagnosis of
faults through domain adaptation (DA) methods. However, owing to the complex physical …

Abstract In Machine Learning (ML), a well-known problem is the Dataset Shift problem
where the data in the training and test sets can follow different probability distributions …

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-
quality labeled data from the source domain. However, in engineering scenarios, achieving …

Data-driven intelligent fault diagnosis requires a large amount of data. However, collecting
sufficient labeled data from the field is generally difficult because mechanical devices are …

Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically,
diagnosing faults in rolling bearings, which are essential rotating components, is of …

As one of the mainstream transfer learning methods, correlation alignment (CORAL) has
been widely applied in fault diagnosis field and has achieved certain achievements …