Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Rolling bearing fault diagnosis with limited imbalance data is significant and challenging. It
is​ a nice attempt to generate data for balancing datasets. In this paper, a wavelet capsule …

Meta-learning has effectively addressed the limit of deep learning fault diagnosis models
that demands a large number of samples. However, existing meta-learning models lack the …

Deep transfer learning is used to solve the problem of unsupervised intelligent fault
diagnosis of rolling bearings. However, when the data distribution between two domains is …

As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize
the adversarial learning of the feature extractor and domain discriminator to extract the …

As a precision mechanical component to reduce friction between components, the rolling
bearing is widely used in many fields because of its slight friction loss, strong bearing …

Bearing fault diagnosis is vital for ensuring reliability and safety of high-speed trains and
wind turbines. Therefore, a minimum unscented Kalman filter-aided deep belief network is …

Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …

Forecasting the stock composite index is a challenge on account of the abundant noise-
induced high degree of non-linearity and non-stationarity. Numerous predictive models …