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 the era of Industry 4.0, highly complex production equipment is becoming increasingly
integrated and intelligent, posing new challenges for data-driven process monitoring and …

As one of the representative unsupervised data augmentation methods, generative
adversarial networks (GANs) have the potential to solve the problem of insufficient samples …

Low fault diagnosis accuracy in case of insufficient and imbalanced samples is a major
problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large …

Although the existing generative adversarial networks (GAN) have the potential for data
augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Building a high-accuracy utilization factor prediction model for tunnel boring machine with
limited available data is a research challenge. To solve the problem mentioned above, a …

Deep transfer learning-based fault diagnosis of machines is achieved based on the
assumption that the source and target domain data could be centralized to assess the …

The study of cross-domain semi-supervised fault diagnosis of bearings using meta-learning
technique has important practical significance. However, existing methods fail to consider …