Fault diagnosis is of great significance for ensuring the safety and reliable operation of
rolling bearing in industries. Stack autoencoder (SAE) networks have been widely applied in …

Deep learning is widely used in the field of rolling bearing fault diagnosis because of its
excellent advantages in data analysis. However, in practical industrial scenarios, 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 the rolling bearing is the most important part of rotating machinery, its fault diagnosis has
been a research hotspot. In order to diagnose the faults of rolling bearing under different …

To improve the feature mining capabilities of deep auto-encoder networks and select the
network hyperparameters adaptively, an enhanced deep auto-encoder network was …

The rolling bearing is a critical part of rotating machinery and its condition determines the
performance of industrial equipment; it is necessary to detect rolling bearing faults as early …

As an effective method, deep transfer learning is used to solve the problem of unsupervised
fault diagnosis of rolling bearings. In the process of obtaining domain invariant features, the …

Deep learning (DL) has been successfully used in fault diagnosis. Training deep neural
networks, such as convolutional neural networks (CNNs), require plenty of labeled samples …

In recent years, autoencoder has been widely used for the fault diagnosis of mechanical
equipment because of its excellent performance in feature extraction and dimension …

Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in
time and reduce losses, this paper presents an intelligent diagnosis method for rolling …