With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Recent studies on machinery anomaly detection only based on normal data training models
have yielded good results in improving operation reliability. However, most of the studies …

Transformer has been widely applied in the research of rotating machinery fault diagnosis
due to its ability to explore the internal correlation of vibration signals. However, challenges …

Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …

Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of
engineering equipment. Many intelligent diagnosis methods have been successfully …

Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity
DT model of the physical assets can produce system performance data that is close to …