As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …

Industrial information integration engineering (IIIE) is a set of foundational concepts and
techniques that facilitate the industrial information integration process. In recent years, many …

The existing fault diagnosis methods of rotating machinery constructed with both shallow
learning and deep learning models are mostly based on vibration analysis under steady …

Deep learning-based bearing fault diagnosis has been systematically studied in recent
years. However, the success of most of these methods relies heavily on massive labeled …

Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …

A deep neural network (DNN) has become increasingly popular in industrial Internet of
Things scenarios. Due to high demands on computational capability, it is hard for DNN …

Deep learning architectures usually require large scale labeled datasets for achieving good
performance on general classification tasks including computer vision and natural language …

While machine-learning techniques have been widely used in smart industrial fault
diagnosis, there is a major assumption that the source domain data (where the diagnosis …

Series dc arc fault creates a fire hazard and negative impacts on the distribution bus if not
detected and isolated quickly. However, the detection of a series arc fault is challenging due …

Recently, a considerable literature has grown up around the few-sample fault diagnosis
task, in which few samples of fault data are available for model training. The lack of fault …