Conventional intelligent bearings fault diagnosis methods generally extract fault features
with a single channel, which seriously limit the features richness and the diagnostic …

In recent years, deep learning methods, especially convolutional neural network (CNN),
have received extensive attentions and applications in fault diagnosis. However, recent …

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 …

The vibration signal of a bearing is closely related to its fault. The quality of the features
extracted from the signal has a great impact on the accuracy of fault diagnosis. In this paper …

Transfer learning has attracted great attention in intelligent fault diagnosis of bearings under
different working conditions. However, existing studies have the following limitation.(1) The …

Aiming at automatic feature extraction and fault recognition of rolling bearings, a new data-
driven intelligent fault diagnosis approach using multi-head attention and convolutional …

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 …

Due to rolling bearings usually operate under fluctuating working conditions in practical
engineering, the raw vibration signals generated by bearing faults have nonlinear and non …

Deep learning-based fault diagnosis methods usually require samples to meet the
conditions of independent and identical distribution. In actual industrial occasions, the data …

Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial
practice, the running state of bearings is monitored by collecting data from multiple sensors …