Rotating machinery intelligent diagnosis with large data has been researched
comprehensively, while there is still a gap between the existing diagnostic model and the …

Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …

Many deep-learning methods have been developed for fault diagnosis. However, due to the
difficulty of collecting and labeling machine fault data, the datasets in some practical …

The rapid development of artificial intelligence and deep learning has provided many
opportunities to further enhance the safety, stability, and accuracy of industrial cyber …

In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based
on traditional machine learning and deep learning, have been developed and successfully …

Both of traditional intelligent fault diagnosis (TIFD) based on artificial features and modern
intelligent fault diagnosis (MIFD) based on deep learning have made healthy progress in …

The techniques of machine learning, as well as deep learning (DL) methods, have seen a
wide application in the intelligent fault diagnosis field these years. However, contemporary …

Deep learning (DL) based diagnosis models have to be trained by large quantities of
monitoring data of machines. However, in real-case scenarios, machines operate under the …

The most existing deep neural networks (DNN)-based methods for fault diagnosis only focus
on prediction accuracy without considering the limitation of labeled sample size. In practical …

In real industrial scenarios, the working conditions of mechanical equipment are always
highly variable and the amount of data that can be collected is limited, which renders a …