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 …

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

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The advent of the Industry 4.0 initiative has made it so that manufacturing environments are
becoming more and more dynamic, connected but also inherently more complex, with …

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 …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …

With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …

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

In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …