Recently, with the urgent demand for data-driven approaches in practical industrial
scenarios, the deep learning diagnosis model in noise environments has attracted …

Intelligent algorithms based on convolutional neural network (CNN) has demonstrated
remarkable potential in diagnosing bearing faults. However, Accurate and robust fault …

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 …

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 …

In the event of mechanical equipment failure, the fault may not belong to any known
category, and existing deep learning methods often misclassify such faults into a known …

In recent years, deep learning technique has been used in mechanical intelligent fault
diagnosis and it has achieved much success. Among the deep learning models …

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 rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …

Convolutional neural networks (CNNs) have demonstrated promising effectiveness in
vibration-based fault diagnosis. However, the faulty characteristics are usually distributed on …

Deep learning has been an important topic in fault diagnosis of motor bearings, which can
avoid the need for extensive domain expertise and cumbersome artificial feature extraction …