Current deep-learning methods are often based on significantly large quantities of labeled
fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing …

To solve the problems that existing bearing fault diagnosis methods cannot adaptively select
features and are difficult to deal with noise interference, an end-to-end fault diagnosis …

Recently, deep learning (DL) models based on convolutional neural networks have
achieved satisfactory results in rolling bearing fault diagnosis. However, the bearings …

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing
attention due to the availability of massive condition monitoring data. However, most existing …

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 …

In recent years, deep learning has achieved great success in bearing fault diagnosis due to
its robust feature learning capabilities. However, in the actual industry, the diagnostic …

Rolling bearings are the core components of rotating machinery. Their health directly affects
the performance, stability and life of rotating machinery. To prevent possible damage, it is …

Traditional methods used for intelligent condition monitoring and diagnosis significantly
depend on manual feature extraction and selection. To address this issue, a transfer …

Research aimed at diagnosing rolling bearing faults is of great significance to the health
management of equipment. In order to solve the problem that rolling bearings are faced with …

The fault diagnosis of rolling bearing will be negatively reduced because of variable working
conditions, large environmental noise interference and insufficient effective data sample. To …